exploring passengers’ interest toward onboard connectivity

IN DEGREE PROJECT ELECTRICAL ENGINEERING,SECOND CYCLE, 30 CREDITS

, STOCKHOLM SWEDEN 2018

Exploring passengers’ interest toward onboard connectivity in the Urban Area

ANATOLII SHOKHIN

KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE

Abstract

In the recent decades, individual industries and whole sectors have been transformed due to the digital revolution. A combination of disruptive technologies and innovative business models, leading to new market opportunities and bringing unique values to customers. Companies that are primarily focused on the digitalization are able to scale their businesses faster and gain leading market positions. This thesis explored the impact of digitalization on the rail industry based on a business case by Bombardier Transportation (BT). Deregulation of the rail market and a focus on passengers’ experience was identified by BT as a business opportunity. Bombardier’s strategy was to extend products and services beyond those of a train operator, toward passengers. Bombardier initiated a project with code name “ON: BOARD” that was aimed at increasing journey experience by providing digital entertainment. However, the problem was the lack of knowledge regarding the passengers’ interest towards services provided under the scope of the project ON: BOARD, in particular in the Urban Area. The goal of this thesis was to observe passengers’ interest towards services of the project ON: BOARD traveling with Commuter trains in the area of Stockholm city. In order to meet the research goal and answer research questions, a qualitative method was selected. To be more precise, a survey with a structured questionnaire was conducted within a heterogeneous population. Non-probability quota-based sampling technique was selected. The collected data was analyzed based on descriptive statistical method, in particular, using one-way and cross-tabulation methods. Based on the research result the conclusion was following: passengers in the Urban area, in particular, commuter trains, were highly interested in the connectivity functionalities provided under the scope of project ON: BOARD. Thus, Bombardier was recommended to enter Urban Transport as the part of the market expansion strategy. Keywords Digitalization; Rail Industry; Survey; Online Behaviour; Business Development; Strategy

Abstract

Under de senaste decennierna har individuella industrier och hela sektorer transformerats på grund utav den digitala revolutionen. En kombination av omvälvande teknologier och innovativa affärsmodeller har lett till nya marknadsmöjligheter och unika värden för kunder. Företag som är primärt fokuserade på digitalisering har möjligheten att snabbt skala deras affärsverksamhet och erhålla ledande marknadspositioner. Detta examensarbete har undersökt digitaliseringens effekt på järnvägsindustrin baserat på ett affärsfall av företaget Bombardier Transportation (BT). Avreglering av järnvägsmarknaden och fokus på passagerarupplevelse identifierades av BT som affärsmöjligheter. Bombardiers strategi var att utöka sina produkter och tjänster utöver det vanliga utbudet hos en tågoperatör, mot passagerare. Bombardier initierade ett projekt med kodnamn ”ON: BOARD” som syftade till att förbättra passagerarupplevelsen med hjälp av digital underhållning. En problematik uppstod dock på grund utav en bristande vetskap angående passagerarnas intresse om de tillhandahållna ON: BOARD-tjänsterna, speciellt i stadsområden. Målet med detta examensarbetet var att observera passagerarnas intresse av tjänster tillhandahållna via ON: BOARD, begränsat till pendeltåg runtom Stockholmsområdet. För att uppnå detta och att besvara de upprättade forskningsfrågorna valdes ett kvalitativt tillvägagångssätt. Det vill säga, en strukturerad enkätundersökning genomfördes inom en heterogen population. En icke-sannolikhetsbaserad och andelsbaserad samplingsteknik valdes som metod. Den insamlade datan analyserades med hjälp av statistiska metoder, framför allt användes ”one-way” och korstabulerings metoder. Baserat på resultaten och den efterföljande analysen tillhandahölls ledningen av BT rekommendationer. Utöver detta kartlades användarnas onlinebeteende med hjälp av resultaten och förslag till framtida forskning presenterades. Nyckelord Digitalisering; Järnvägsindustri; Enkätundersökning; Onlinebeteende; Affärsutveckling; Strategi

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Table of Contents

1 Introduction................................................................................................................11.1 Background.....................................................................................................................11.2 Problem............................................................................................................................21.3 Goal....................................................................................................................................21.4 Researchquestions......................................................................................................21.5 QuestionsofInterest...................................................................................................31.6 Purpose............................................................................................................................31.7 Methodology/Methods..............................................................................................3

1.7.1 Research Design.......................................................................................................................41.7.2 ResearchPurpose....................................................................................................................41.7.3 ResearchStrategy....................................................................................................................41.7.4 ResearchAnalysis....................................................................................................................4

1.8 Benefits, Ethics and Sustainability......................................................................41.9 Delimitations..................................................................................................................51.10 Outline..............................................................................................................................5

2 On-BoardConnectivity............................................................................................72.1 Introduction...................................................................................................................7

2.1.1 Requirements...........................................................................................................................82.1.2 Solution Overview..................................................................................................................9

2.2 RailwayCommunication.........................................................................................102.3 GSM–R...........................................................................................................................11

2.3.1 GSM-R Features...................................................................................................................112.3.2 GSM-R Limitations.......................................................................................................12

2.4 LTE-R..............................................................................................................................132.4.1 Architecture............................................................................................................................142.4.2 Spectrum Availability...................................................................................................152.4.3 LTE –R Functionalities...............................................................................................162.4.4 LTE-R Limitations.........................................................................................................17

2.5 Conclusion....................................................................................................................172.6 DeploymentStrategy................................................................................................18

2.6.1 Parallel Network...................................................................................................................182.6.2 National Wide LTE Network....................................................................................192.6.3 Mobile Virtual Network Operator..........................................................................192.6.4 Conclusion.........................................................................................................................20

3 Background..............................................................................................................213.1 Redefinethecompetition........................................................................................213.2 Previousstudies.........................................................................................................223.3 Survey............................................................................................................................233.4 BusinessSurveys........................................................................................................24

3.4.1 The structure of Business Surveys..............................................................................24

4 Methodologies.........................................................................................................274.1 ResearchMethods.....................................................................................................27

4.1.1 Method Selection.................................................................................................................274.1.2 Quantitative Research.......................................................................................................274.1.3 Qualitative...............................................................................................................................284.1.4 Triangulation.........................................................................................................................29

4.2 ResearchApproach...................................................................................................29

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4.2.1 Approach Selection.............................................................................................................294.2.2 Theoretical Background..............................................................................................29

4.3 ResearchPurpose......................................................................................................304.4 Sampling.......................................................................................................................30

Selection of the Sampling.............................................................................................................31Probability Sampling......................................................................................................................31Non-probability sampling...........................................................................................................33

4.5 Questionnairevariables..........................................................................................354.6 Typeofquestions.......................................................................................................364.7 Lengthofthequestionnaire...................................................................................374.8 DataAnalysis...............................................................................................................38

One-Way Tables.................................................................................................................................39Cross-Tabulation...............................................................................................................................40

4.9 DataArchiving............................................................................................................404.10 SurveyEthics...............................................................................................................404.11 SurveyErrors..............................................................................................................41

Specification Error...........................................................................................................................42Processing Error................................................................................................................................43Sample Error.......................................................................................................................................43Coverage Error...................................................................................................................................44Nonresponse error...........................................................................................................................45Measurement Error.........................................................................................................................46Conclusion.............................................................................................................................................47

5 SurveyPreparation...............................................................................................495.1 Background..................................................................................................................495.2 Methodology................................................................................................................49

5.2.1 Survey Planning....................................................................................................................505.3 Questionnaire.............................................................................................................515.4 Pilot................................................................................................................................54

5.4.1 Background.............................................................................................................................555.4.2 Observations.....................................................................................................................555.4.3 Result....................................................................................................................................565.4.4 Conclusion.........................................................................................................................61

6 Results.......................................................................................................................636.1 Patternsinthepassengers’onlinebehavior....................................................63

6.1.1 Age Group................................................................................................................................636.1.2 Travel Information..............................................................................................................646.1.3 Mobile Usage..........................................................................................................................666.1.4 Interest towards Wi-Fi......................................................................................................706.1.5 Recommendations..............................................................................................................71

6.2 Customers’Profile.....................................................................................................716.2.1 Results.......................................................................................................................................72

6.3 SurveyErrors..............................................................................................................73

7 Conclusion................................................................................................................757.1 FutureWork................................................................................................................76

References........................................................................................................................77AppendixA...........................................................................................................................1

AppendixB...........................................................................................................................5

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AppendixC...........................................................................................................................9

AppendixD.......................................................................................................................13AppendixE........................................................................................................................17

AppendixF........................................................................................................................21

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1 Introduction

In the last five years, Train Operating Companies (TOC) have been experiencing drastic changes in their business operations due to increasing popularity of low-cost express coach operators and deregulation of the rail freight market. The last factor lead to decrease of entrance barriers for the new players. The Swedish National Road and Transport Research Institute (VTI) [1] conducted research about the effect of new entrants on the railway industry in Sweden. The case when greenfield operator MTR Express entered the Stockholm-Gothenburg line was stated there as an example. In response to increased competition, an incumbent operator SJ decreased the ticket price by 12 percent. Authors highlighted the fact that ticket prices reached equilibrium, operators shifted their focus into increasing passengers’ comfort as well as the overall journey experience.

1.1 Background Bombardier Transportation (BT) is one of the world’s largest rail vehicle manufacturer. The Rail Control Solutions (RCS) division offers a portfolio of both the rail control and signaling safety systems. Currently, Bombardier is competing with companies such as Alstom, Siemens, Thales and Ansaldo-Hitachi with regard to rolling stock, signaling, train control, on-board telemetric, and fleet management solutions. Deregulation of the rail market and a focus on passengers’ experience identified by BT as a business opportunity. Bombardier’s strategy was to extend products and services beyond operator, toward passengers. Bombardier initiated a project with code name “ON: BOARD” that was aimed at increasing journey experience by providing digital entertainment services that could be accessed via passengers’ smart devices. In recent decades, individual industries and whole sectors have been transformed due to the digital revolution. Paper Digital Vortex [2] gives a definition of digitalization; it is a combination of disruptive technologies and innovative business models, leading to a new market opportunities and bringing unique values to customers. Companies that are primarily focused on the digitalization are able to scale their businesses faster and gain leading market positions. According to Gonzalez and Francisco [3], digitalization and digital transformation can be considered as an outcome of two main factors. The first one is about changes in consumer’s behavior towards online and mobile services. The second factor is mainly an outcome of the first one; companies are bringing their own digital services in order to meet customer’s demand. Hence, the pace of digital transformation is increasing.

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As of the July 2017, Bombardier’s project ON: BOARD was currently at the initial stage that included a tight internal collaboration among different departments in order to establish the shared vision of the project, and define both milestones and key partners. At the core of the project was to develop a flexible platform that would allow tailoring a functionality based on the industry and customer’s needs. It should be pointed out that project ON: BOARD consisted of two major layers. The first one was to establish on-board connectivity for passengers by deploying free Wi-Fi network both inside trains and on stations. The second layer was to provide services and applications towards passengers on top of the connectivity layer.

1.2 Problem One of the most important aspects was to clarify in which type of the trains there is a demand from passengers’ side and which market is most attractive in terms of the potential revenue model. Initially, the project ON: BOARD was aimed towards Intercity and High-Speed Trains. The management team understood the importance of other areas as well, in particular, market opportunities of the Urban Area transport, such as commuter trains, metro, and light-rails. However, the problem was the lack of knowledge regards to passengers’ interest towards services provided under the scope of the project ON: BOARD. Hence, there was a high uncertainty level for the project’s success in terms of the number of active users, leading to the major obstacle in the development of the services for the Urban Area market.

1.3 Goal The goal of this thesis was to observe passengers’ interest towards the connectivity layer of the project ON: BOARD under the scope of Commuter trains in the area of Stockholm city.

1.4 Research questions In order to meet the goal of the thesis, the following research questions were answered meticulously:

RQ1. What are the patterns in the passenger’s online behavior in public transports? RQ2. What does the potential customer’s profile look like?

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1.5 Questions of Interest An additional research was performed in order to provide recommendations towards both the selection of the communication standard and network deployment strategy that would allow meeting the requirements of the project. Thus, the following questions were answered as well, however, they are not part of the methodological research.

Q1. What is the communication standard that would allow meeting the requirements from the application layer of the project?

Q2. What is the network deployment strategy recommended to implement in order to meet the strategical part of the project?

1.6 Purpose The purpose of this thesis was to answer research questions by conducting an abductive research. In order to answer research questions 1 and 2, and to define passengers’ interest towards the project, an inductive research was initiated. The objective was to form a set of recommendations based on results obtained from the survey. Despite the fact that a study conducted by Transdev [4] was investigating a similar topic, it was decided not to form a hypothesis based on results of that study. The motivation was following, to begin with, the Transdev’s survey has been conducted at the end of 2014. Thus, there was an assumption that passengers’ online behavior and overall interest towards the on board connectivity might be changed over some period of time. In addition, the Transdev’s study was focused on the rural areas in France, meanwhile, the goal of the given thesis was to observe passengers’ online behavior in the Urban Areas. Last but not least, in the Transdev’s study, the average daily travel time of survey participants was not reflected. From author's perspective, the travel time is having a direct impact on passengers’ interest towards onboard connectivity and overall preferences in online services. In order to answer research questions 3 and 4 and to provide recommendations towards the communication standard and network deployment strategy, a deductive approach was selected. An in-depth literature study was conducted, based on which a conclusion has been formed.

1.7 Methodology / Methods This section provides an overview of the research methodology that was selected based on the research objective. Aspects such as research design, approach, purpose, strategy and analysis have been discussed below.

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1.7.1 Research Design

In order to meet the research objective and determine patterns in the collected data, quantitative research design was selected. Numerical approach allows to ascertain a statistical relationship and eliminate random variability. The operational definition for this quantitative research was the following: define pattern in the passengers’ online behavior during the commuting time. Results were generalized as the passengers’ online behavior in the area of Stockholm city.

1.7.2 Research Purpose

The purpose of this research was to explore the passenger’s online behavior based on the data collected via survey. The objective towards the data analysis was to observe relationship among variables.

1.7.3 Research Strategy

Survey was selected as the research strategy. A questionnaire session with passengers was conducted in order to obtain quantitative data. The survey was performed at various commuter lines and dates. The characteristics of the survey is cross-sectional. The type of session was self-completed, with structured and standardized questions. The aim of the questionnaire was to detect patterns in customer’s behavior and interest towards connectivity features that are offered under the scope of the project ON: BOARD.

1.7.4 Research Analysis

The data collected from passengers was analyzed based on descriptive statistical method. In particular, both one-way and cross-tabulation have been used. The collected data was summarized based on the common patterns.

1.8 Benefits, Ethics and Sustainability Due to the fact that the given research was conducted among humans, ethical considerations were an essential aspect of the survey. In particular, the survey under this research was conducted based on guidance provided in a Federal Policy for the Protection of Human Subjects (45 CFR Part 46). To begin with, during the survey participants were informed that the participation in the survey was on volunteer bases. Thus, they were able to withdraw their involvement at any time without any consequences. Another essential aspect highlighted in 45 CFR 46 is towards transparency of the research objectives. Participants have a right to receive an information about research objectives and interest groups. This guidance was reflected in a disclaimer part of the questionnaire, where participants were informed about the purpose of the study. Moreover, in case of participants wanted to obtain the

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details about the purpose of the study, research problem or involved parties author verbally provided the details. The last guidance of 45 CFR Part 46 is participants anonymity and confidentiality, in the other words, the answers given by participant cannot be linked to that person. In 45 CFR Part 46 stated that attribute variables such as specifying gender, age group, race and occupation there is a certain level of risk for the anonymity. In order to minimize the risk of data confidentiality compromise, attribute variables were narrowed only to the age group participants. Moreover, participants were informed in a disclaimer about data anonymity and confidentiality.

1.9 Delimitations First and foremost, it is important to note that this research was focused only on the connectivity part of the project. Moreover, the aim of the survey was to observe patterns in general rather than focus on details of each answer individually. Even though the survey was conducted in Sweden, the English language was chosen for the questionnaire due to language limitations of the author. The focus of this research was on Commuter trains only. Thus, passenger’s online behavior may vary in other types of transport. Based on the results from the pilot run of the survey, it was decided to select certain hours for the survey, when the lowest number of passengers were travelling. The selection of this limitation was motivated in Section 5.3.4. It is important to note as for the July 2017 the project was at the initial stage. Due to Non-Disclosure Agreement (NDA), internal information such as business model, strategy plan, features, potential partners and market entry strategy were not revealed in the scope of this research.

