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DEPARTMENT OF GEOGRAPHY

FACULTY OF THE SOCIAL SCIENCES

UNIVERSITY OF IBADAN, IBADAN, NIGERIA.

Staff/Postgraduate Seminar

LOCATION BASED SERVICES

being Area Paper

by

ENARUVBE, GLORY OMOFAWHORAN

(76073)

SUPERVISOR: PROF. ‘BOLA AYENI

September 9, 2011.

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1.0 Introduction

Recent developments in Information and Communication Technologies (ICTs) have

brought about an array of applications. These have also changed inter-personal relationships

and interaction between people, places and how business services are conducted. This is more

so as the production and marketing of goods and services are now more information

dependent. The need to transmit information has accelerated due to a deregulated worldwide

market which has increased uncertainty and the competition among places for investment and

jobs. Also, economic activities are now conducted over ever larger distances resulting in the

need for deployment and use of telecommunications systems that has become indispensable

in the contemporary economic landscapes (Warf, 2006).

Townsend, (2007) describe the pervasive deployment of telecommunications

networks as one of the defining characteristics of the late 20th-century cities in the developed

world. These technologies provided versatile channels for public and private social, political,

and economic communications, and helped reshape the geography of many human activities.

Urban sprawl is one way urban form has responded to the new spatial freedoms allowed by

pervasive use of telecommunications.

The 21st century saw the deployment of digital telecommunications and computing

technologies resulting in much greater flexibility and sophistication when compared with the

earlier analogue systems. These technologies are capable of providing greater variety of

services tailored to users needs. Advances in mobile wireless communications, therefore,

have greatly expand the ability to communicate from a wider variety of urban locations. The

mobile phones have significantly revolutionized telecommunication and drastically affected

life style and interaction in modern societies. The voice capabilities of the mobile phones are

currently augmented with data capabilities of increasing speed. The small size mobile

terminals – mobile phones and PDAs – are being integrated and are evolving into smart

phones and communicators, which allows users to access Mobile Internet services and run

applications at any time and in any location (Virrantaus, et al, 2001).

In a recent report, (ITU, 2010) the International Telecommunications Union estimate

that the global number of mobile phone subscribers will be over five billion in 2010 while the

prices of information communication technology (ICT) services continues to fall. This

increasing number of mobile phone subscribers, coupled with their inherent high portability

and personal nature make the mobile phone a veritable tool for the provision of customized

business and social services. They are used for storing and accessing information at any time

wherever the users go. The continuous availability of the device and the emerging capability

of the terminals and/or the mobile network infrastructure to position the terminals on the

earth allow new types of spatio-temporal real-time services that are called Location-Based

Services (LBS). Scholars have defined LBS in diverse manners. Many of the definitions

depend on the perspective and the application to which the services are applied. However,

most of the definitions of LBS can be summarised as information technology (IT) services

that are provided with the current location of the user as a critical consideration and

accessible through a mobile network using a mobile device or the internet through a wireless

network. Many of the functionalities of location based services have been observed to be

extension of those of geographic information system functions. Indeed, as Magon and Shukla

(2001) put it, LBSs involve the integration of GIS, Internet, wireless communication, location

finding techniques and mobile devices. This technology has not only impacted spatial

interactive in space and time, but also access to information. It equally has tremendous

influence on how businesses are conducted.

Miller, (2005) asserts that spatio-temporal interactions are the main drivers of social

and economic systems. Hence, the movement of people, goods and services is an important

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aspect in the development process of any economy (Somuyiwa, 2010; Akomolafe, et al,

2009; Southworth & Peterson, 2000). The form, capacity as well as the efficiency of the

transport system affects the quality of life of individuals and groups in a society. It also

affects business and services that take place within and between regions in a country. The

pervasive deployment of Information and communication technology infrastructure has

forced most businesses to manage global supply chains (Nagarajan, et al, 2005) as a result of

a more open and competitive modern economy markets. In spite of this, they continue to

respond to customers request within tight time schedules. The availability of real-time

information such as vehicle position and traffic conditions becomes crucial. This information

is now available as a result of the rapid growth in ICTs which provide real-time information

at very low cost (Ichoua, Gendreau, & Potvin, 2007).