1.10 Outline Chapter 1. The aim of this chapter is to introduce readers with the background of the topic that is followed by the problem statement. Based on that a survey objective was formed. In order to meet the objective, research questions should to be answered first. In addition, this chapter provides an overview of the research methodologies that were selected for the given thesis. Chapter 2. The objective of this chapter was to extend the background of the project by answering question of interest 1:” What is the communication standard that would allow meeting the requirements from the application side of the project?” and question of interest 2: “What is network deployment strategy

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recommended to implement in order to meet the strategical part of the project?”. Firstly, a high-level overview of the project’s architecture was presented, including a portfolio of products and services. Based on that, an assessment of various communication standards was done that is followed by the discussion of the network deployment strategy. Chapter 3. Each survey is unique according to the context, objective, type of the collected data, design of the questionnaire and timeline. However, the overall process of conducting surveys is unified. Thus, a literature study was performed to establish and outline concepts of conducting surveys within a business context. Chapter 4. This chapter is focused on reviewing research methodologies in details. The emphasis was done on the research methods, approach and purpose. Meanwhile, details of the research strategy were covered in the Chapter 2. Moreover, under the scope of method selection, aspects such as design of the questionnaire, definitions of the sampling techniques, and both the common issues and ethical considerations were covered as well. Chapter 5. In this chapter, all the essential preparation steps before the actual survey are described. First and foremost, it was done the selection of the population and sampling technique that applicable for the given research. Secondly, the design of the questionnaire were presented. Thirdly, a pilot survey were discussed. The result of it has effected on the structure of some questions, selection of time and data visualization. Chapter 6. The objective of this chapter was to meet the research objective and answer both research question 1 and 2. The analysis of the data collected from the survey was performed. Based on that a conclusion were drawn. Chapter 7. Conclusion and future work were discussed.

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2 On-Board Connectivity

The objective of this chapter was to provide the contexts of the project by answering question of interest 1:” What is the communication standard that would allow meeting the requirements from the application side of the project?” and question of interest 2: “What is network deployment strategy recommended to implement in order to meet the strategical part of the project?”. A high-level overview of the project’s architecture is presented below, including a portfolio of products and services. Network requirements and the solution overview are presented in this Chapter as well. Based on that, an assessment of various communication standards was done that is followed by the discussion of the network deployment strategy.

2.1 Introduction Bombardier Transportation’s original product portfolio of the rolling stock and signaling solutions aimed towards Train Operating Companies (TOCs), fleet and infrastructure owners. The objective of the project ON: BOARD was two-sided: from one perspective, it aimed at extending the products and services portfolio towards the existing customer base. From another side, it was recognized as an opportunity to enter Business to Consumer (B2C) market by providing services aimed towards passengers. The management team of Bombardier Transportation set two major milestones for the project ON: BOARD. The first one was to provide onboard connectivity services, in particular, the Wi-Fi coverage both inside the train carts and on rail stations. The second milestone was interconnected with the first one: to provide services and applications on top of the connectivity part of the project. The general overview of the project is illustrated in Figure 1.

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Figure 1. Project ON: BOARD High Level overview

Even though the focus of the given thesis was to explore only the connectivity part of the project, it is still critical to outline the portfolio of services under the scope the project. For the existing customer base such as TOCs, fleet and infrastructure owners the product portfolio was the following: predictive maintenance, traffic management and closed-circuit television (CCTV) for the security purposes. Services that were aimed primarily towards passengers were the following: passenger information system (PIS), travel companion, infotainment, smart ticketing system and catering services. The focus of the project was done towards the flexibility and adaptability of services.

2.1.1 Requirements

In order to meet customers' expectations and being able to integrate above-mentioned services, Bombardier’s management team established following requirements and expectations towards the connectivity part of the project. It should be pointed out that the management team did not provide any empirical specifications regarding the coverage area and bandwidth capacity. Below are provided only high-level guidance and requirements. The following assessment of communication standards was based on direct comparison between standards rather than on comparison to the specific guidelines. First and foremost, the Wi-Fi service must be the highest quality (in terms of bandwidth) and be always available for passengers. It must be ensured that both the coverage area as well as the network capacity are in the high-density areas. Due to the fact that Wi-Fi network would be present both in inside trains and on stations, it is critical to ensure a combination of the smooth transition among base stations as well as single sign in option. Last but not least, the connectivity part of the project should form a foundation for the upcoming

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services and ensure the scalability for the services that will be provided later on demand.

2.1.2 Solution Overview

Figure 2 illustrates a high-level solution overview that is based on the list of requirements from the Section 2.1.1. To begin with, one of the requirements was to ensure that passengers are required to log in once entering the coverage area. Moreover, it is critical to ensure the Wi-Fi handover within stations and trains. Thus, from each train and station, the multiple mobile backhaul connection to the Central Network Services (CNS) should be implemented in order to authenticate users, grant access to the Internet and terminate the session. The CNS should be established in a data center in order to accommodate an aggregated users’ traffic. Operation Support Systems (OSS) would be implemented in order to integrate management system towards end-users, traffic policies, configuration and fault management. In addition, OSS is going to be used for the performance monitoring. CNS also supports Big Data and Location Based services, as well as an integration of infotainment services.

Figure 2. Solution overview

Train Station

Multimodalmobilebackhaulconnections

BackhaulConnection

Wi-FiHandover:Trains/Stations

Gateway OSS/Management

InternetServiceProvider

CentralNetwork

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2.2 Railway Communication In order to meet the objective of the given chapter and answer questions of interest 1 and 2, the focus was on the railway communication standards between train and CNS. It should be pointed out that the communication between station and CNS was not covered under this research. The comparison of railway communication was done between the current industry’s standard GSM-R and emerging LTE-R that is considered as the next railway communication standard both by academicals sources such as High-Speed Railway Communications paper [6] and Sniady and Soler [7], as well industry representatives such as Nokia [8]. It includes observing advantages and disadvantages of each standard both from the use cases and technological perspective. Based on the analysis, a conclusion that satisfies the requirements of the project was outlined. Table 3 illustrates the requirements that will affect the selection of the communication standard. In order to meet the requirements of the project, it should be able to support both the mission critical and non-critical communication.

Figure 3. Communications criteria

According to Rail Safety and Standards Board [9], the objective of the mission critical services is to provide communication between train-to-train, train-to-dispatcher and among trackside staff members. On the other hand, under the scope of non-critical communication are passenger experience, business process and operations support. Figure 4 reveals details on services of each component from non-critical communication group based on the services portfolio of the project ON: BOARD.

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Figure 4. Non-critical communications

2.3 GSM –R According to the official GSM –R website [10] GSM-R is considered as a standard in the rail communication industry. GSM –R is a private network that is available for the internal usage only, for instance in the signaling and control solutions. In addition, GSM –R is used for the communication among the staff members. In order to meet the railway standards, GSM –R combines the regular GSM technology and an outcome of two projects: European Integrated Radio Enhanced Network (EIRENE) and Mobile Oriented Radio Network (MORANE). As a result, according to the Directive 96/48/EC99 [11], GSM-R has been officially integrated into the European Rail Traffic Management System (ERTMS) standard. In order to meet requirements of the rail industry GMS-R has been implemented on the dedicated base stations that are located close to the track. Taking into account that GSM-R is considered as a communication standard in the railway industry, this standard was by default selected for the mission critical services, in order to satisfy the ERTMS requirements. In the Section 2.3.1 highlighted the list of GSM-R functionalities that were implemented based on ERTMS regulations. Based on this list of features, an assessment of LTE-R was done a communication standard for mission critical services.

2.3.1 GSM-R Features

GSM –R is a communication standard for the European Train Control System (ETCS), that is used for the signaling and control solutions. There are three gradations of the ETCS. The first one is called ETCS- 1 where the GSM-R is used for the voice communication. On the other hand, in case of the ETCS -2 and 3 the GSM –R has been used for the data transition. It should be pointed out that

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standards ETCS-2 and 3 have been developed for high-speed trains that reaching speed up to 350km/h. Based on Sniady and Soler [7] functionalities that are offered under the scope of GSM-R for the rail industry are listed below. First and foremost, in the GSM –R has been integrated Voice Group Call Service (VGCS). The idea behind VGCS is to establish a group call within a team. The conversational principle is similar to Push to Talk (PTT) when all users are able to participate, but only one is able to talk at a time. The use cases of VGCS is to support trackside workers, on station staff members and even communication between trains. Another feature is called Voice Broadcast Service (VBS). It is similar to VGCS, in terms of multiple users’ participation. However, the major difference that only one participant is able to transmit the signal, while the rest are only receiving it. One of the implementations of VBS is to broadcast pre-recorded messages. Enhanced multilevel precedence and preemption system (eMLPP) allows prioritizing specific traffic. In most of the cases used in the emergency situations. The scale is the following: 4 lowest priority where 0 is the highest. Railway Emergency Call (REC) is an emergency group call services, which aim is to inform staff members about the emergency situation. According to eMLPP, REC has the highest priority, identified as 0. The direct mode allows staff members to communicate directly with each other, similar to the regular GSM. However, the communication process is based on the PTT. The only exception is that it does not require additional equipment. Shunting mode is used when the workers are performing shunting operations. Link Assurance Signal (LAS) has been constantly sent over some period of time in order to ensure that the link is working. Function addressing is a process where a user got identified by a number identifying the function rather than by the terminal equipment that has been used. Location-depending addressing is a process when a user got identified based on the location. For example, a moving train could be identified by the base station that it has passed.

2.3.2 GSM-R Limitations

Despite the fact that GSM –R has been a communication standard in the railway industry for almost a decade, there are still limitations that should be discussed. The list of limitations has been primarily based on the Sniady and Soler [7]. In this section, limitations of the GSM-R were examined, including all the three categories such as Interference, Capacity and Capability.

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One of the major problems of the GSM –R is the interference brought by the regular GSM network. As it was mentioned before, GSM –R is a private network that is used only for mission critical communication. On the other hand, Network Mobile Operators (MNO) a willingness to provide communication services for their customer base during the travel time. Hence, there is a conflict of interest regarding the coverage planning, leading to the interference. This problem could be avoided if MNOs adjust the frequency band in the areas close to the railway track. However, this is already a topic of spectrum allocation that is under national and rail authorities’ responsibility. Regarding the capacity, GSM-R has a support of the 19 channel, where the capacity per channel is 200-kHz. Considering that the GSM-R is primarily used for the voice transition, where the resource has been using only some period of time, such capacity has been considered to be enough. Nevertheless, in the case of the data transmission, it is required to establish an end-to-end connection, that in the case of GSM-R would occupy the whole time slot. Last but not least, GSM-R has a limited network capability, with the transmission rate per connection is 9.6 kb/s. Such low transmission rate is enough both for communication between staff members and signaling solutions. However, due to low data transition, GSM-R is not able to support services such as predictive maintenance and condition monitoring.

2.4 LTE-R LTE-R is an emerging communications technology in the railway industry. From the integration perspective, LTE-R is at the initial phase. According to the paper published by Nokia [8] on moment European Union – Agency for Railway (UIC), European Telecommunications Standards Institute (ETSI) and 3rd Generation Partnership Project (3GPP) are working on defining a communication standard that will replace GSM-R from 2022. The completion migration time is expected by 2030. Thus, during the timespan when this research has been conducted, LTE-R has not been standardized. Moreover, European mainline TOCs are restricted to implement telecommunication technologies that have not been standardized for the mission critical communication. Bombardier Transportation has been actively involved in participation and certifying LTE among four different vendors: Ericsson, Huawei, Nokia and ZTE [12]. However, an emphasis has been done towards a non-critical type of the communication. In contrast, in 2016 Nokia and Korea Rail Network Authority (KRNA) [13] announced the initiation of the world’s first LTE-R commercial deployment on the line between Wonju and Gangneung as a preparation phase for the 2018 Winter Olympic Games. LTE-R would be used both for operational and maintenance services. The LTE-R network in the South Korea will be operating

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on the 700 MHz, the same channel that is used for national public safety network.

2.4.1 Architecture

LTE-R is an Internet Protocol (IP) based communication standard that is using packet switching. The regular LTE architecture consists of two major components: Evolved Universal Terrestrial Radio Access Network (E-UTRAN) and Evolved Packet Core (EPC). E-UTRAN consists of eNodeBs. EPC consists of the Mobility Management Entity (MME), the Serving Gateway (S-GW) and the Packet Data Network Gateway. EPC supports seamless handover among base stations, while E-UTRAN supports high data capacity. One of the significant advantage of LTE -R is the ability to combine multiple separate networks into LTE using IP/Multiprotocol Label Switching (IP/MPLS) as it is illustrated in Figure 5. A significant advantage of it is a considerable decrease in operating expenses, in particular, network management and maintenance.

Figure 5, Combination of multiple networks into LTE [12]

Tecrail project [14] was exploring the usage of applications that would allow meeting regulatory standards. A significant advantage of LTE over the GSM-R is the layer separation. As a result, changes in one layer do not affect other layers. Hence, this provides a significant flexibility of LTE adaptation towards the requirements from authority side. With regards to LTE adaptation, in the High-Speed Railway Communications paper [6], authors suggested prioritizing LTE configuration towards the coverage area. Moreover, in order to reach the stable connection with the train that is capable to reach speed up to 500km/h, it is recommended to implement quadrature phase-shift keying (QPSK) modulation. In addition, the packet retransition is recommended to be minimized. Thus, a preferred transport layer protocol is UDP.

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Despite the above-mentioned advantages, one of the major issues of LTE is the lack of voice calls support. MNOs have solved this problem by switching to the 2G or 3G networks. However, this is the limitation that prevents LTE from becoming a standalone standard in the rail industry. According to Gessner [15], there are three scenarios of solving this problem. The first one is to implement Voice Over LTE (VoILTE), which requires an expensive IP Multimedia SubSystem (IMS). The second approach is to implement Circuit switched fallback (CSFB) that would allow forwarding voice calls to the GSM-R network. However, as it was mentioned in Section 3.4, a migration process from GSM-R to the newer standard will be initiated in 2022, making this approach obsolete by that time. The last approach is simultaneous Voice and LTE (SVLTE). However, SVLTE has a certain requirement towards handsets, that making them considerably more expensive.

2.4.2 Spectrum Availability

Spectrum allocation is one of the major challenges for MNOs. Telecom operators are challenged with the selection of the spectrum that will satisfy both the coverage area and the network capacity. The selection of the spectrum is governed by the National regulators, who are responsible for the spectrum allocation and its availability for the auction. Hence, spectrum allocation is one of the major considerations in the implementation of LTE in the rail industry. The recommendations of the spectrum selection are primarily based on the Nokia’s paper [13]. LTE provides a flexibility in the selection of the frequency band and channel bandwidth. Thus, the major considerations are the national wide regulations and the requirements of services that are going to be deployed on top of the LTE. One of the major considerations regarding the LTE-R is whether the GSM-R’s band is going to be reused or abandoned. This question remains open before the official statement from regulators. The range between 700 MHz and 800 MHz bands have been selected for the “digital dividend” initiative. The idea behind this project is to migrate the analog television to digital on these bands in Europe. Moreover, according to Nokia, in North America and Korea this band has been used for the public safety services. Bands such as 900 MHz, 1800 and 2100 MHz are currently used for serving 2G and 3G networks in Europe and Asia. Over the time, these bands are expected to be reused for serving 4G and even 5G. 450 MHz band offers a wide coverage area that makes it attractive to implement for the public safety and mission critical services. Moreover, in the railway industry, this band is reserved for the Terrestrial Trunked Radio (TETRA) communication that is serving mission critical tasks in, for instance, signaling.

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Thus, Nokia recommend selecting other bands, for instance, 1.4 MHz, 2.6 MHz, 3 MHz and 5 MHz. The motivation is the following: despite the small coverage area, these bands are suitable for high dense areas by offering higher throughput and better spectrum efficiency. That is especially relevant considering high density of end devices brought by passengers and high train density. In contrast, authors of the High-Speed Railway Communication [15] disagree with the selection of the above-mentioned bands. Their major consideration is that higher bands are experiencing larger loss and more severe fading. In addition, the coverage area is less than 2 km. Considering high requirements for the signal to noise (SNR) and bit error (BER) rate in the rail industry, it would require a higher density of the base stations, leading to significantly higher capital expenditures. Authors have mentioned the case in China [17] when the LTE has been allocated to 458.7 and 468.7 MHz between rural analog access network 457.2 and 467.2 MHz and public security network 459.3 and 469.3 MHz. Moreover, in Europe UIC is considering to implement the next generation rail communication standard on top the current mast sites. This can save between 80-90 percent on the capital expenditure on the deployment of the new standard. Thus, in Europe spectrum bands under 1GHz are considered more cost efficient.

2.4.3 LTE –R Functionalities

As it was mentioned in the Section 3.4.1, at the core of LTE –R is a packet switching technology that provides an opportunity to deploy non-critical services on top of it. First and foremost, LTE forms a foundation to empower real-time monitoring tools that could be used in multiple uses cases, for instance: real time train performance monitoring, maintenance of the physical conditions and even monitoring rail-track’s conditions [13]. The major requirement for real-time monitoring application is to have latencies bellow 300ms that are met by LTE. Despite the fact that GSM –R is used in the real-time monitoring as well, the number of use cases is very limited, in particular mission critical monitoring of signaling systems. Meanwhile, LTE –R allows to extend the number of the use cases. In addition, LTE –R allows to transmit the CCTV traffic, instead of having a separate network, leading to the lower both capital and operational expenditures. There are multiple use cases of CCTV in the railway industry such as: monitoring rail tracks, on station and on board monitoring leading to securing both passengers and physical property. Nokia is emphasizing that LTE –R allows to empower real-time video analysis applications, that are aimed at detecting hazard activity and triggering an alarm.

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Last but not least, LTE -R provide enough capacity to support an onboard Wi-Fi and additional services such as infotainment and ticketing applications.

2.4.4 LTE-R Limitations

Despite the above-mentioned advantages of LTE, there are couple of limitations that have been discussed in this section. It should be pointed out that inability of LTE to support voice communication has been previously discussed in the Section 3.4.1. During the travel time, trains are travelling through multiple areas such as open-areas, tunnels, viaducts and cuttings. Each area has a certain impact on the signal performance. According to 3GPP publication [18], in case of LTE –R, only non-fading channel mode was implemented in response to open-areas and tunnels. In contrast, authors of the article Challenges toward wireless communications for high-speed railway [19] were emphasizing that the propagation characteristics in areas such as viaducts and cuttings are different from other areas, leading to performance degradation. This statement applies both for GSM-R and LTE-R. In the study Shadow Fading Correlation in High-Speed Railway Environments [20] examined given problem for the GSM-R standard. Based on results the solution was following, to implements a scenario-based path loss and shadow fading model at the band rate of 930MHz. However, this question remains open for the LTE-R. In addition, the construction of the train could add additional challenges in receiving the signal. First and foremost, trains are made out of metal that is resulting to signal reflection, leading to the increased signal delays. Meanwhile, the increase of the nonstationary aspect of channels could be observed when the train is in the motion. Also, it should be pointed out that the interior of the train could result in the signal propagation [15].

2.5 Conclusion Considering above mentioned facts the answer question of interest 1 : “What is the communication standard that would allow meeting the requirements from the application side of the project?” is following. GSM-R is suitable for the mission critical tasks. Moreover, the migration process to the newer standard is planned to be initiated in 2022, while termination time is expected to be by 2030. However, GMS-R provides limited capabilities towards non-mission critical services such as passenger experience, business processes and operations support. On the other hand, LTE-R is able to support bandwidth intensive and delay sensitive services. However, the major limitation is that LTE-R has not been standardized neither in the Europe nor in Asia. Hence, it is restricted to implement it for the mission critical applications. Based on the comparison of GMS-R and LTE-R the conclusion is following in order to meet the requirements of the project ON: BOARD it is recommended

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to implement services and product portfolio on top of LTE network. However, taking into account the lack of standardization of LTE in the railway industry, it is currently recommended not to include mission critical services as a part of the product offer.

2.6 Deployment Strategy The objective of this chapter was to provide recommendation towards the deployment strategy of the LTE, as the part of the connectivity services offering under the scope of the project ON: BOARD. Case company asked to conduct a research regarding the network deployment strategy, in particular, to perform a comparison of the following strategies: implementing a parallel network, becoming Mobile Virtual Network Operator (MVNO) and participate in the deployment of the national wide LTE network. The major considerations were cost efficiency and the speed of the deployment. Based on the above-mentioned facts, a question of interest 2 was formed “What is network deployment strategy recommended to implement in order to meet the strategical part of the project?”

2.6.1 Parallel Network

The first option is to deploy a parallel LTE network among the current GSM-R. In order to minimize an interference among two networks, there is a need for the additional frequency. There are two methods of obtaining another frequency band. The first one is partitioning of the GSM-R frequency. An advantage of such method is that there is no need in participating in the frequency band action. Moreover, there are possibilities of site re-usage. Despite the above-mentioned advantages, there are some challenges arising. First and foremost, it will have an impact of the bandwidth limitations both for the GSM-R and LTE networks. Alcatel-Lucent [21] have examined this problem in the scenario when the GSM-R has been allocated in the 2MHz band, while for the LTE - 1.4 MHz. This had an impact on the LTE data rate, which was below 2 Mbps. In case of the project ON: BOARD, this will have a limiting impact on services that are aimed towards passengers. In addition, the drop in the bandwidth could have a negative impact on the ERMTS functionality in case of GSM-R, which is unacceptable. The second option to obtain a dedicated frequency band. As a result, LTE there are no bandwidth limitations, as in case of the frequency partitioning. Moreover, the deployment of the parallel network on a separate frequency would not effect the GSM-R. However, there are several drawbacks brought by the dedicated frequency band. To begin with, in order to obtain a separate frequency band, Bombardier has to participate in the frequency band auctions that could gradually increase the capital expenditures. Moreover, in each country, there are local band actions. As a result, this could have an impact on the total profitability of the project. Moreover, Bombardier’s strategy towards

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the project is a rapid global expansion. Meanwhile, a time required to participate in actions would significantly slow down the expansion strategy. Lastly, LTE has not been yet standardized in the rail industry. Thus, at the current stage, there is a high risk in making a significant investment in frequency bands that might be not included by authorities as a part of the LTE-R standard. Based on the above-mentioned facts, parallel network deployment strategy is not recommended. The decision has been based on the bandwidth limitations as in the case of frequency partitioning and the increase of the capital expenditure towards the non-standardized technology as in the case of the dedicated frequency.

2.6.2 National Wide LTE Network

The second approach would require an involvement of multiple parties, including major TOCs, agencies, infrastructure owners, regulators and government. This is an ideal scenario where the contribution of each party would allow to form a network with a nationwide coverage area. In fact, both in the US and South Korea the 700MHz band have been used for public safety services over LTE network. In particular, in South Korea have been initiated the world-first LTE deployment in the high-speed railway that is operating on the similar band as public safety services. Despite the both commercial and socioeconomic advantages brought with the implementation of the National Wide LTE Network, there are several considerations that should be taken into account. To begin with, as it was mentioned before, Bombardier is planning a rapid global expansion, rather than focusing on a specific list of countries. Meanwhile, National Wide LTE network requires a tight collaboration among various parties. Thus, the implementation time could be significantly increased, in order to meet the requirements of each party. Lastly, there is a high risk for railway industry participating in the implementation of the National Wide LTE network before an official statement from UIC towards the next-generation communication standard. Even though the National Wide LTE network in theory is an ideal scenario, the advantage of it could be observed only on particular markets. Meanwhile, Bombardier Transpiration was aiming at rapid global expansion, based on the unified framework. Thus, National Wide LTE network project could be applied on to specific markets.

2.6.3 Mobile Virtual Network Operator

The third deployment strategy is that Bombardier Transportations start to operate as the MVNO. This approach would allow to significantly minimize the capital expenditures, due to the contracted operator will be responsible for the infrastructure, including the coverage area and network capacity. As a result,

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Bombardier would be able to narrow down the focus towards the development of services. However, this deployments strategy is leading towards several limitations. To begin with, a third party is going to be responsible for the data transmission, including passenger’s personal data and the real-time information about the train’s status. This is might be a consideration from the client’s side. Thus, this approach is not suitable for the mission-critical services. Moreover, the lack of the network ownership can be resulted inability or limitations towards traffic prioritization on demand. Despite the fact that the capital expenditure is going to be significantly decreased compared to parallel network deployment, the downside of this strategy is a significant increase in operational expenditures over some period of time. Last but not least, Bombardier’s strategy towards the project to become a service provider, rather than MVNO. Considering high deployment costs of Parallel Network Deployment and high uncertainty level of National Wide LTE, MVNO considered as a recommended approach. The major considerations in favor of MVNO deployment strategy was significantly lower capital expenditure and a rapid project deployment compare to the other presented network deployment strategies. In addition, becoming an MVNO could be considered as a temporary solution aimed at the rapid project deployment before the standardization of the next generation communication technology.

2.6.4 Conclusion

Based on results the answer to question of interest 2 “What is network deployment strategy recommended to implement in order to meet the strategical part of the project?” is following. Taking into account project requirements the recommended deployment strategy is to play a role of MVNO.

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3 Background

In the given chapter was explored the focus primarily on the needs of the end customer rather than on competition. Followed by the analysis of the previous studies on the topic of onboard connectivity in public transport. Last, not the least, differences between regular and business survey, followed by the high-level introduction to business survey process, that was further discussed in Chapter 4.

3.1 Redefine the competition According to multiple academic sources [22,23,24], the fundamental objective of every single business is two-sided. From one perspective, business should produce products or services to the society. From another perspective, business has to ensure that in the long run the income is higher than production costs. Thus, profitability is the top priority of the business. At the fundamental level, it is important for a business to have an information about the market, competitors and rivalry products. Nonetheless, by focusing only on competitors, the innovation pace is going to be incremental. It could be observed that rival companies are offering similar kind of products and services with minor features or price differentiation. However, the major risk brought by mirroring competitors strategy is that the final product or service will not be appealing to the end customers. In the Annual Letter to Shareholders CEO of Amazon Jeff Bezos [25] has said: “If we can keep our competitors focused on us while we stay focused on the customer, ultimately we’ll turn out all right.” Companies should switch focus from their competitors towards the end customer. By truly understanding customer’s problems and pain points, it will lead eventually to a product that will provide a much higher value and a unique experience, making the traditional competition obsolete. In the article Obsess Over Your Customers, Not Your rivals [26] published at Harvard Business Review, Tara-Nicolle Nelsons presented several steps that will ensure the transition toward being more customer oriented. The first step is to rethink the sales process. Instead of considering sales as the only transactional interaction with a customer, in the long run it is beneficial an approach by offering a transformational experience. The second step is to reconsider the customer’s profile. Instead of keeping the focus on the existing customer base, companies should broader the understanding of the problem. It might be a case that different target groups are faced with the similar kind of problem. Hence, the potential clients are the ones who are facing with the similar problem, even though they might be in different industries or even types of end consumer, such as Business to Business (B2B) and Business to Customer (B2C).

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The thirds step is to establish a two-sided interaction with the customer directly. The aim of this step is to understand own customer beyond offered product line. This way, an organization would be able to define what pain points that customers are faced with. In addition, it is a great opportunity to track the dynamics in the consumer’s behavior. The fourth step consists of six sub-processes that will eventually lead to the better understanding of the problem that customers are faced with. First of all, it is critical to outline customer’s challenges. Secondly, it is important to understand the context of the problem, such as time and location. Based on the above-mentioned steps, company would be able to create a solution for the end customer. However, this is not the last step. Based on the output from the first two steps, there is a risk the collected data might not be accurately illustrating of the customer’s pain points. Thus, there is a need in additional three steps that would allow forming a truly understanding of the problem. The fourth step is to observe how customers are trying to solve their problems. In the fifth step is to understand the obstacles that prevent a customer from reaching the goal. The last step is to provide a solution based on the collected data from previous steps. As a result, the benefits are two-sided. From one side, companies would be able to explore new opportunities beyond the existing offers. From another, there is a higher chance to create a product that will be appealing to the customers. There are several methods that allow to track, monitor and even communicate with the end customer. One of the most popular methods are statistics, surveys, online data collection, workshop etc. This research is going to be focused on retrieving data and defining patterns based on the collected data via surveys. The aim of the following chapters is to introduce the reader to the concept of surveys, their relevance for businesses and method of preparing and conducting surveys and analyzing the collected data.

3.2 Previous studies According to the American Public Transportation Association article [27], around 70 percent of Millennials (group age between 18-35 years) prefer to travel multiple ways, for instance, a combination of private car, public transport, and car sharing services. An interesting observation is that 44 percent of respondents admitted an advantage of public transport in terms of online socialization. However, an article is limited to the focus group Millennial generation in USA market. Thus, it does not provide an overall picture of passenger’s online behavior regardless of the age group. The Paper presented by IBM [28] reveals that passengers prioritized delay predictability on top of short travel time. Berkley’s publication [29] extends the idea of predictability; in the case of a delay that is not under operator’s control, authors are suggesting providing connectivity solutions for passengers that

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eventually distract from the waiting time. However, the major limitations of above-mentioned studies are that the focus area was limited only toward transport predictability. Information about connectivity features was given only in the advisory manner. On the other hand, a French transport operator Transdev has conducted a study [4] within 2500 French citizens on the topic related to the digital experience in the public transport. Even though the language of the study is French, there are multiple materials in English provided by Transdev that reveals the results of the study [30] [31]. The aim of Transdev research was to detect the passenger’s online behavior during travel time in public transport. In addition, the study covered the passenger's interest towards travel companions and on board Wi-Fi. 79 percent of respondents answered that they would find useful and reverent travel companion application that provide information about the journey and schedule. 46 percent of respondents have shown the interest towards the ability to purchase tickets via smartphone. Moreover, 42 percent of survey participants have acknowledged that would use public transport more often if it would provide onboard Wi-Fi. 68 percent of respondents have ranked the free on-board Wi-Fi in the top three most desirable services. Another study that was performed by Transdev [32] highlighted the fact that 95 percent of local French authorities has agreed that in order to attract people to use public transport more often the approach has to be individual; that could be reached by providing both connectivity and personalized travel assistant services. The result of Transdev studies provide a comprehensive the overview of the passenger’s demands towards onboard Wi-Fi and personalized travel assistant. Another significant advantage is that the research covered not only the passenger’s perspective, but also gives insights on local authority’s opinion. Despite the significant value of Transdev research it is only limited only towards French market. However, it was decided to observe the correlation of results between this research and the study done by Transdev. In particular, to observe free onboard Wi-Fi as a factor that increases passengers’ interest towards the public transport.

3.3 Survey In order to meet the research objective, the survey was selected as the research strategy. Hence, it is critical to outline a general concept of the survey. There is a vast amount of definitions of “survey” presented by both academic and non-academic sources. For instance, the definition presented by Groves is the following: “a survey can be seen as a research strategy in which quantitative information is systematically collected from a relatively large sample taken from a population.” The survey outputs are statistics; “quantitative descriptors” [33, p2].

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Surveys are normally conducted within a population based on some opinion or topic. The result of the survey is usually a summarized compilation of opinions within specific geographical area. Based on the collected data, statistics could be developed. Elements of the survey such as sample frame, questionnaire, communication techniques, data capturing and coding are developed under the scope of established statistical objective.

3.4 Business Surveys Due to the fact that the given survey was done in the business context, it is critical to define the differences amid general surveys and business one. The generic survey techniques and frameworks are widely accepted and could be applied in numerous circumstances. However, there are some considerations that are applicable only towards business surveys. Aspects such as cost, time, requirements in human resources, relevance and periodicity of the data are especially critical within the business context. The data collected via surveys could be used in detecting and analyzing patterns that could directly effect on the company’s strategy and business model. Despite the importance of the survey in the business context, it is considered as an intangible asset, due to absence of the direct effect on the business performance [34, p40].

3.4.1 The structure of Business Surveys

Figure 6 provides an overview of the survey process based on Groove’s recommendations [34]. The aim of this chapter to provide a complete overview of methods and techniques that are critical for conducting business surveys. The following Chapter focused on three major parts of the survey. The objective of the first part is to introduce readers to aspects such as population sampling and questionnaire design, including question variables, question types and length of the survey. The aim of the second one was to provide an overview of the data analyzing and archiving techniques. Last but not least, aspects such as survey ethics and errors are discussed in the third part. After the introduction of the specific method or technique, a separate discussion is followed, where author based on the literature review motivate the selection of a particular method based on the research objective.