Transportation planning and management entails the capture, integration and analysis

of large data sets that relate to spatial features. Geographic information systems (GIS) are

tools for the capture, storage, integration, management, display and dissemination of spatial

data and information. GIS are therefore, able to address complex social, economic and

environmental issues and are also able to address issues related to transportation.

Most issues in transportation planning, analysis and management are however, not

only complex but are also ill-structured and lack clear cut solutions that can meet the needs of

all stakeholders. Decision support systems could therefore assist decision-makers in

interactively synthesising and analysing relevant data to create useful information and

provide a set of feasible alternative solution to semi-structured or ill-structured problems.

These systems, typically involve a large set of feasible alternatives and multiple, conflicting

and incommensurate evaluation criteria (Densham, 1991; Malczewski, 2006). Integration of

LBS and SDSS can therefore, be useful in addressing transport related issues.

This paper therefore, attempts a review of the literature on the integration of SDSS

and ICT application in modelling freight management. Apart from this introduction, the

remainder of the paper is divided into four: section 2 examines the concepts of location based

services in transport modelling; SDSS and ICTs and its application in transport and logistic

management is examined in section 3; section 4 is dedicated to concepts and theories of

transportation research; section 5 identifies knowledge gaps in the application of GIS-Based

SDSS and LBS technologies in addressing transportation problems while section 6 concludes

the paper.

2.0 Definition and issues of LBS.

2.1 Defining LBS

There seems to be no generally accepted definition of location based services in the

literature as scholars have defined it in various ways. Virrantaus, et al, (2001) define LBSs as

services accessible with mobile devices through the mobile network and utilizing the ability

to make use of the location of the terminals. LBS involve the ability to find the geographical

location of mobile device and then provide services based on this location information

(Liutkauskas, et al, 2004; Roongrasamee, et al, 2003; Open Geospatial Consortium (OGC),

2005). Spiekermann, (2004) define LBS as services that integrate mobile device’s location or

position with other attribute information so as to enhance the value of service provision to a

user.

LBSs, as Koeppel, (2000) puts it, are services or applications that extend spatial

information processing or Geographic information capabilities, to end users via the internet

and/or wireless network. OGC, (2005) also describe LBS as wireless-IP services that use

geographic information to serve a mobile user. Kupper, (2005) on the other hand refer to

Location-based Services as IT services for providing information that has been created,

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compiled, selected, or filtered taking into consideration the current locations of the users or

those of other persons or mobile objects. While Shiode, et al, (2004) see LBS as

geographically-oriented data and information services to users across mobile

telecommunications networks. These services can also be provided in conjunction with other

conventional telecommunication services like telephony and related value added features, for

example, to realize location-based routing of calls or location-based billing.

Reichenbacher, (2005) however, uses a broader term, geoservices, which he refers to

as services meeting any space-related needs. They integrate data and functionality or

processing and are directed towards a defined need. He asserts that the basic geoservices are

derived from fundamental GIS functions: positioning services rendering locations,

geographic search service for any geographic features, geocoding services obtaining

coordinates for relevant POIs or addresses, reverse geocoding service delivering geographic

features for specific coordinates, proximity services that find the nearest POI or POIs for a

position or address, routing services that calculate route and direction instructions, directory

and catalogue services and presentation services. Reichenbacher, (2005) also asserts that

geoservices are targeted at mobile users and are accessible with mobile devices through a

mobile network. These services utilize the ability to make use of the location of the terminals

to provide mobile users with information dependent on their current location. As he puts it,

part of that information may be communicated through maps.

The definitions of Location based services in the literature can be categorised into

three. First, Location base services as the provision of services that are based on the ability to

know the location or position of the mobile terminal (OGC, 2005; Liutkauskas, et al, 2004;

Spiekermann, 2004; Roongrasamee, et al, 2003;Virrantaus, et al, 2001), secondly, location

based services has been defined on the basis of service provisioning by the extension of

geographic information processing and through the internet or some form of wireless protocol

(Kupper, 2005; OGC, 2005; Shiode, et al, 2004; Koeppel, 2000). Thirdly, as services

customised to meet space-related needs of the user based on the present location of the user

(Reichenbacher, 2005).

These definitions seem to be in accordance with Steiniger, et al, (2006) who

illustrated LBS as services achievable through the integration of Web GIS, mobile GIS and

mobile internet technologies and spatial databases (Fig.1). As Steiniger, et al, (2006) have

observed, LBSs are created from New Information and Communication Technologies

(NICTS) such as the mobile telecommunication system and hand held devices, from Internet

and from Geographic Information Systems (GIS) with spatial databases (Shiode, et al, 2004).

Figure 1: LBS as integration of technologies (from Steiniger, et al, 2006)

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It follows that a common feature of all the definitions of LBS include the application

and/or integration of information communication technology, particularly mobile networks as

well as web-enabled GIS and geographically referenced location information in the provision

of services to mobile consumers. These technologies are also creating complex array of new

spatial patterns through which we view, interact and connect to the world.

2.2 LBS Applications

Typical applications of LBS identified by scholars include navigation services

(Rinner, 2008; Raper, et al, 2007); emergency response services (Erharuyi & Fairbairn, 2003;

Aloudat, 2007) as well as transport and tourism services (Rinner, 2008; Berger, Lahmann &

Lehner, 2003). Raper, et al, (2007) also state that other areas of LBS application including

location-based gaming, assistive technology and location-based health services, are more

recent development.

Additionally, Leung, Burcea & Jacobsen, (2003) have also identified tracking the

location of mobile callers, tracking and dispatching mobile resources, traffic coordination and

way-finding and location-aware advertising. Raper, et al, (2007) in a review of selected

literature on the applications of location-based services noted that LBS have become

influential in some areas of application in the recent past. They identified such areas as

mobile guides and intelligent transport systems (ITS) as areas that are more established while

others such as location-based gaming, assistive technology and location-based health services

are emerging areas.

Kim, (2002), Sarkar, (2007) and Worrall, (1991) note that a large percentage of public

and private decisions are related to some sort of spatial consideration, leaving only a few

areas that are not affected by geographical considerations. Kim, (2002) notes that recent

development in information and communication technologies (ICTs) generate and puts an

unprecedented amount of geographic information of all kinds at a user’s fingertips. He further

states that research needs of LBS are vast and these include applications in the following

areas;

1. Utilization of real-time data in spatio-temporal context in GIS

2. Development of spatio-temporal topology in GIS

3. Development of efficient means to handle large data set for LBS

4. Interoperability among contents providers and interface standardisation for

efficient request-response services

5. Efficient and cost-effective means to collect real-time traffic data

6. Development of alternative theories for utilising population data vis a vis

sample data in GIS

7. Development of heuristic solution algorithms for LBS

Kim, (2002) focused his argument on issues of LBS related to request for transport

routing and navigation. Using statistical notations, he was able to show various ways of

estimating route costs, link travel time and spatio-temporal link travel time. Kim, (2002)

showed route estimating costs in terms of purchasing and stopping costs, time cost, distance

related costs and went on to calculate total costs for shopping and routing. He achieved this

using a node-node adjacency matrix representation of the network. Estimating link travel

time, according to Kim, (2002) depends on whether or not real time traffic data including

volume and speed are available to service brokers or users. In estimating for link travel time

in the absence of real time traffic data, he suggested that three link travel time tables be made.

These include peak-hour link travel time table for weekdays, non peak-hour link travel time

table for weekdays and link travel time table for weekends. You & Kim, (2000) also

demonstrate estimating link travel time with real time traffic data.

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2.3 Classification of Location based services

Giaglis, et al, (2003) observe that one of the main enablers of LBS proliferation in

recent years was the 1999 mandate of the US Federal Communications Commission (FCC)

that, by October 2001, emergency services should be able to automatically position any

citizen dialling 911 to within 125 meters in two thirds of cases. The reasoning behind this

mandate is that people who are injured or in some other need do not necessarily know exactly

where they are and hence the emergency services should be able to locate them in an

automatic way so that help can be sent out to them. This has placed a legal obligation on

mobile networks to support location identification provision in their service portfolio. Given

this legal obligation, many network providers have seized the opportunity to design and

implement further mobile location services that will commercially exploit the ability to know

the exact geographical location of a mobile user.