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Figure 6 Business Survey [34]

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4 Methodologies

The focus of this chapter was done towards both general research methodologies and survey studies in particular. The objective of the given chapter was to motivate the selection in of particular method under the scope of this research. In order, to be able to do so, the structure of the given chapter is following. First, a high-level overview of the topic was presented, followed by the selection of a particular method or technique that allowed to meet research objectives. In order to motivate the particular selection, the theoretical overview of the topic is presented below. The selection of research methodologies was covered in Section 4.1, 4.2 and 4.3, and were primarily based on the paper published by Anne Håkansson [35]. Meanwhile, the objective of Sections 4.4- 4.11 was to introduce three main building blocks of the survey. The first one, designing the survey according to the research objective, in particular selecting an appropriate question variables and question types. The second building block was focused on aspects such as sample size and sample frame. The third one was aimed at defining methods of aggregating and analyzing the collected data. Moreover, typical mistakes have been covered in this chapters, as well as ethical considerations.

4.1 Research Methods Selection of the research methods is an initial step in the research planning. Thus, it is essential to outline the differences between quantitative and qualitative research methods. The research strategy for the given research was selected cross-sectional survey. Thus, both the overview and the selection of the methodologies was under the scope of surveys and interviews.

4.1.1 Method Selection

In order to meet the research objective and being able to answer research questions, it was made a decision to select the quantitative research method. The objective of this thesis was aimed at defining patterns in the data. The research strategy was selected cross-sectional study, due to time and budget limitations to conduct longitudinal study. The format of the data collection was selected questionnaire, in particular self-completed. Below provided a theoretical background that supports the selection of the research method.

4.1.2 Quantitative Research

Quantitative method is aimed at examining the relationship among variables. In particular, under the scope of quantitative research are conducted experiments and measurements that will eventually prove or falsify the hypothesis or theory. Results of the quantitative research must be based on the large data sets [35].

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According to Håkansson, in order to meet the requirements of the quantitative study, only deductive or abductive research approaches are applicable. Moreover, in case of the research design that implies interaction with other human beings either longitudinal or cross-sectional survey must be selected [35]. The data have to be collected using questionnaires. According to Saunders [36] there are two types of the questionnaire applicable for the quantitative study. The first one is self-completed that is distributed either by hand or by post. The second type is interview-based that requires either telephone or face to face conversation. Overall, a survey conducted under the quantitative research aimed at defining trends and patterns in the data among large data set. Due to the fact that the research objective was to retrieve insight and define patterns in passengers’ online behavior, quantitative research method is applicable. The focus was done towards retrieving the insights in general, rather analyzing each case specifically. Due to time boundaries, the cross-sectional survey was selected. In case of the longitudinal, survey have to be repeated over some period of time, that is not under the research objective. The type of the questionnaire selected self-completed, in the interest of time and minimize the refusal rate.

4.1.3 Qualitative

Qualitative method is aimed reaching an understanding of a concept, theory or even behavior. This method analyses non-numerical data with relatively small data sets [36]. According to Håkansson, only inductive or abductive research approaches are germane to qualitative studies. The data collection is performed using interviews [35]. According to Eriksson and Kovalainen [37], interviews are divided into three main categories. The first type is called structured and standardized interview. The idea behind it is that interviewee have to answer on predefined questions. It is prohibited to paraphrase questions during the interview. The second category is called guided and semi-structured interviews. For this type of interview specific topics are predefined. However, formulation and order of questions could be done in a free manner. The last type is called open interview. The core idea behind it that researcher is allowed to changed topics and questions during the interview, leading to analyzing in the depth the topic. Despite the differences in approaches, all three types of interviews are pursuing the goal to study a topic in depth by reveling information from insiders. Qualitative research is not applicable towards the given research. The objective was to retrieve insights from passengers in general rather than focusing on understanding of their behavior. However, qualitative method would be used in future studies as it mentioned in the Section 7.1.

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4.1.4 Triangulation

Some of the research objectives are requiring to explore the topic from both sides in order to create a complete overview. Thus, a triangulation research method (mixed research) is used. Triangulation combines both quantitative and qualitative research methods [35]. According to Saunders [36], there are three types of the triangulation research methods. The first one is called concurrent and the idea behind it is that the data collection and analysis of both quantitative and qualitative part are done simultaneously. The second type is called sequential exploratory, where the data collection of one method is followed by another. The last category of triangulation method is called sequential multi-phase. The main idea is that each method could be used multiple times, meanwhile, they are following each other similar to the sequential exploratory category. Despite the advantages brought by triangulation method, it was decided not to select it for the given research. The major consideration was time boundaries set by Bombardier.

4.2 Research Approach Research approach defines the research process that will eventually lead to the conclusion. There are three types of the research approaches: inductive, deductive and abductive.

4.2.1 Approach Selection

In order to be able to draw a conclusion, it was decided to select an abductive approach. The initial objective of this research was to form a hypothesis towards passenger’s interest based on the result of the data collection. Thus, an inductive research approach is applicable. Even though in Section 3.2, Transdev published results on the similar topic have been presented, they were not considered as the hypothesis for this study, due to differences in the geographical locations. Meanwhile, to be able to provide recommendations towards the selection of the communication standard the deductive approach is applicable.

4.2.2 Theoretical Background

The idea behind of inductive approach is that based on the result of the research a theory is formed. Håkansson emphasized that the most common research method for the inductive approach is qualitative. It has been motivated by the fact that conclusion is formed by observing either behavior or opinion. The deductive approach, on the other hand, aims at proving or falsify the theory or hypothesis based on experiments and measurements. Due to the fact that deductive approach requires large data set to draw a conclusion, a quantitative

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research method is commonly used. Deduction approach is focused on the data consistency and reproducibility. Thus, it is required a detailed description of the preparation and the measurement process as well as an explanation of the relationship among the obtained variables. Abductive approach is a combination of the deductive and abductive approaches. It is used to form a hypothesis or theory similar to inductive approach. However, the major difference is that some data has been already present. Thus, there is a need for additional measurements, in order to form a theory.

4.3 Research Purpose In this section is described four types of the research purposes: explanatory, descriptive, evaluative and exploratory. The objective of the explanatory research is to observe a theory or phenomena from a different perspective, in order to contribute to the further development of it. The descriptive research is primarily based on the results from the previous exploratory research. The idea behind it is to analyze a specific problem from the different perspective. An evaluative research is used for measuring and benchmarking a theory, hypothesis and even a product. Explanatory, descriptive, evaluative research are not applicable for the given thesis, due to the fact that the was problem not based on the theory or hypothesis., Exploratory research is aimed at explaining the relationship among variables, based on which a hypothesis could be formed. Considering that the research problem was the lack of data analysis to form a hypothesis, the given thesis is characterized as exploratory research.

4.4 Sampling Individuals and organizations that are relevant for the research are called the population. According to the United Nations [38] in case of the small population size, the whole population could be involved in the research. This technique is called census. Nevertheless, census is not applicable to the large population. Hence, sampling is used for the research. The definition of the sample given by Paul Smith [39] is the following: sample is an observation of only some parts of the population that are relevant for the research. All the collected data from the sample is going to be generalized for the whole population. Sampling allows decreasing the costs and time frame of the survey. On the other hand, by observing only a part of the population the sample error ratio is increasing. Due to the budget and time limitation allocated for this research, it was made a decision to survey the participants based on sampling. In order to obtain credible results, it is critical to justify the selection of the population sample. Thus, it is critical to select the proper sampling technique that is applicable to the research. There are two major sampling techniques that are pertinent to the

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academic research: Probability and Non-Probability technique. Each of them were examined in the details below.

Selection of the Sampling Despite advantages brought by the probability sampling technique, such confidence level and minimizing the sampling error, it has been decided to apply the non-probability technique. The major consideration was the fact that Bombardier Transportation allocated for the overall survey process 21 day. Thus, the non-probability technique was selected, in particular quota-based sampling. The motivation was to be able to adapt the sample size towards time-frame limitations, while obtaining credible results from the population. In case of the conflict in the collected data, it was considered to perform a data saturation. The selection of the sampling techniques was based on the literature study, where as the primary source has been selected George’s and Seber Mohammad’s Book “Adaptive Sampling Designs Inference for Sparse and Clustered Populations”. [40]. Below are provided a theoretical overview of both for the probability and non-probability sampling, based on which the selection of the sampling method was done, including a detailed description of building blocks that are essential to each technique. The description of each method was analyzed if it was applicable towards research goal or not.

Probability Sampling Probability sampling is applicable towards the population size that is known. Given sampling technique consists sampling frame, sample size and sampling techniques. Sampling frame is a complete list of participants that are the part of the defined population. For nongovernment organization, it is rather challenging to construct own sampling frame due to the limited access to the administrative information. Thus, the sampling frame could be based on the previous studies, yellow pages, credit ratings or membership list [40, p2]. According to Årsberättelse 2013 (annual report) published by SL [41, p59], the total number of passengers in 2013 within the Stockholm area was 757 million. Where the total number of passengers of Commuter Trains (Pendeltåg) 82 million. The focus of this thesis was done on passengers that were commuting using Commuter trains on daily basis. Thus, the defined sampling frame in the given research was 225’657. Sample size allows to limit sampling frame to a smaller number of minimum required number of participant under the controlled certainty level. An essential part of sample size is confidence level that represents the level of certainty of the obtained data based on the total population. According to the Field [22], the recommended confidence level is 95 percent that is leading to 0.05 significance level. On behalf of confidence level, it is critical to select the margin of error. It stands for the accuracy of the observed data. For instance,

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instead of claiming that 20 percent of participants are satisfied with services, with the margin error is 3 percent, the result is following: between 17 and 23 percent of participants are satisfied with the service [40.p.2]. Considering the size of the population relevant for the research (225’657), the confidence level of 95 percent, and margin error 3 percent, the total sample size was 1’063. After defining the sample size, the following step was the methodological data collection obtained using sampling techniques. There are several sampling techniques that are applicable to the probability sampling. The first one is a simple random sampling. The idea behind it is that a list of the whole population is present and the sampling size has been defined. The selection of participant is going to be based on the random and non-repeating numbers. For instance, the size of the population is 2’000 and the sample size is 556. Thus, the generation of the random numbers is following: 556 of random numbers that are in the range between 1 and 2’000. Based on the result, the selection of participants is going to be done [40, p3]. The major constrain of the simple random sampling towards the given research was the fact that it was required to have a complete list of passengers, based on which the collection of sample could be randomized. However, due to customers’ data privacy concerns, SL did not publish customer user base. Thus, the implementation of simple random sampling was not possible under the given research. Moreover, it is essential to clarify that in case of such list of all passengers’ would exist, there would be an extremely low probability to interview people based on simple random sampling, due to the unforeseeable behavior of each passenger who’s name was included in the list. The second technique is called systematic random sampling. An essential part of it is sampling fraction that could be calculated by dividing the sample and population size, where the population size is the denominator and sample size is a nominator. In the case when the population size is 2’000 and the sample is size 400, the sampling fraction is going to be 1/5. The next step is to generate a random number between 1 and 5, based on the sampling fraction, that is going to be used as the first case. Based on that the selection of the following numbers is going to be done [40, p3]. Similar to simple random sampling, systematic random sampling was not possible to implement in the given research, due to lack of access to customer base. The third technique is called stratified sampling technique. The idea behind it is that a stratification is done within a sampling frame according to the relevant variable that could represent age or gender group of participants. For instance, the observed population is based on the age group. The total sample frame is 300. Thus, an equal number of participants out of each age group is going to be selected. The next step is to select the random participants based on either simple or systematic random sampling [40, p4]. Despite the lack of user base data, the major constraint towards the stratified sampling technique was the fact that the research objective of the given research did not have any intentions to stratify passengers based on the specified variables.

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The final technique applicable for the probability sampling is called cluster sampling. The logic behind the cluster sampling is similar to the stratified sampling, where the population is divided by variable. However, in the case of the cluster sampling, the variable occurs naturally. As an example, consider a geo-location division where the variable could be a district, city or state. Simple or systematic random sampling could be used for the sake of randomization. [40, p6]. In the given research, the cluster sampling was based in the location (Stockholm area) and the type of the public transport (Commuter train) that passengers selected. However, the following randomization either by simple or systematic random sampling would not be possible to implement, due to lack of customer base data. Despite the fact probability sampling provides a unified approach sampling frame creating and well-defined confidence level, it was decided not to follow the given approach. The major constraint was the fact sample frame of 1’063 participants would require the larger time-frame for the data collection, approximately 21 days, in case of 50 participants would be interviewed per day. It should be pointed out that large data volume would increase the time required data aggregation and analysis. Meanwhile, the time-frame that Bombardier Transportation allocated for the given research was 21 days, including planning of questionnaire, performing pilot run and following discovered improvements, survey process itself, and aggregating and analysis. Thus, in the interest of the time-frame, it was decided not to select probability sampling as a part of the given research. Lack of the complete list of SL’s customer base, mentioned in the description of simple and systematic random sampling methods, did not effect on not selecting of the probability sampling. The reason was that lack of such list would not have an impact on the sample frame, only on data collection process.

Non-probability sampling In the case of non-probability sampling, the size of the population is not known or defined. In addition, non-probability sampling is applicable to the research that have limited budget and time-frame. In the case of non-probability sampling technique, there are several types of sampling that could be applied. The first one is Homogenous. According to Chambers and Clark [42], a homogenous population is combined based on the common characteristics, for instance, type of company, job occupation and age group. The sample size of the homogenous population should be at least between 4 and 12. On the other hand, nationwide research could be applied to the Heterogeneous population, where participants belong to the diverse occupation, age, and ethnical groups. The recommended sample size is at least between 12 and 30 [43]. Based on the objective of the given research, the applicable sampling was Heterogeneous, in order obtain the data from the overall population, rather from specific groups based on the common characteristic.

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Data saturation is a method that can be applied for the non-probability technique. To be more precise, it allows to increase the sample size up to the point when the result would reach equilibrium. In other words, the results from new participant will not provide new information. In the given research, data saturation was considered to be used only in case of the conflict of result. For instance: 47 percent of respondents were not interested in the offer proposed by Bombardier, while 53 percent would find it useful. In such scenarios, additional data collection should be performed to form a solid conclusion. According to Tansey [44], there are four sampling techniques that are applicable for the non-probability method. The first one is quota sampling. It is required from the researcher to establish a quota for the sample that is based on certain parameters. The quota could be, for instance, 1 percent including stratification based on the gender, age group and occupancy. For the given research, quota sampling was considered as a preferred sampling method, due to possibility to adapt the sample size towards time-frame limitations, while obtaining credible results from the population. In case of the conflict in the collected data, it was considered to perform a data saturation. The second technique is called purposive sampling. Compared to simple and systematic random sampling, that are under the scope of probability technique, the purposive sampling is completely opposite. Instead of relying on random numbers in selection of samples, as it is in the case of purposive sampling, the selection is based on the researcher’s judgment in selecting the sample [44, 770]. There are four subcategories of the purposive sampling that are listed below:

• The first one is extreme case sampling where the focus is on the deviant or no common cases. Not applicable towards the given research, due to the fact that the research objective was to define common patterns in the customer’s data.

• The second one is heterogeneous case sampling. The idea behind it is to obtain variations of one key variable or component at the wide scale. Heterogeneous case sampling was decided not to use in the given research, because on top of customers’ interest towards the project, other variables were measured as well.

• The third subcategory of the purposive sampling is homogenous case sampling that is completely opposite to the heterogeneous. The focus there is on observing similarities of the key component. Homogeneous case sampling is used for observing a specific subgroup of the population. In the given research, there were no intensions to sample only a specific group. Thus, homogenous case sampling was not applicable.

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• Critical case sampling is considered as the fourth subcategory and is aimed at observing and analyzing extreme cases. Under the scope of the given thesis work, the objective was to define average result. Thus, critical case sampling was not applicable towards the given research.

The third sampling technique is called volunteer sampling. There are two major subcategories of the volunteer sampling: snowball and self-selection sampling. Snowball sampling is recommended for the research that is experiencing the lack of publicly available data. Thus, the selection of the samples is based on the recommendations from various parties. Self-selection sampling is based on a volunteer basis. For instance, survey could be posted online [44, p770]. Snowball volunteer sampling was not applicable within the given research, due to limitations in the area where the research was conducted. While, self-selection method was decided to implement, due to lack of the control on the response rate per day. The fourth and the last non-probability technique is called haphazard sampling. The selection of the cases for the sample is based on their reachability and availability. However, there are certain consideration regards to the creditability of results [44]. Haphazard sampling was decided not to implement in the scope of given research. In the data sampling the data creditability was prioritized in favor of reachability. To summarize, based on the above-mentioned theory, it was decided to that non-probability sampling technique was applicable for the given research. The major consideration was that obtained results would provide a general overview regarding the area of the research. To systemize the data collection process quota sampling was selected.

4.5 Questionnaire variables Identifying questionnaires variable is a mandatory part in constructing every single survey. According to Willem [45], there are three major subcategories of questionnaire variables: attribute, opinion and behavioral variables. Questionnaire variables were used to answer both RQ1 and RQ2. Attribute variable is considered as the first subcategory. Their aim of it is to define the profile of respondents. Attribute variables allow fetching the result according to some group of the population based on the gender, age group or occupancy [45]. Attribute variables were selected as a part of this research to answer RQ2: How does the potential customer’s profile look like? Collected result were fetched based on the age group of participants. As a result, it was possible to draw a conclusion regarding the interest towards the project and define most effective communication channel for a particular age group. The second subcategory of questionnaire variable is called the opinion variable. The idea behind it is to retrieve an opinion, impression or even interest. True and false question are widely used for asking an opinion. Combination of

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opinion and attribute variables provides a comprehensive result towards the interest of the specific group of populations [45]. Opinion variable was used in the given research to define populations’ interest towards the project. Result collected under opinion variable were later combined with an attribute variable, in order to fetch results for a specific age group. The last subgroup is called behavioral variables. The aim of this type of questionnaire variable is to define the action that participant did in the past either once or on continuous basis. In addition, behavioral variables are widely used when retrieving participant intentions to perform an action in the future [25]. Behavioral variables played an essential role in the given research, in particular at answering the RQ 1: What are the patterns in the passenger’s online behavior in public transports? In addition, behavioral variables allowed to define the average time spend in commuter trains daily, other types of transport that respondents were using daily, monthly mobile data selection and preferences in social media. To summarize, all the above-mentioned questionnaire variables were used in the given research. Attribute-variables were used to answer RQ2, while behavioral variable were used to answer RQ1. Opinion variable allowed to define participants interest towards the project.

4.6 Type of questions There are several types of questions that are applicable towards the survey. The selection of a specific type of question could affect both the results of survey and participant intentions to get involved in the survey. Thus, it is critical to outline and describe each type of question in details. Fink [46] has identified five types of survey questions, including: list, category, ranking, rating, and open-ended questions. The first one is list questions, that provide an opportunity for respondents to give an answer by selecting predefined options from the list. Under this type of questions, it is allowed to provide multiple answers. However, the key limitation of list questions is that respondent is limited to options that are provided in the form. Hence, list questions are more suitable for the qualitative type of research [46]. List questions were used in the given research to define other public transport that respondents were using on daily basis. Hence, extending the understating of passengers’ behavior. Information about other transport usage could be used in a further research as a part of service expansion beyond the commuter trains. The second type of questions is called the category questions. This type of questions is similar to the list questions in a way that it requires respondents to select predefined answers from the list. However, the major difference is that in category questions it is allowed to select only one answer [46]. Category questions were used for selecting the age group of participant, preferred

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monthly data package and overall satisfaction with quality of connectivity, and the interest towards the project. The third type is ranking questions that require respondents to prioritize answers based on relevance or importance applicable to them. The advantage of these type of questions is that they can potentially provide a broader view on the topic of the research. However, there is a risk in biased and non-objective answers due to the lack of knowledge in the topic. Moreover, ranking questions are requiring more time from participant to compare questions. Thus, it is not recommended to exceed the number of ranking questions [46]. Ranking questions were used in the section were participant have to rate witch social media network they were using during commuting time. It was asked from participant for to prioritize between 0 (no usage) to 3 (always) listed names of social networks. Based on that, the most effective communication channels with customers were defined. Rating questions are considered as the fourth type of questions. The use case for the rating questions is to ask in standardized manner to select respondent’s opinion about the given topic. It is recommended not to provide a neutral option, in order to outline results more clearly of the population's opinion [46]. Rating questions were not used in the given survey. The preference was given in favor of ranking questions, that allowed to draw a conclusion of the users’ social media preference and experience. Meanwhile rating questions would only highlight only participants’ opinion about particular social platform. The last type of questions is called open-ended questions. The objective is to allow respondents to fill in the answer in their own words. It is a great opportunity to have insights of the exact opinion on the given topic. However, this type of questions is not recommended for the quantitative research, due to the fact that focus is on the amount of collected data. The reason is that it is challenging to analyze answers that have been written in unstructured form. Thus, there is a chance that results may turn out to be vague [46]. Since research design was selected to be quantitative and the research objective is to identify in passengers’ online behavior, it was decided not to implement the open-ended questions. The reason is that above-mentioned types of questions are primarily aimed for the qualitative studies, where the focus is on the level of details in answers. To summarize, under the scope of the given research it was decided to select list, category and ranking questions. Above-mentioned types of questions were suitable of the quantitative research. Moreover, a combination of these question variables allowed to define customers’ profile.

4.7 Length of the questionnaire According to the Berdie’s article [47], in order to receive high response rate, it is recommended to construct short questionnaires. Regarding the size of the questionnaire, Blumberg, Fuller, and Hare [48] have defined the correlation

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between the response rate and the size of the questionnaire. The highest response rate of 30 percent has been reached with the one-page form. Meanwhile, enquiries that were having more than one page, experienced a significantly higher refuse rate. Even though long surveys have lower response rate, according to Cape and Phillips [49] the dropout rate is almost identical for both short and long surveys. Another interesting observation made by Cape and Phillips is that the design of the questionnaire can have a significant impact on participant’s perception of the size. One of the suggestions was to keep attention towards the wording. Instead of asking questions in a formal way, authors suggested focusing on engaging participant with an emphasis towards personalization and informal wording. Authors highlighted that by acknowledging participant's competence, their interest can increase noticeably. Last but not least, participant appreciate the openness towards goals of the survey. Thus, objective and values of the survey are recommended to be clarified for participants [49]. Based on the above-mentioned information, it was made a decision that the length of the questionnaire should not exceed one page. The major fact was to minimize the refusal rate.

4.8 Data Analysis The aim of the data analysis is to transform obtained data systematically to the statistical results that represents opinion of the observed population. According to Snijkers [50], data analysis could be subdivided into primary and secondary analysis. Primary analysis is aimed for the data that is being analyzed for the first time, for instance, results from the survey. The aim of secondary analysis is to either verify or update previous results. The objective of the given research was to retrieve and analyze data for the first time. Thus, the analysis characterized as primary. There are multiple techniques available for analyzing the data received from surveys. The selection of the method is based on the type of the data that has been collected, the time frame of the survey and the construction of the questionnaire. Bellow provided a theoretical overview, followed by the discussion whenever the particular data analysis method was applicable or not towards the given research. Selection of Data Analysis Data analysis is playing an essential role in the research, in particular, to meet research objective and being able to answer research questions meticulously. In order to answer RQ1, one-way tables technique has been used to define patterns of a particular parameter. In contrast, in order to answer RQ2, cross-

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tabulation method will be used due to the fact that multiple parameters should be linked only to the particular group of passengers. Theoretical Background Repeated cross-sectional and longitudinal types of surveys are conducted over some period of time in order to define variations in the monitored data. In order to define patterns or variations in the observed data either aggregate or micro level data analysis is used. According to McGuckin [51], aggregate analysis is used for outlining the overall trends and patterns. For instance, monitoring the vitality of the company or even industries in general. On the other hand, micro level data analysis is applied in order to monitor particular component out all of the data. Meanwhile, micro-level data analysis is aimed towards monitoring particular company’s case, for instance, employee's’ performance or customer’s awareness and satisfaction [51, p511]. Considering that the objective of the given research was to define patterns on the market in general, the selection of the analysis was given in favor of aggregate. Both for the repeated cross-sectional and longitudinal surveys it is allowed to modify the survey design over some period of time in case of the aggregated data analysis. However, it is required for the researcher to acknowledge about the modifications. In case of the micro level data analysis, it is prohibited to perform any changes, in order to minimize the interference brought with either modified questionnaires or even method of data collection. [50, p511] It should to be pointed out that the data collection for the given research objective was done for the first time. Thus, no survey modifications were done during this research. However, survey modifications might appear in future studies, in case of specific area would require additional studies. According to paper by Statistical Service Centre [52], the first step is to produce data listing, in order to define patterns in the collected data. In case of the verbal interview, the listing is a recording of the conversation. For the result obtained from the pen and paper or online enquiries, authors recommend to list and adjust the data using Microsoft Excel. For the qualitative type of research, it is advocated to code the data numerically and arrange it a tabular form. As a result, output can be graphically illustrated. There are two methods of the data tabulation. However, before listing them, it is critical to outline recommendations that are valid for both of them.

One-Way Tables One-way tables are considered as the simplest method of data analysis. The idea behind them is to produce results for each question separately. One-way data tables provide a general overview of the survey’s results. However, the major limitation is that this type of data analysis does not reveal the dependency within multiple results [50, p18]. In order to answer RQ1, it was required to generate a general overview of the survey’s results, based on the analyzing of

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each survey question separately. Thus, one-way tables were used as a part of the RQ1.

Cross-Tabulation Cross-tabulation technique allows mapping results between each other. This method allows scaling tables to the multiple tables. As an example, consider the relationship of answers with the gender, age group and job occupation of participants. Authors emphasized the importance of selecting relevant components of the characteristics, which lead to more clear and transparent results. In fact, results are represented as the percentage for that specific criteria [50, p18]. In case of multiple answers questions, there are three methods of analyzing the data. The first one is called multiple dichotomies. The idea behind this method is to list all the collected answers in columns. However, this method adds complications to the large surveys. The second method is to calculate the total number of responses for the specific criteria. The third method suggests organizing answers into two columns. The first column stands for the criteria, while the second one represents responses. The total number of rows is equal to the number of responses Cross-tabulation allows to outline patterns in the respondent’s answers. Thus, an overall picture of specific group of population can be stated. However, cross-tabulation is not applicable at individual level data analysis [50, p19]. The objective of the RQ2 was to define customers’ profile based on multiple criterias. Thus, cross-tabulation was used. In particular, the method of two-columns, when multiple answers such as preferences, opinion and behavior were mapped to the age group.

4.9 Data Archiving For the research validity, it is critical to archive any information that is related to the survey. For instance, the information about the data collection, obtained results, variables, methods of analysis and the final report. In addition, it is recommended to add information such as the geo-location, time and date of data collection [50, p540].

4.10 Survey Ethics Notwithstanding the importance of selecting a proper sampling technique and questionnaire design, ethical considerations were an essential aspect of the survey. Within ethical concerns, aspects such as volunteer participation, privacy of collected data and accurate representation of results have to be considered. Under the scope of this chapter, various ethical consideration at multiple survey stages has been covered. This Chapter is based on the principles outlined both in Belmont Report and 45 CFR 46. As an academic source, author

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has selected Chapter 3 from Handbook of Survey Methodology for the Social Sciences, that have been majorly contributed by Oldendick [53, p23]. Belmont Report has been developed back in 1975 by the National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. At the core of Belmont Report aspects such as respect and justice on a personal level, including the protection for the well-being. Based on the principles of Belmont Report, a Federal Policy for the Protection of Human Subjects (45 CFR Part 46) has been developed. 45 CFR 46 consists of guidance and recommendations for conducting research that involves human beings. Some general principles are applicable towards surveys [53, p24]. First of all, participants should be informed that the participation is on a volunteer basis. In addition, participants should be acknowledged that they can withdraw their involvement at any time without any consequences. In case of underage participants or people with a mental disability, it is required to obtain a written permission from their supervisor. 45 CFR 46 outlined that in social related studies any potential physical risk should to be minimized. Meanwhile, the survey process should be designed in a way that does not require any expenses from participant’s side [53, p26]. Secondly, 45 CFR 46 outlined principles towards transparency of the research objectives. Participants have a right to receive an information about research objectives and interest groups. If the study pursues any business or social-related benefits, participants should be informed about them in advance [53, p26]. Thirdly, participants have certain expectations regarding their responses anonymity and confidentiality. Anonymity stands that the answers given by participant cannot be linked to that person. However, in case of attribute variables such as specifying gender, age group, race and occupation there is a certain level of risk for the anonymity. Thus, 45 CFR 46 recommends bold principles that would help to avoid any linking to the participant’s identity. First and foremost, any personnel that is involved in the data collection or analysis have to sign a Non-Disclosure agreement. In addition, personnel have to be instructed about the importance of data confidentiality and that any leakage of the information could result in the termination of the employment. Secondly, in case of digital surveys, data should be protected with a password and access should be granted only for authorized personnel. Thirdly, without a doubt, any personal information about participants such as name, date of birth, phone number and address should be removed in the interest of data confidentiality [53, p27].

4.11 Survey Errors There are multiple types of survey errors that have a potential to effect on the overall result of the study. Thus, it is critical to outline most common issues. In addition to that, it is essential to clarify what kind of trade-offs might occur in

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the survey process. The objective of this section was to develop an understanding of various types of survey errors and define their effect on the results of the given study. In this section was done an introduction to various concepts. The survey error assessment for the given research was done based on the survey result that are reflected in the Section 6.3.

Total Survey Error (TSE) paradigm represents various types of errors and potential trade-offs that are common for the survey. TSE is a framework that defines and summarizes most common that have a critical impact on the over study. A theoretical overview of TSE is primarily based on the Handbook of Survey Methodology for the Social Sciences by Gideon, in particular, Chapter 4 that is written by Bautista [54, p,37].

The objective of TSE framework helps a researcher to obtain as accurate as possible data from surveys, by examining various errors that might occur at different stages. The focus of TSE is aimed towards data quality by exploring primarily two major type of errors: sampling and non-sampling. In fact, TSE is a combination of both above-mentioned type of errors as it presented in Figure 7. It should be taken into account that Coverage, Nonresponse and Measurement Error are considered as the non-sampling type of error [54, p38].

Figure 7, Total Survey Error

All above-mentioned types of errors have a great impact on survey’s bias and variance. The definition of bias is following, it is a systematic errors or pattern deviation from the population value that has a tendency to take place in one direction. Variance is considered as the uncertainty from the population’s value. Both bias and variance represent the Mean Square Error (MSE) that illustrates the variation between statistical and true value. The MSE formula is following: MSE = bias2 + variance. The objective of MSE is to establish an estimator that allows evaluating the effect of unknown parameters on the overall survey’s biases. Below are examined errors that have an impact on the survey’s results [54, p39].

Specification Error The first type of error is called the specification error. This type of error typically occurs when the questionnaire is not linked towards the research objectives. In particular, the root of this type of problem in most of the cases is poor planning and lack of an understanding of the research objectives. The impact of specification error is that the collected data does not meet the research objectives. Thus, results became irrelevant. In order to avoid this type of errors, Bautista recommends to focus on the research planning and defining the

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research objectives. Based on the background overview and detailed research planning the questionnaire could be developed [54, p39].

Processing Error Processing error is considered as another type of error. Usually, it occurs in the data analysis step. As a result, it leads to the wrong data interpretation. There are multiple factors that could lead to the processing error. Firstly, it can be a consequence of selecting wrong coding techniques done by the researcher. Another factor could arise during analysis of the open questions and the following misinterpretation of the participant’s answers. In order to prevent processing error, it is recommended to focus on selecting the proper data analysis technique on the early stage, before conducting the actual survey. In addition, on the stage of constructing the questionnaire researcher has to assume how the obtained data will be analyzed [54, p39].

Sample Error The major limitations of every single survey are time and cost constraints. Researchers are willing to obtain latest data in short period of time. Meanwhile, due to the fact that statistical data is considered as an intangible asset, the required budget should be minimized. As a result, only part of the population is getting sampled, as it was discussed in the Section 2.5.2. [54, p40]. Even though sampling allows us to retrieve population’s opinion with minimized cost, the major limitation that results represent only a portion of the population, leading to the sampling error. In fact, the sampling error is subdivided into two categories: sampling variance and sampling bias. Both categories are examined below. In addition, methods that allow minimizing the impact of these types of errors were presented and analyzed [54, p40]. Sample variance could be observed in a difference of the results within the similar population on the same topic over some period of time. By increasing the sample size, the sample variance is decreasing. However, this leads to the tradeoff between acceptable variance and required time and budget. Another method to minimize the sample variance is to implement stratification. In other words, subdivide population based on categorizes, as it was discussed in the Chapter 2.5.2. By subdividing the population under multiple groups, the overall uncertainty is decreasing. Moreover, if there is a need, the survey could be repeated only within a particular group. Clustering is considered to be another method that allows decreasing the sample variance. Clustering is similar to the stratification, where the population is subdivided based on the categories. However, in case of the cluster, a population in most of the cases is divided based on the geo-location. In order to minimize the impact of the sample variance, it is highly recommended to combine both stratification and clustering [54, p40].