Table 1: Major Classifications of Location Based Services

Though a commonly accepted classification framework for location based services is

lacking in the literature to date (Bauer et al, 2005), Wolfson (2002), however, states that

location based services can be divided into two categories. These are mobile resource

management (MRM) applications and location-aware context delivery services. Mobile

resource management applications, as Wolfson (2002) puts it, use location data combined

with route schedule to track and manage service personnel or transportation systems. A

number of these are commercial applications e.g. systems for mobile workforce management,

automatic vehicle location, fleet management, logistics, transportation management and

support systems. While location-aware context delivery services are services that use location

data to deliver customised information to a mobile user in order to increase relevancy.

Examples of location-aware context delivery services include; delivering accurate driving

directions and instant coupons to customers nearing a store.

Ververidis & Polyzos (2006); Giaglis, et al, (2003) and Bauer, et al, (2005) have

categorised Location based services into six major types (Table 1); tracking (people tracking

and object tracking), navigation (regular routing services and specialised routing services),

information services (interactive information services and regular information services),

communication services (private communication services and business communication

services), entertainment services and transaction services (location based advertising services

and location based billing services). The GSM Association (2003) and Third Generation

Partnership Project (2004) also noted tracking services, which include person tracking, fleet

management and asset management. These services require permanent tracking of the object

of interest in order to detect events that may occur around these objects at any given time.

Location Based Services can be broadly classified as reactive (pull) and proactive

(push) LBSs (Kupper, 2005; Kupper & Treu, 2005; Ververidis & Polyzos, 2006; Virrantaus,

et al, 2001). Reactive LBS is always explicitly activated by the user. The interaction between

LBS and user is roughly as follows: the user first invokes the service and establishes a

service session, either via a mobile device or a desktop PC. He then requests for certain

functions or information, whereupon the service gathers location data (either of himself or of

Types

Categories

Tracking Navigation Information

Services

Entertainment

Services

Communication

Services

Transaction

Services

Mobile resources

Management

√

√

Location-Aware

Content Delivery

√

√

√

√

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another target person), processes it, and returns the location-dependent result to the user, for

example, a list of nearby restaurants. This request/response cycle may be repeated several

times before the session is finally terminated. Thus, reactive LBS are characterized by a

synchronous interaction pattern between user and service.

Proactive LBSs, on the other hand, are automatically initialized as soon as a

predefined location event occurs, for example, if the user enters, approaches, or leaves a

certain point of interest or if he approaches, meets, or leaves another target. As an example,

consider an electronic tourist guide that notifies tourists via SMS as soon as they approach a

landmark. Thus, proactive services are not explicitly requested by the user, but the interaction

between them happens asynchronously.

In contrast to reactive LBSs, where the user is only located once, proactive LBSs

require to permanently track him in order to detect location events.

2.4 Important Components of Location based services

One of the most obvious and important ingredients behind LBS is positioning

recognized system. According to Kupper, (2005), positioning is the process to obtain the

spatial position of a target. Becker & Durr (2005) identified two basic types of coordinates.

These are geometric and symbolic coordinate formats. Geometric coordinates is as used by

GPS and refers to a point or geometric figure in a multi-dimensional space. The topological

properties of such a space allow the calculation of distances between locations and their

inclusion in other locations. Symbolic coordinates, on the other hand, do not provide any

reasoning about their spatial properties without any additional information. Such coordinates

are available via cell-IDs in cellular networks, such as GSM or wireless LAN, as well as via

other positioning technologies, e.g. radio frequency tags (RFIDs) or infrared (IR) beacons.

Giaglis, et al (2003) assert Location based applications and services are based on

underlying technological capabilities that enable the identification of the location of a mobile

device, thereby making the provision of location base services possible. LBSs utilize an array

of different technologies to provide individualized information to the end user. Four major

elements are required to transmit the highly specified information to the user: the location of

the mobile device, a communication system, GIS data, and a control centre. According to

Magon & Shukla, (2001), the obvious technology needed in providing LBS is getting to

know the location or the position, the geographic data of that location and an application to

process the position information along with the geographic data to provide Location Based

Service. So we could consider the ingredients needed for LBS as Location or positioning,

Geographic data, Control Centre and Communication System (figure 2).

These four required elements have been broadly categorised as enabling and

facilitating technologies (Johnson, 1998). Enabling technologies are the basic technologies

that allow for obtaining location information from a mobile user, while facilitating

technologies refer to complementary technologies that provide the contextual and/or

infrastructural environment within which LBS can be implemented in a value-added fashion.

Johnson, (1998) refers to enabling technologies as the basic technologies that allow for

obtaining location information from a mobile user, while the facilitating technologies are

complementary technologies that provide the contextual and/or infrastructural environment

within which LBSs can be implemented in a value added fashion.

The enabling technologies have further been broadly divided into mobile network

dependent technologies or cellular positioning and mobile network independent technologies

or satellite position (Giaglis, et al, 2003; Steiniger, et al, 2006; Kupper, 2005). Mobile

network dependent/cellular positioning technologies, also referred to as integrated

infrastructure approach (Kupper, 2005), depend on the ability of a mobile device to receive

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signal from a mobile network covering its area of presence. Such technologies perform better

in densely populated environments where network base stations are closer to each other.

Mobile network dependent location methods include cell identification (Cell-ID) method,

time of arrival (TOA) method, angle of arrival (AOA) method and observed time difference

and enhanced observed time difference (OTD and E-OTD) methods. On the contrary, mobile

network independent /satellite positioning technologies, the stand-alone approach (Kupper,

2005), can provide location information even in the absence of mobile network coverage.

Location methods using this approach are broadly divided into long-ranged and short-ranged

methods. GPS and A-GPS are examples of long-ranged positioning devices. While blue-

tooth, RFID and WLAN are short-ranged positioning devices.

A number of different enabling technologies exist, each with its inherent strengths and

weaknesses. The basic technology assessment criteria refer to coverage range, accuracy

support and application environment (Giaglis, et al, 2003; Jiang & Yao, 2006). This also

depends on a variety of factors that include visibility, line of sight to a base station, handset

location (i.e. indoor or outdoor), terrain, measurement environment (i.e. urban or rural areas),

among others (Gum & Proietti, 2002). A successful LBS technology must meet the position

accuracy requirements determined by the respective service, at the lowest possible cost and

with minimal impact on network and the equipment.

Geographic Data

Control

Centre

Communication

System

Cell ID

TOA

AOA

E-OTD

A-GPS

Transportation and Navigation

Location Based Information

Emergency Services

Other Services

Figure 2 Components of Location Based Services (Source: Magon & Shukla, 2001)

2. 5 Privacy concerns in Location based services Users of IT services are always exposed to the risk that their personal information and

data collected and processed during service usage may be misused by unauthorized parties or

by the service provider itself. The motivation behind this misuse is often to observe and

analyze the user’s behaviour, attitudes, and social situation in order to tailor special offers or

advertisements for him, but sometimes it may also be with criminal intentions (Kupper,

2005). Privacy invasion is considered a very important concern in Location based services.

Privacy protection in location-aware services, as Kaasinen (2003) asserts, is related to the

right to locate a person, use the location, store the location and forward the location. Both

privacy and security concerns could create resistance to LBS adoption (Warrior, et al, 2003;

Kupper, 2005; Zhou, 2011). Kupper (2005) states that LBS privacy concerns are particularly

sensitive as the target’s location information passes many different actors along the LBS

supply chain. Also, the target is passive in that it is automatically tracked by an LBS provider

and related actors during its everyday activities and is often not aware of this fact

permanently. Thirdly, the location information is often regarded as belonging to a category of

high-level information that is desired to be saved more than other personal information e.g.

address, gender and age. LBS privacy concerns may be particularly sensitive as services

allow colleagues, family members or others to have real-time information on the location of

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individuals. Positioning capability is, however, often used to increase security e.g. tracking of

vehicles carrying valuable cargo and tracking of children. In these applications, location

information provides a compelling value proposition. In both of these examples privacy and

security is only maintained if access to the location information is restricted to authorized

users. How location information will be managed when the positioning capability becomes

ubiquitous is, however, still uncertain (Tilson, et al, 2004). In spite of these concerns, it has

been shown that people are positive towards the location based services as long as they

perceive them to be useful (Barkhuus & Dey, 2003; Kaasinen, 2003).