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On the other hand, sampling bias occurs when not all members or groups of the population have similar opportunity to be involved in the survey. The main characteristic of sampling bias is that there is a systematic error that limits the research by obtaining an excessive data only from one group, while other groups are having less probability of being selected. Thus, survey results become biased towards only one group’s view, while ignoring others. Sampling bias is the result of the implementation of various randomization techniques. In order to minimize the impact of it, it is recommended to compensate unbalanced answers based on the original weight. This method is called mean of weights. The major drawback of this methods is that it increases the uncertainty of the results, due to the fact that various weight has been used for various answers [54, p41].

Coverage Error Sampling frame allows covering a part of the interested population. However, it could lead to the coverage error. This type of error can occur at the planning stage and can take either undercoverage or overcoverage form that are illustrated in the Figure 8 [54, p42]. Undercoverage is an outcome when some groups or clusters of the population were decided to be removed from the sample frame. On the other hand, overcoverage is a consequence when clusters that are above the target population are included in the research [54, p42].

Figure 8. Categories of coverage error

Coverage error could be divided into two subcategories: coverage bias and coverage variance. Coverage bias can occur when some subgroups of the target population were not included in the scope of the study. As a result, this particular situation could lead to the sampling undercoverage. In most of the cases, sampling bias happened due to inability to reach some groups. In order to avoid coverage bias, it is recommended to make acknowledgment about the missing group and present results only for observed ones. Another method to prevent the sampling bias is to include every single person in the population. However, there are two major drawbacks. First of all, the required budget for the survey will dramatically increase. The second limitation is that by increasing

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the population size, there is a high risk in the overcoverage of the sample frame [54, p42]. On the other hand, sampling variance is a result of the coverage error, when obtained results show some significant differences. Sampling variance is an illustration of the consequences brought by the overcoverage problem. Thus, in order to avoid this type of error, a meticulous sample planning is recommended. In particular, it is recommended to define separate samplings for various groups of population. Hence, separate conclusions can be drawn from each group, leading to the minimized variance [54, p43].

Nonresponse error One of the major limitations of the survey is that in most of time a significant number of individuals refuse to participate. As a result, this leads to the nonresponse error. The major threat of it is that the obtained result might not illustrate a complete picture of the population. In fact, nonresponse error could be subdivided into two categories. The first one is called item nonresponse. It occurs when participant agrees to participate in the study, but not providing his or her answer for each and every question. In contrast, the definition of the unit response is when individual have decided not to participate in the study at all [54, p43]. Similar to the coverage and sampling error, nonresponse error is divided into nonresponse bias and variance. Nonresponse bias could be observed when results obtained from participants differ from nonparticipants opinion [54, p43]. According to the AAPOR [54], nonresponse bias is calculated by multiplying the difference of the response and nonresponse means with the nonresponse rate. Figure 9 illustrates the nonresponse bias formula. Where Yr stands for the response mean, while Ym is a nonresponse mean. M represents the number of patricians. N is population, including both participants and nonparticipants.

Figure 9. Nonresponse bias

According to the nonresponse bias formula, bias could be minimized by increasing the response rate. However, Bautista tends to disagree with such opinion. The fact of having nonresponse rate does not particularly lead to the bias results. Instead of increasing the sampling size, it is suggested to implement weighting techniques for questions that were not answered. In other words, unit nonresponse. Weighting allows minimizing variations in the results. On the other hand, if participant refused to be involved in the study, in order to compensate the missing data, so-called model-based simulated values can help to fill the gap. However, it is not recommended to implement it due to the fact that it leads to the uncertainty of the results [p53, 44].

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Similar to the sample or coverage error, if the survey on the same term is going to be conducted with the similar population, there is a high chance to obtain different results that are called variations. However, in case of the nonresponse variations, the situation is different. In particular, there are possible variations in the results that could be obtained from nonparticipants over some period of time [53, p44]. The is no particular method that would allow to measure and even minimize the nonresponse variance. Thus, it is recommended to focus an attention towards techniques that would allow to increase the actual response rate. For instance, instead of interviewing participants, self-administrative questionnaire provides more freedom. Thus, individuals will be more willing to participate. However, there is a need in assuming that not all the participants are advanced with technology, for instance in case of online questionnaires. Leading to lower response rate from elderly generation. In that case, it would be beneficial to implement multiple data collection techniques for various population groups. For example, for the younger generation it would be suitable to fill the online form, while elderlies would find it more convenient to participate in the post survey [53, p44]. Moreover, by introducing potential participants with the topic and importance of the study, it will most likely increase the response rate. Another technique that would allow improving the response rate is a reward-based system. However, there are two major limitations. The first one is increased costs. The second one is that patricians in order to get the reward faster, might not be willing to spend a sufficient amount of time in order to provide proper answers. Thus, an overall quality of the results is decreasing [50, p44].

Measurement Error The objective of every single survey is to represent the population’s opinion on the given topic as precisely as possible. However, some answers require an understanding of the context in order to be able accurately to analyse the results. Thus, misinterpretation of questions or answers are leading to the measurement error. Similar to other types of errors that have been examined above, if the error occurs systematically it is called measurement bias, while the random errors are called measurement variance. Figure 10 represents four main factors that are leading to the measurement error. Even though each factor is illustrated in a septate box, all of them are affecting each over. Hence, the analysis is going to be focused on outlining dependencies between factors. In addition, possible trade-offs and their effect on the result of the survey has been examined as well [53, p45].

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Figure 10. Measurement Error

For example, if an interview was selected as the data collection method, it is most likely that open questions will be selected as well. An advantage is that open questions give more flexibility for the respondent. In addition, during the interview-based data collection method, in case of issues with understanding the question, respondent could ask to repeat and even paraphrase the question. As a result, respondent is able to give a more accurate answer that represents his or her opinion. Nonetheless, open type of questions could lead to the measurement error from the interviewer side, by misinterpretation of the collected answers. Moreover, by paraphrasing questions, an inexperienced interviewer could shift away from the original objective of the question. In addition, by paraphrasing the question, the interviewer could give own judgment of the topic. Thus, obtained result could be impacted by the measurement bias [53, p46]. On the other hand, for the pen and paper or web mode of data collection, it is applicable to select semi-structured questionnaires with the list, category or ranking type of questions. Semi-structured questionnaires provide a significant advantage for the interviewer by standardizing answers that could be easily analyzed based on the common patterns. On the other hand, semi-structured questionnaires limit respondent’s ability to provide a detailed response or an opinion on the given topic [53, p46]. Moreover, if the respondent doesn’t a have a clear position on the topic, that person is forced to select a position, leading to the measurement variance. An interesting observation has been done by Bautista that mode of the data collection could affect the answer. For instance, in the pen and paper or online forms, participants that do not have a strong position and tend to select the first answer. An opposite situation is with the face to face or telephone-based interviews, where participants have a tendency to select the last option. In order to avoid measurement error caused by the respondent, it is recommended to ask a paraphrased question with the same meaning multiple times [53, p47].

Conclusion Despite the fact that above-mentioned types of errors have a clear definition and it is well known what is triggering them, it is still challenging to estimate the overall TSE of the survey. The reason is that the questionnaire is in the most of the time is part of the qualitative research. That is why an error could lead

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only to a small fluctuation in the results, making it hard to find it on the individual level. Nonetheless, TSE is still extremely helpful for researchers, especially in the planning and designing stage, where the knowledge of various types of errors is helpful in both the questionnaire design and data collection planning.

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5 Survey Preparation

The objective of the given chapter was following. Describe the initial preparation phase of the survey, including sampling planning and questionnaire design. In addition, this chapter summarized the methodologies that are applicable to the given research. The motivation of the selection of the specific research and methodology was done both in the Section 2 and 4. Furthermore, details of survey location, time and population was discussed. Moreover, this chapter describes the pilot survey that was initiated in advance, to verify the understanding of the questionnaire and test data analysis techniques. At the end, observations and required modifications were presented.

5.1 Background The survey was selected as the research strategy for the given thesis, in order to meet research objective and to answer research questions. The objective of this research was following: define if there are market opportunities for the project ON BOARD in the Urban Area. In order to answer RQ1: “What are the patterns in the passenger’s online behavior in public transports?” and RQ2 “What does the potential customer’s profile look like?”. it was required to collect the data from the passengers. Based on the literature review in Chapter 2, it was decided to perform data collection using survey. The focus of the study was done on the qualitative results. Hence, it required the selection of methods of the survey design, sampling and analysis that are valid for the qualitative research.

5.2 Methodology Even though the selection of method and technique was discussed both in Chapter 1 and Chapter 2, the author believes in the importance of summarizing methods that were included in the survey. In Table 1 presented information about methodologies that have been used during the planning and analyzing processes.

Table 1. Survey Methodologies Research Method Qualitative

Research Approach Abductive Research Purpose Exploratory Research Strategy Survey Research Analysis Descriptive Statistical Method

Sampling Technique Non-Probability Quota-based

Type of Survey Cross sectional Self-Completed

Structured Questionnaire

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Questionnaire Variables Attribute Opinion

Behavioral

5.2.1 Survey Planning

Table 2 provides an overview of the date, time and location of the survey. In the interest of time, it was made decision to focus the research in the Stockholm Area only. In the initial plan, an intention was to cover both commuter trains and metro. However, during the planning process, it has been decided to narrow down the type of transport for survey only to commuter trains. The major limitation of conducting the survey in the Metro is the noise, leading to the high refusal rate. Regarding the selected dates, it was decided to choose the week 25 on purpose. The reason is following, on 24th of June in Sweden celebrated the Midsummer holiday. Thus, during week 25 there is a chance to interview participant both during the workdays and holiday, leading to broader overview of the given problem. The time selection between 10 am and 15 pm was done based on the reflection of the pilot survey that is described in Section 5.4. In short, it was observed when the number of passengers was increasing it lead to the higher refusal rate.

Table 2. Survey details Location Stockholm Area

Type of Transport Commuter trains Duration of the survey 6 days

Dates Week 25 18/06/2017-24/06/2017

Time 10am -15 pm Quota (per line) 50 people/day

2 days/line 100 people/line

Total number of participants 300 Population Heterogeneous Clustering Based on the age

Stratification Based on the line The duration of the survey was based on the established quota for sampling. Bombardier Transportation required to cover 300 participants. Based on that a quota was established, that was following 50 participants were interviewed per day. Each line had to be examined in two days, leading to 100 interviewed people per line. As it is illustrated in Table 3, the total number of examined lines were three. The selection of the population was heterogeneous, meaning no preferences were made to a particular group. The clustering of the results was done based on the age, in order to answer research question 2. The stratification

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was based on the line. Stratification was used in the planning process, especially in specifying the quota per line. However, stratification was not illustrated in the results, due to the fact the research objective was to provide results for the Stockholm area in general, not for a specific line.

Table 3 Commuter train lines in Stockholm area Line Stretch Travel

Time Length Stations

35 Bålsta- Stockholm C- Nynäshamn

1:44 107 km 27

36 Märsta – Stockholm C – Södertalje C

1:21 74 km 24

37 Södertalje C - Gnesta 0:31 30 km 6

It should be pointed out that the line 38 between (Tumba) – Älvsjö – Stockholm C – Arlanda C – Uppsala C was decided not to include in the survey. The reason is that line 38 has only four stations difference compare to the line 36. In addition, this line goes through the International Arlanda Airport. Thus, a significant number of the participant would be tourists. Meanwhile, the initial objective was to define patterns in the passengers’ online behavior that are using commuter trains on daily basis.

5.3 Questionnaire The core principle in the designing the questionnaire was an ability that after the result analysis to answer both research questions 1: “What are the patterns in the passenger’s online behavior in public transports?” and 2: “What does the potential customer’s profile look like?”. As it was discussed in Section 4.1, due to the fact that the research objective was to observe patterns in the data, it has been decided to use only pre-defined answers. The final design of the questionnaire is illustrated in Figure 11. The design of the questionnaire was based on the results and observation collected during the pilot, as discussed in Section 4.3. One of the major considerations in the design of the questionnaire was to minimize refusal rate. Based on the literature review in the Section 2.4.5, it was decided to limit the size of the questionnaire to one page. On the other hand, the space limitation should not have an impacted on the obtained the data relevant for the research objective. The questionnaire was dived into four logical sections. The motivation of including them in the survey described below. Additionally, the selection of the particular question variables and type of questions discussed.

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Figure 11. Survey

1.Pleasespecifyyouragegroup

2.Howmuchtimedoyouspendinpublictransportdaily:

5.Whatisyourmonthly mobileDataPackage:

Under13 13-17 18-25 26-34 35-54 55-64 65over

4.Areyouusingmobileinternetduringtraveltime:

TravelInformation

Lessthan40minBetween

40min– 1h

Between

1h-2hMorethan 2h

Yes Yes,alittle No

Less than1GB 1-5 GB 5-15 GB Morethan15GB

3.Whichtransportareyouusingondailybasis:

Bus Metro Tram Commuter trains Taxi CarSharing

MobileUsage

6.Duringthetraveltime,whatisthequalityofInternetconnectivity:

Awful Satisfactory Good

7.Whichservicesareyouusingduringthetraveltime?

(3=always,2=often,1=alittle,0=never)

Work(email)

News Social Media

Video Music Shopping Gaming

8.Beyondthetransport- whichsocialmediaareyouusingmost?

(3=always,2=often,1=alittle,0=never)

Facebook Instagram Twitter WhatsApp Snapchat

9.WouldyouusepublictransportmoreofteniftherewouldbeafreeWi-Fi

Yes,It will make public transport more

attractive

No, Free Wi-Fi would not effect my

choice

GeneralInformation

ThissurveyisapartofMaster’sthesisatKTHonthetopicon-boardWi-FiandTravelcompanionsinthe

PublicTransport.Allthecollecteddataisanonymousandconfidential.

Thankyouforyourcontribution!

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Disclaimer First and foremost, it is essential to clarify the disclaimer that is located on top of the questionnaire. As it described in the Section 2.4.8, according to the regulation of the 45CFR46 (Belmont Report a Federal Policy for the Protection of Human Subjects), participants have to be informed about voluntary participation in the study. Moreover, participants have a right to know the area and goals of the study. In addition, the researcher has to ensure data anonymity and confidentiality. As a result, based on the above-mentioned recommendation a disclaimer in the questionnaire was created. The idea behind it is to inform participants the institution and objective of the study. In addition, participants have been informed in a written manner about the data anonymity and confidentiality. General Information The idea behind of the section “General Information” was to answer research question 2: “How does the potential customer’s profile look like?”. It was decided to map the age group to the results from other sections, in particular, section three Mobile Usage. An original assumption was that various age groups are having different preferences both in services and social media. Question-related to the gender of the participant was decided not to be included in the survey, due to the fact that project ON: BOARD had no intentions to focus services only to the specific gender group. Meanwhile, a question about the occupation was considered as unethical. Question 1: “Please specify your age group”, was classified as an attribute question. The aim of it was to define the profile of respondents by fetching the result to the age group. Question 1 characterized as the category question, due to the fact that the selection of one answers was allowed. Based on the question 1 a population clustering was done. Travel Information This section was aimed at defining project’s further scalability by exploring other popular types of transports in the Stockholm area, as well an average usage of services per day. Both question 2: “How much time do you spend in public transport daily?” and 3: “Which transport are you using on daily basis?” were characterized as the behavioral variables, since the aim them to observe passengers’ regular actions. Question 2 was a category question. Meanwhile, question 3 was classified as a list question. Mobile Usage This section was logically divided into two sub-section. The first one was between question 4: “Are you using mobile Internet during travel time?” and 6: “During the travel time, what is the quality of the Internet connectivity?”. The objective of it was to identify passengers’ pain points regarding the mobile

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connectivity. For instance, low quality of data connection or selection of the smallest data package combined with a heavy Internet usage could potentially identify as a business opportunity. The focus both in questions 4 and 6 was done on the user experience research, rather than on obtaining empirical data. Questions 4 and 5: “What is your monthly data package?” were identified as the behavioral question variable. Meanwhile, question 6 was an opinion variable. All three questions were considered as the characteristic questions. The second sub-section was between the question 7: “Which services are you using during the travel time?” and 8:” Beyond the transport, which social media you are using the most”. The objective of it was to obtain results that would allow answering research question 1: What are the patterns in passengers’ online behavior during the travel time? Both questions were identified as the behavioral variable. In order to retriever insights accurately, it was decided to use ranking questions. The metrics are following: 3 is classified as always, 2 is often, 1 is considered as a little and 0 is never. By asking participants to rank each answer individually, it can potentially provide a broader view on their preferences. The evaluation of each answer was based on the average user’s grade. An importance of having question 7 was to identify customer’s interests and online habits of the services that could be potentially included in the offered services by Bombardier. Question 8 provided an information of how Bombardier could potentially reach their customers, in the other words, which social media channels were going to be used in the marketing campaign. The most effective method was to combine the information specific age group and their most preferred social media platform. Interest towards Wi-Fi Question 9: “Would you use public transport more often if there would be a free Wi-Fi?” was a part of the last sub-section. It is a reflection of the Transdev survey that has been examined in Chapter 2.2. The aim of it was to define the correlation between results that list question that is characterized as an opinion variable. However, it is important to note that results of question number 9 did not have effect on the project Connected Mobility Directly. In fact, author admits that question 9 was biased, due to the fact that passengers that were using only public transport, still might select a positive answer. Thus, the results might not represent a true opinion of the selected population. An advantage of the question 9 that Bombardier could use it as a selling point when the project would be discussed with potential partners.