3.0 SDSS and ICT applications in transport and logistic management

3.1 Spatial Decision Support Systems and Location Based Services

Spatial decision support systems (SDSS) are primarily designed to provide user with a

decision-making platform that enables the analysis of spatial information to be carried out in

a flexible manner (Densham, 1991). These systems can be viewed as spatial analogues of

decision support systems (DSS) developed for business applications.

Gorry & Morton, (1971) integrated Antony’s, (1965) categories of management

activity and Simon’s (1960) description of decision types in arriving at a definition for DSS.

Antony, (1965) described management activities as consisting of strategic planning,

management control and operational control. Simon, (1960) described decision problems as

existing on a continuum from programmed to non-programmed.

Gorry and Morton, (1971) combined Antony, (1965) and Simon, (1960) description of

decisions, and described decision problems as structured, unstructured and semi-structured,

rather than programmed and non-programmed. Similarly, Simon (1960) described the

decision-making process as consisting of three phrases: intelligence, design and choice.

Intelligence is used in the military sense to mean searching the environment for problems,

this implies the need to make a decision. Design involves the development of alternative

ways of solving the problem and choice consists of analysing the alternatives and choosing

one for implementation. Gorry and Morton (1971) define a DSS as a computer system that

dealt with a problem at least some stages of which was semi-structured or unstructured. A

computer system is developed to deal with the structure portion of a DSS problem, but the

judgement of the decision maker is brought to bear on the unstructured or semi-structure part,

hence, constituting a human-machine system.

Gorry and Morton (1971) argued that the characteristics of both information needs

and models differ in a DSS environment, as compared to most organisational information

systems that were in use at that time. Management information systems, such as billing, other

accounting systems, inventory control and the like, require current, accurate data that is

derived primarily from sources internal to the organisation. DSS applications, because many

are strategic in their orientation, tend to require data from outside the organisation, and this

data may be in the form of trends or estimates. The ill-defined nature of information needs in

DSS situations leads to the requirement for different kinds of databases than those for

operational environments. Relational databases and flexible query languages are needed.

Similarly, the ill-structured nature of the decision environment implied the need for flexible,

interactive modelling system, such as those of spreadsheet packages. A DSS as Sprague,

(1980) puts it, is a class of information system that draws on transaction processing systems

and interacts with the other parts of the overall information system to support the decision

making activities of managers and other knowledge workers in the organization.

SDSS are computer tools developed to assist decision-makers resolve complex, ill-

structured spatial problems and provide acceptable solution that addresses conflicting,

multiple spatial objectives. It provides a framework for integrating database management

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systems with analytical models, graphical interface and tabular report generation capabilities,

and expert knowledge of decision makers. SDSS, therefore address fundamental functional

and modelling issues. Rutledge et al, (2007) assert that a good SDSS will support different

decision making styles and adapt over time to the needs of the particular user through

interactive and iterative processes. An SDSS has the advantage over a non-spatial DSS by

being able to store and manipulate complex spatial data structures, conduct analyses within

the domain of spatial analysis, and provide spatially-explicit output (i.e. maps) and other

reporting tools. This provides a robust framework for exploring resource management issues

by highlighting potential limits to resource use (e.g., only so much land, water, energy, etc.)

and the consequences of different allocation schemes. As Densham (1991) asserts, many of

the complex spatial problems often faced by decision makers, have multiple, conflicting

objectives solution. To be acceptable, a solution needs to be able to reconcile these

conflicting objectives.

SDSS has been applied in various aspects of research in addressing real-world

management problems in the literature. The application areas range from agriculture,

healthcare provision, education, forestry (Chruch, et al, 2000; ), vehicle and fleet

management, mining industry, telecommunication, among many others.