5.4 Pilot In order to ensure the quality of collected data, it is critical to test the questionnaire in the real environment. To explore this consideration, a pilot survey was initiated. The objective of it was to verify if respondents were able to interpret questions in a proper manner. In a case of the sufficient number of

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respondents have experienced any difficulties with some questions, required modifications would be undertaken. In addition, pilot run was used an opportunity both to test one-way data analysis method and visual illustration of results.

5.4.1 Background

Table 4 provides a detailed information about the date, time and the line when the pilot survey was conducted. The type of transport selected Commuter Train, due to the fact that travel time is significantly higher than for instance in Metro. The noise level in the Metro cart is noticeably higher; making the interaction with respondent more challenging. The time period was selected on purpose between 13.00 and 15.00. Based on authors personal observation between 13.00 and 14.00 commuter trains were half-empty. Meanwhile, after 15.00, there was observed a significant increase in the number of passengers. The total number of respondent was 17. Reflections about the survey process were revealed in the Section 4.2.2.

Table 4. Pilot Survey Information about the line

Type of transport Commuter Train Destination Sodertalje–Stockholm

Stockholm - Sodertalje Average travel time (one way) 50 minutes

Information about the survey Date 2nd June 2017 Time 13.00 – 15.00

Duration 2 hours Number of respondents 17

Figure 1.4 illustrates the original version of the questionnaire that was tested during the pilot. Observations were discussed in the Section 4.3.2. Meanwhile, the decision about required modification presented in the Section 4.3.4.

5.4.2 Observations

First and foremost, questions 3, 7 and 8 required from the respondent to evaluate each box on a scale from 0 (never) up to 3 (always). However, 35 percent of participants only crossed answers that were applicable to them. The second consideration was the language barrier. According to the results published by European Commission [54], 89 percent of Swedish population are speaking English. Despite a high ratio of English speakers, 8 respondents refused to participate in the survey, due to the language barrier.

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As it was mentioned in the Section 4.2.1, the survey run in two sessions. The first one was between Sodertalje and Stockholm at 13.00. The second one was between Stockholm and Sodertalje at 14.00. During the second session, were observed a significantly higher number of passengers inside the train, compared to the first one. However, 76 percent of all data were collected during the first session. Nonetheless, the low response rate in the second round could be explained by the fact that people were travelling back home after the working hours. Apparently, due to the fatigue, people were less likely to be cooperative. Important to note that during the second session, people that agreed to participate were travelling either with a family member or a friend. In contrast, people who travelled alone and were surrounded by strangers were less cooperative. An interesting observation was made when a person was asked to participate in the survey, the first reaction was trying to establish an eye contact with a neighbor. After that, participants were refusing to be involved. Furthermore, people that were sitting close to the initial person, were immediately refusing to participate as well. This phenomenon was observed by Daley and Latane [56], if the number of people involved in a situation is increasing, on individual level each person would feel less responsible, due to the assumption that others will help.

5.4.3 Result

A pilot survey was an opportunity not only to test the survey process but the data analysis as well. The method that has been selected was to analyze the data based on one-way table method, leading to descriptive statistics. It was expected to define passengers profile based on the age group and online behavior. Two types of charts were plotted. Pie charts have been selected for the for list and category questions. Column charts have been plotted only for ranking questions. Figure 12 illustrates the respondent’s profile based on the age group. Despite the fact that both age groups 55-64 and under 13 were not involved in the survey, the results still could be called diverse. The largest portion of respondents was 34-54, with the response rate of 7 participants. The age group 18-25 had a slightly less response rate result, reaching 5 participants. Meanwhile, respondents that were having age range 26-34 and 65 and over had a similar result, 2 participants.

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Figure 12. Age Group

A bar chart represented in Figure 13 provides an overview about the average time that respondents were spending in the public transport, daily. Even though travel time between Sodertalje and Stockholm is 50 minutes only one way, the largest portion of the respondents marked that they spend less than 40 minutes in the public transport. It could be explained that either commuter trains were not their regular type of transport or respondents misunderstood the question, assuming travel time only one way. During the survey, only 5 participants stated that their regular travel between 1 and 2. Least number of respondents were either travelling more than 2 hours or between 40 minutes and 1 hour.

Figure 13 Travel time

The bar chart introduced the Figure 14 compares transport preferences of survey participants. It was required to evaluate each answer based on the metric: “3” is always, “2” often, “1” a little, and 0 stood for never. The result on

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the graph was based on the average number out of all participants. Despite the fact that it was required to evaluate each parameter, a majority of participants were just skipping options that were not applicable to them. Thus, a skipped option was considered as “0” never. Moreover, as it was mentioned in the Section 4.2.2, 39 percent of answered just by selecting options, not an evaluating. Selected answers have been considered as “3” always. Without a doubt, both commuter train and bus have been prioritized on top of other types of transport. Metro received a high evaluation as well. At the other end of the scale, trams, taxi and car sharing had a significantly lower preference.

Figure 14 Transport Usage

The Internet usage during the travel time is represented in the Figure 15. The largest portion of participant were using internet in the commuter train, with the 76 percent out of all participants. Around 18 percent are using Internet only a little bit. Only 6 percent were not using at all.

Figure 15 Internet usage

00,20,40,60,81

1,21,41,61,8

Bus Metro Tram CommuterTrain

Taxi CarSharing

AverageScore

TransportUsage

Yes76%

Yes,little18%

No

6% InternetUsage

Yes

Yes,little

No

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Based on the results presented in the Figure 16, the results for the data package was following. Almost half of all participants were having between 1 and 5 Gigabyte (GB) data package. The rest three options formed a quarter of responses.

Figure 16 Data Package

The user’s perception on the quality of mobile connection is illustrated in the Figure 17. 75 percent of all responses stated the quality of connection as the “satisfactory”. Meanwhile, both for “good” and “awful” reached 12 percent of responses.

Figure 17, Quality of mobile Connection

Figure 18 and 19 were the outcome of the questions 7 and 8 that were based on the evaluation metric, as in the question 3. Based on the average response

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Lessthan1GB Between1and5GB Between5-15GB Morethan15GB

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metric, bar charts were plotted. Both question 7 and 8 had as the last option to specify what other services passengers are using during the travel time. However, only one respondent marker that options in the question 7, without specifying the name of that service. The largest portion of answers was in favor of Social Media and Music services. Without a doubt, passengers prefer to spend travel time either working with emails or reading news as well. On the other hand, results indicated that passengers less likely are watching videos, going online shopping or spending time in playing with games.

Figure 18, Services Preference

Based on the results presented in Figure 18, it was critical for the research to explore in which social media platform people are spending time the most. According to the results that are shown in Figure 22, Facebook had scored the highest preference rating compare to other social media. Instagram had almost the half of the Facebook result. Meanwhile, the average score of 0.5 was applicable both for WhatsApp and Snapchat. Twitter has received the lowest usage rate.

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Work(email)

News SocialMedia

Video Music Shopping Gaming OtherSpecify

AverageScore

ServicesPreference

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Figure 19, Social Media Preferences

According to the Figure 20, 71 percent of respondent stated that free Wi-Fi in the public transport would not effect on the choice of preferred transportation method. On the other hand, only 29 percent agreed that free Wi-Fi would make public transport more attractive.

Figure 20 Free Wi-Fi in Public Transport

5.4.4 Conclusion

The data retrieved from the pilot allowed to form a general understanding of patterns in survey results. Based on that the assessment the main survey was done. In case of the significant deviations between pilot and main survey result, an additional survey round should be initiated.

0

0,5

1

1,5

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2,5

Facebook Instagram Twitter WhatsApp Snapchat OtherSepcify

AverageScore

SocialMediaPreferneces

Yes29%

No

71%

FreeWifiinpublictranpost

Yes No

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Based on observations from the pilot, results and considerations were following. First and foremost, people have experienced the misunderstanding of questions 3, 7 and 8. It is critical for the research to keep the evaluation metrics for those questions. Hence, questions itself were paraphrased. In addition, in the questions 7 and 8 it was made a decision to remove the option “Other, please specify”. Secondly, the language of the survey remained English. The third one and most important, was to revise the data collection strategy. It appeared to be so that least response rate was detected when the number of passengers have significantly increased. Moreover, the mobility of researcher was limited in the crowded environment. Thus, the recommended time for the survey is between 10 am and 13pm.

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6 Results

This chapter represents result of the main survey. The objective of it, based on the data analysis to answer question 1 and 2. One-way table data analysis method was used in order to answer research question 1: What are the patterns in the passenger’s online behavior in public transports? Cross-tabulation data analysis, in particular mapping results to a particular age group, was used to answer question 2: How does the potential customer’s profile look like? Based on the outcome, a conclusion was drawn that provides an answer to the research objective: are the any market opportunities for Bombardier’s project ON: BOARD in the Urban area. Last but not least, measurement errors have been discussed.

6.1 Patterns in the passengers’ online behavior The aim of this section was to answer the research question 1. Thus, collected data from the survey was analyzed based on the one-way table method. Results for each question was examined individually, in order to define patterns in the data. Data was visualized based on the recommendations from the Section 4.3.4

6.1.1 Age Group

The Age Group represents both the question 1 and the population clustering. The results of the survey are illustrated in Figure 21. According to it, the most involved age group in the survey was between 18-25 years old, with a total number of responses 102. Age groups 26-34 and 35-54 were illustrated almost similar response rate, with the 72 and 76 responses respectively. In contrast, only 25 people with an age between 55 and 64 showed their willingness to participate in the survey. People with age of between 13-17 and 65 and over showed the least participation in the study. Meanwhile, no participant with an age below 13 was detected. An impact of it was discussed in Section 5.3.

Figure 21. Age Group

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6.1.2 Travel Information

The objective of the section Travel Information was to define patterns in passengers’ preferences in transport and measure the average travel time per day. Figure 22 illustrates the results of the question 2 that were aimed at defining passengers travel time per day. The aim of the collected information was to define an average time of customers’ interaction with the provided services. According to the collected data, the results of the average travel time spent in public transport has been evenly disturbed. The largest variations in the results were between 40 minutes – 1 hours and more than 2 hours, with the differences just 9.6 percent out of all responses. It could be explained by the fact almost an equal response rate has been reached both in closest and furthest stations from the Stockholm central.

Figure 22 Average Travel Time

However, the major limitation of Figure 22 was that it illustrates results both for passengers that showed an interest towards the project and those who did not. In order to meet the objective of the question 2, a cross-tabulation data analysis was used in order to define the average travel of the passengers that showed an interest towards the project. Thus, results of the question 2 and question 9 were combined. Figure 26 shows the average travel time of passengers that were interested in the project. The largest differentiation in results was detected in the section less than 40 minutes and 40 minutes and 1 hour. Taking into account that an overall number of participants that shown the interest towards the project was 189, the major difference in the results was just 3 percent.

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Figure 23 Average Travel Time

When Figure 22 and Figure 23 were compared the lowest variation in results was observed in the group more than 2 hours. The difference in results compared to an overall number of participants was just 4%. Table 5 provides an overview of the differences in results for other groups as well. Based on the result the conclusion was following, passengers that are travelling more than 2 hours are the most who have shown the interest. Another observation that for other groups, the variation between the results were still considered as insignificant.

Table 5. Variations in results for the question 2 Less than

40 min 40 min- 1h 1h-2h More than 2

h Overall Results 44 50 49 46

Only who showed the

interest 73 88 80 59

Difference 29 38 31 13

Percentage 9.6 12.6 10.3 4.3 The objective of the question 3 was to define which transport passengers of commuter trains are using on the daily basis, including the commuter train. Based on the results, a possible extension of the project could be planned with regards to other types of public transport. As it mentioned in the Section 4.2, question 3 is a category question. The results of the question 3 are presented in the Figure 24.

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Figure 24. Daily Transport

Even though the survey was conducted in commuter trains only, the usage of the commuter train on the daily basis was stated only in the third place after the selection of the bus and metro. There are two factors that lead to such results. The first one could be explained by the fact of changes in users’ behavior during the holidays time. As it mentioned in Section 4.1, week 25 was selected on purpose in order to monitor the data both during the working days and holidays. Thus, the fact that during the week 25 in Sweden is celebrated the national holiday, this might trigger changes in the preferences of the transport selection. From another perspective, results could be effected by the fact that the questionnaire was designed only in English, due to author’s limited knowledge of Swedish. As it was observed in Section 4.3.2, the refusal rate of 47 percent out of all participants have been motivated by the lack of ability to speak and read in English. Even though the refusal rate, due to language limitations during the actual survey was significantly lower, there is still a possibility that participant was confused with the term Commuter train, due to the fact in Swedish it is called Pendeltåg. Thus, results were effected by the measurement bias. The effect of statistical errors of the survey was discussed in Section 5.3.

6.1.3 Mobile Usage

The aim of the section Mobile Usage was to observe patterns of the passengers’ mobile Internet connectivity. Participants were asked to state their mobile Internet usage during the travel time, their preferences in data package and evaluate the quality of the connection. Section Mobile Usage was considered as defining passengers’ pain points regarding the connectivity. Figure 25 illustrates results of the question 4 regarding the Mobile Internet Usage during the travel time. 77 percent of respondents stated that they were actively using Intent during the travel time. 19 percent acknowledged that they were using it a little. Meanwhile, only 4 percent of participated passengers

0

50

100

150

200

250


Taxi CarSharing

Num

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DailyTransport

67

revealed that there were not using the Internet at all. Hence, the conclusion is following, under the scope of observed population, the majority of respondents were actively using mobile Internet during the travel time.

Figure 25 Mobile Internet Usage

Figure 26 was plotted based on the results received from the question 5, where participants were asked to select their monthly mobile data package. Almost the half of all participants stated that their monthly data package was between 1 gigabyte (GB) and 5 GB. Significantly lower number of passenger (85) were chosen the package in a range of 5-15 GB. Meanwhile, only 49 participants indicated that their package is higher than 15 GB. Only 9 participants were having less than 1 GB data package.

Figure 26 Monthly Data Package

Yes77%

Yes,alittle19%

No

4% MobileInternetUsage

Yes Yes,alittle No

0

20

40

60

80

100

120

140

160

Lessthan1GB 1-5GB 5-15GB More15GB

Num

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MonthlyDataPackage

68

Combining results from the question 4 and 5, the results were following. Despite the fact that majority of 77 percent of participated passengers were actively using mobile Internet during the travel time, almost half of them have stated that their monthly data package range is only between 1 and 5 GB. The results could be explained by the fact that participants did not want to spend the significant budget on the connectivity. Further were discussed the selection of the data package on the preferences in services during the travel time. The results for the question 6 are represented in Figure 27. The objective was to define the quality of the connection from end users’ perspective. 56 percent of participants stated that the quality of the mobile connection was satisfactory. 27 percent of involved passengers were evaluating the quality of the connection as good. Meanwhile, from the perspective of the 17 percent of participants, the quality of the connection was awful. Considering the most popular selection of the data package, an assumption could be done that people were not willing to spend the higher budget on the connection due to the connectivity issues. However, this case has to be examined in details under the qualitative study, as it discusses in Section 6.1.

Figure 27. Quality of the Connection

Online Behavior As it was discussed in the Section 4.2 the objective of the question 7 and 8 was to define patterns in the passengers’ online behavior. Thus, based on the results research question 1 can be answered. Both questions required from participants to evaluate each answer based on the metric system, where 3 was the highest meaning always and 0 the minimum represented never. Based on the average grade calculated out of all responses, the popularity of specific services or social media has been monitored.

Awful17%

Satisfactory56%

Good27%

QualityoftheConnection


69

Figure 28 represents results of the question 7, where participants were asked to rank each service between 0 and 3. Some participants were marking only answers that were applicable to them, leaving others empty. Thus, an empty answer considered as 0. According to the results, the highest preference score was reached Social Media. Almost equally, with minor differences, passengers preferred both to read new and listen to music in commuter trains. Less preferable people were doing work related things and watching videos. Meanwhile, only insignificant number of participant acknowledged that sometimes they were doing shopping or playing games. Regarding the Social Media, question 8 provided a detailed overview of the passengers’ preferences in that area. An interesting observation was made with regard to the score of popularity of the video services. From author’s perspective, video services were received third lowest popularity score, due to the fact as it was illustrated in Figure 2.5, the most preferred data package has been selected in the range between 1 and 5GB. Thus, due to limited data package, passengers preferred to spend their time in other services.

Figure281. Preferred Services

Figure 29 illustrates results for the question 8. The aim of this question was to define which channels Bombardier should use to inform potential customers about their services. According to the results, passengers significantly prioritized Facebook as the social network that they were using the most during the travel time. Almost twice less were having Instagram, WhatsApp and Snapchat. Meanwhile, Twitter was defined as the least used. Important to note that preferences both in services and social media might differ between various age group. Thus, a defining customer profile has been done in Section 5.2.

0

0,5

1

1,5

2

2,5

Work(emails)

News SocialMedia

Video Music Shopping Gamimg

AverageScore

PreferedServices

70

Figure 29. Social Network preferences

6.1.4 Interest towards Wi-Fi

As it was mentioned in Section 4.2, question 9 was originally inspired by the Transdev study that has been discussed in Section 2.2. The aim of it was to define would passengers be more likely to use Public Transport if a free onboard Wi-Fi would be provided. In the other word, an overall interest towards the project. Even though question 9 was included in the survey, author had considerations about it that were discussed in Section 5.3. However, it was made a decision not to modify the question, in order to compare results between the study of Transdev and outcomes of the given research. Figure 30 illustrates the results of the question 9. The majority, in particular, 63 percent, stated that free Wi-Fi would make public transport more attractive. In contrast, in the study conducted by Transdev, only 42 percent of respondents have highlighted that free Wi-Fi would increase the attractiveness of public transport.

00,20,40,60,81

1,21,41,61,82


SocialNetworks

71

Figure 30 Interest towards onboard Wi-Fi

6.1.5 Recommendations

Based on results analyzed Section 5.1, patterns in the passengers’ online behavior were following. Majority of participants were actively using mobile data connection during the travel time. However, the most preferred data package was in a range between 1 and 5 GB. As a result, people were more likely using services that required lower data consumption such as social networks, news and music services. Moreover, people stated the quality of the connection as satisfactory. People with various travel time were interested in the connectivity features provided by Bombardier, especially passengers that were spending more than 2 hours per day in the transport. Regards planning the marketing campaign, it is highly recommended to consider Facebook as the initial point of interaction with customers. However, various age groups might have different preferences with regards to the social network. Thus, defining customers’ profile were examined in the Section 5.2. However, the topic of customer profile regards of the age group were examined in Section 5.2 Buses and Metro carts could be considered as the potential project extension, due to high popularity among commuter trains’ passengers. Overall, the majority of participants stated that free Wi-Fi would make public transport more attractive to them.

6.2 Customers’ Profile The aim of this section was based on the data analysis to answer research question 2: How does the potential customer’s profile looks like? The monitored population were clustered based on the age group. The selection of the age group as the only participant differentiation was motivated in Section 4.3. In addition, age group section was playing as the population clustering. The idea of the given section is following, observe each age group individually on the interest towards the project. In addition, define preferences in services and social networks. An importance of having this information was to adapt a

Yes63%

No

37%

IntresttowardsonboardWiFi

Yes No

72

portfolio of the services provided under the scope of the project ON: BOARD to particular groups that shown interest towards the project. Furthermore, data about preferences in social media of the specific age group would allow planning the marketing campaign more accurately. Cross-tabulation data analysis was used in order to link collected data from multiple questions to the specific age group. Under the scope of this section, only preferences in services, social networks and overall interest towards the project were covered. It should be pointed out that in this section were presented graphs for each question and each group individually. Instead of that, a table with a summary of results was presented. Graphical illustration of the output for each group could be found in the Appendix as it illustrated in the Table 6.

Table 6, Result of various Age Groups 13-17 18-25 26-34 35-54 55-64 65 and over

Appendix A

Appendix B

Appendix C

Appendix D

Appendix E

Appendix F

6.2.1 Results

Table 7 provides a complete overview of passengers’ profile individually for each age group. Without a doubt, all groups, except 65 and more, indicated that free onboard Wi-Fi would make public transport more attractive. An interesting pattern in mobile internet was observed for the age group 65 and more. Half of the participants stated that they were actively using mobile internet on the regular basis. Another observation was made in the preferred services. Surprisingly, passengers that are older than 65 years have stated that they were using both Facebook and Instagram on the regular basis. In results for passengers with an age between 13 and 64, no significant variations neither on preferences nor in opinion towards onboard Wi-Fi were detected. Only regarding preferences in Social Networks, passengers with an age between 13 and 25 have prioritized Snapchat on top of WhatsApp compare to other groups. Another observation was made towards passengers in the age range between 26 and 34, in particular preferences in services. This age group were the only one that has ranked highly that they were watching videos on regular basis during commuting time. By observing the selection of the data package observed age group were the only one where the majority were having data packages between 5 and 15 GB. This observation confirms an assumption made in the Section 5.1.5 that preferences in services depending on the monthly data package. Summing up the results, it can be concluded that even though the objective of this section was to define preferences of each customer group individually, it appeared to be so that preferences among various age groups were almost

73

identical. Thus, recommendations were applicable to all groups and similar to the one that has been discussed in the Section 5.1.5.

Table 7. Passenger’s Profile

13-17 18-25 26-34 35-54 55-64 65 and more

Services -Social networks

-Music -News

-Social networks

-Music -News

-Social networks

-Music -Video

-Social networks

-Music -News

-Social networks

-Music -News

-News -Work

Social Networks

Facebook Snapchat Instagram

Facebook WhatsApp Instagram




Facebook

Instagram Interest towards

Wi-Fi

YES

YES

YES

YES

YES

NO

6.3 Survey Errors In this Section survey errors that occurred during this study were discussed. The structure of this Section is following: first it was discussed errors that have been avoided or the impact of it have been minimized. Hereupon, errors that had a significant impact on the study were discussed. Terminology is based on the literature review done in the Section 2.5.9. First and foremost, the selection of the pre-defined questionnaire allowed to ensure already on the survey planning step to eliminate the chance of having either specification or processing error. On the major considerations in the design of the questionnaire was to link it to the research objective. Moreover, pre-defined answers helped to eliminate processing error, by standardizing the data analysis and interpretation. Furthermore, structured questionnaire allowed to eliminate measurement bias by avoiding the paraphrasing of the questions by the interviewer. Nevertheless, the results of the given research were partly affected by the bellow listed survey errors. Firstly, due to the time and budget limitations, the non-probability sampling technique was selected. As a result, there was no clear confidence level of the obtained results. In addition, in case if the study would be initiated again, there is a high risk of either sample variance or bias. In the other words, results obtained from the latest survey either randomly or systematically differs from the original one. Moreover, as it was mentioned in Section 4.3, author faced a high refusal rate, especially when the number of passengers was gradually increasing. The effect of refusal rate was higher than it was expected on the survey planning stage.

74

The initial plan of conducting the survey at different times of the day was decided to abandon in the favor of unified approach. Last but not least, as it illustrated in Table 8 some age groups experienced Undercoverage errors. In particular, age groups 13-17 and 65 and more formed only 8 percent out of participants combined. Meanwhile, age group under 13 were not covered at all.

Table 8. Number of Participants Under

13 13-17 18-25 26-34 35-54 55-64 65 and

over 0 12 102 72 76 25 13

75

7 Conclusion

Upon completion of the comprehensive research, it is possible to conclude that passengers in the Urban area, in particular, commuter trains, were highly interested in the connectivity functionalities provided under the scope of project ON: BOARD. The obtained data indicated that the majority of interviewed passengers were actively using mobile Internet during commuting on the regular basis. However, almost half of respondents stated that they were using monthly data in the range between 1 and 5 GB. As a result, it affected the service preferences, towards low data consuming ones. Moreover, 75 percent of participated passengers evaluated the quality of the connection as satisfactory. The answers for research questions are following:

• RQ1. What are the patterns in the passenger’s online behavior in public transports?

Passengers’ online behavior were following. Majority of participants were actively using mobile data connection during commute time. However, the most preferred data package was in a range between 1 and 5 GB. As a result, people were more likely using services that required lower data consumption such as social networks, news and music services.

• RQ2. What does the potential customer’s profile look like? Without a doubt, all groups, except 65 and more, indicated that free onboard Wi-Fi would make public transport more attractive. Potential customer were actively spending time in social media (Facebook, Instagram, WhatsApp), listing music and reading magazines during the commute time.

The answer for questions of interest:

Q1. What is the communication standard that would allow meeting the requirements from the application layer of the project? LTE network would allow to meet the requirements of the application layer of the project

Q2. What is the network deployment strategy recommended to implement in order to meet the strategical part of the project? The recommended deployment strategy is to play a role of MVNO.

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Author’s conclusion is the following: Bombardier is definitely recommended to enter Urban Transport as the part of the market expansion. Urban Transport is appealing by the fact that the project could be integrated not only in the commuter trains but also in the metro cars and busses. Hence, reaching almost a complete coverage within the public transport, leading to the significantly higher user base. Awareness about the project among customers is recommended to increase using Facebook and Instagram.

7.1 Future Work The next stage of the research will be focused on exploring customers’ demands and expectations from the project. Hence, a qualitative research should be initiated. The research will consist of two major stages. The first one is the survey that will be designed using open-ended questions, in order to obtain passengers’ expectations. Based on the results, a workshop is going to be conducted with the various focus groups on the topic of their daily frustration, demands and wishes during the commuting time. Based on the output, project ON: BOARD would be adapted towards the Urban Area transport.

77

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1

Appendix A

Figure 31. Age Group 13-17, Daily Travel Time

Figure 32. Age Group 13-17, Preferred Transport

0

1

2

3

4

5

6


DailyTravelTime

0

2

4

6

8

10

12

14


Taxi CarSharing

PreferedTransport

2

Figure 33. Age Group 13-17, Mobile Internet Usage

Figure 34. Age Group 13-17, Monthly Data Package

83%

17%

0%

MobileInternetUsage

Yes Yes,alittle No

0

1

2

3

4

5

6

7


Num

bero

fRespo

nses

MonthlyDataPackage

3

Figure 35. Age Group 13-17. Quality of Connection

Figure 36. Age Group 13-17. Selection of Services

58%

42%

0%

QualityofConnection


0

0,5

1

1,5

2

2,5

3

Work News SocialMedia


AverageScore

TypesofSerivces

4

Figure 37. Age Group 13-17. Social Network Preferences

Figure 38. Age Group 13-17. Interest towards onboard Wi-Fi

0

0,5

1

1,5

2

2,5

3


AverageScore

SocialNetworks

83%

17%


Yes No

5

Appendix B

Figure 39. Age Group 18-25. Daily time in transport

Figure 40. Age Group 18-25. Transport Preferences

0

5

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35


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DailyTravelTime

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Taxi CarSharing

Num

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fRespo

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TransportPreferences

6

Figure 41. Age Group 18-25. Internet Usage during Travel Time

Figure 42. Age Group 18-25. Preferences in Data Package

92%

4% 4%

InternetUsage

Yes Yes,alittle No

0

10

20

30

40

50

60


Num

bero

fRespo

nses

MonthlyDataPackage

7


Figure 44. Age Group 18-25. Preferences in Services

16%

51%

33%

QualityofConnection


0

0,5

1

1,5

2

2,5

3



AverageScore

TypeofServices

8

Figure 45. Age Group 18-25. Preferences in Social Networks


0

0,5

1

1,5

2

2,5


AverageScore

SocialNetworks

68%

32%


Yes No

9

Appendix C

Figure 47. Age Group 26-34. Daily Travel Time

Figure 48. Age Group 26-34. Preferences in Transport

0

5

10

15

20

25


Num

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fRespo

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DailyTravelTime

0

10

20

30

40

50

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Taxi CarSharing

Num

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fRespo

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DailyTransport

10

Figure 49. Age Group 26-34. Mobile Internet Usage

Figure 50. Age Group 26-34. Preferences in Mobile Data Package

MobileInternetUsage

Yes Yes,alittle No

0

5

10

15

20

25

30

35


Num

bero

fRespo

nses

MonthlyDataPackage

11

Figure 51. Age Group 26-34. Quality of the Connection


QualityofConnection


0

0,5

1

1,5

2

2,5



AverageScore

PreferedServices

12



0

0,5

1

1,5

2

2,5


AverageScore

SocialNetworks


Yes No

13

Appendix D

Figure 55. Age Group 35-54. Average Daily Travel Time

Figure 56. Age Group 35-54. Preferences in Transport

0

5

10

15

20

25

30


Num

bero

fRespo

nses

DailyTravelTime

0

10

20

30

40

50

60


Taxi CarSharing

Num

bero

fRespo

nses

DailyTransport

14

Figure 57. Age Group 35-54. Mobile Internet Usage


68%

32%

0%

MobileInternetUsage

Yes Yes,alittle No

0

5

10

15

20

25

30

35

40

45


Num

bero

fRespo

nses

MonthlyDataPackage

15

Figure 59. Age Group 35-54. Quality of the Connection


17%

62%

21%

QualityofConnection


0

0,5

1

1,5

2

2,5



AverageScore

PreferedServices

16



0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6


AverageScore

SocialNetworks

53%

47%

IntresttowardonboardWiFi

Yes No

17

Appendix E

Figure 63. Age Group 55-64. Daily Travel Time

Figure 64. Age Group 55-64. Preferences in Daily Transport

0

1

2

3

4

5

6

7

8

9


Num

bero

fRespo

nses

DailyTravelTime

0

2

4

6

8

10

12

14

16


Taxi CarSharing

Num

bero

fRespo

nses

DailyTransport

18

Figure 65. Age Group 55-64. Internet Usage


68%

32%

0%

InternetUsage

Yes Yes,alittle No

0

2

4

6

8

10

12

14

16


Num

bero

fRespo

nses

MontlyDataPackage

19



12%

67%

21%

QualityofConnection


05101520253035404550



Num

bero

fRespo

nses

PreferedServices

20



0

5

10

15

20

25

30

35

40

45

50


Num

bero

fRespo

nses

SocialNetworks

52%

48%

IntresttowardsOnboardWi-Fi

Yes No

21

Appendix F

Figure 71. Age Group 65 and more. Daily travel time

Figure 72. Age Group 64 and over. Daily Transport

0

1

2

3

4

5

6


Num

bero

fRespo

nses

DailyTravelTime

0

2

4

6

8

10

12

14


Taxi CarSharing

Num

bero

fRespo

nses

DailyTransport

22

Figure 73. Age Group 64 and over. Mobile Internet Usage

Figure 74. Age Group 64 and over. Monthly Data Package

50%

17%

33%

MobileInternetUsage

Yes Yes,alittle No

0

1

2

3

4

5

6


Num

bero

fRespo

nses

MonthlyDataPackage

23

Figure 75. Age Group 64 and over. Quality of the Connection

Figure 76. Age Group 64 and over. Preferences in Services

0%

56%

44%

QualityofConnection


0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

Work News SocialMedia Video Music Shopping Gamimg

AverageScore

PreferedServices

24

Figure 77. Age Group 64 and over. Preferences in Social Networks

Figure 78. Age Group 64 and over. Interest towards onboard Wi-Fi

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6


AverageScore

SocialNetworks


Yes No

TRITA -EECS-EX-2018:91

www.kth.se

exploring passengers’ interest toward onboard connectivity

Documents