3.2 SDSS and LBS in location analysis While SDSS are usually applied mainly at the planning stage of tasks, LBS is targeted

towards assisting mobile users in making decisions in time and space during the performance

of tasks. Hence, LBS aid spatial interaction by facilitating interaction without the need for a

physical location in space. Janelle & Gillespie (2004) describe this using four interrelated

concepts; time-space convergence, time-space compression, human extensibility and

trackability. This is also related to Hagerstrand, (1975a) fundamental conditions necessary for

any “precise theoretical research” as stated in the concept of time-geography.

Current LBS, however, are limited to non-compensatory filtering and selection

operations (Rinner, 2003; Raubal & Rinner, 2004). Integration of SDSS and LBS will

therefore, be useful in providing managers and users of spatially related services with guided

decision analysis method and processes that can assist in addressing user preferences and

alternatives within a time constraint and also provide the opportunity for possible subtasks

while boosting spatial interaction.

According to Keenan, (1996) the relationship between GIS and SDSS has been

described as one in which GIS are used as generators for specific SDSS. Apart from generic

functionalities, such as spatial database management and map display available in GIS, SDSS

also use specialized decision support tools such as multi-criteria decision making techniques

(Rinner, Raubal, & Spigel, 2005) and are therefore, often developed by the integration of

geographic information systems with appropriate expert knowledge, data and models

(Engelen, & White, 1997).

Location based services provide users of mobile devices with access to GIS

applications, which employ the user’s current location to answer specific spatial queries.

Similar to SDSS, LBS assist users with their spatial decision-making process during the

performance of tasks in space and time. However, SDSS are designed to assist managers and

decision-makers in resolving spatial problems that are usually characterised by multiple,

conflicting and incommensurate evaluation criteria. These problems are normally ill-

structured in nature.

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3.3 SDSS in Freight Management

Eom, et al, (1998) stated that the transportation industry has been dependent on

decision support systems in their routine operations such as in managing freight, scheduling

vehicles and personnel. The design and implementation of SDSS in transportation research

and applications is widespread in the literature (Borzacchiello, et al, 2008; Kai, 2005;

Arampatzis, et al, 2004; Frank, et al, 2000; Shen and Khoong, 1995). Thill, (2000) edited a

special compilation of geographic information systems in transportation research published

by Elsevier Science Limited. The volume comprises a collection of research work by various

scholars in the field of transportation. It provides an insight into the application of GIS in

transportation studies and shows how GIS has evolved in the field of transportation planning,

analysis and management. Goodchild (1992) notes that GIS has matured from a tool to an

aspect of information technology, and finally to a domain of scientific investigation called

Geographic Information Science. GIS is today regarded as an indispensible tool in

transportation science applications (Spring, 2004).

Because of the important role of transportation in socio-economic development,

scholars have focused a lot of attention on the transport problem analysis using various

perspectives. The range of transport research areas include transportation planning (Shen &

Khoong, 1995; Kai, 2005; Arampatzis, et al, 2004), freight and commodity distribution

modelling (Wisetjindawat, et al, 2006; Powell, Bouzaiene-Ayari & Simao, 2006), passenger

movement (Csiszá, 2003), among many others.

Datta, (2000) classified research in the area of application of operational research in

the transportation problems in developing countries, into three broad classes; planning and

evaluation, distribution; location and scheduling and routing. Datta, (2000) however

concluded that though much have been done in transportation research, not many of the

research have addressed the issues of transportation as it relates specifically to the developing

countries in terms of distribution, technology, infrastructure and management. Like Datta,

(2000), Borzacchiello, et al, (2008) and Goodchild, (2000), also classified research on GIS

and Transportation studies into three broad groups; data representation, analysis and

modelling and applications. Borzacchiello, et al, (2008) however adopted geodatabase,

geomapping and geomodelling framework in their classification of GIS in transportation

research.

Most spatial problems have conflicting objectives and can therefore be addressed

using decision support systems. Eom, et al, (1998) reported a review of decision support

system applications between 1988 and 1994. The review consists of a total of 271 (two

hundred and seventy-one) published applications. Eom, et al (1998) classified DSS

applications into two broad categories; corporate functional management fields; which

include applications in production and operation management. These applications account for

41% of all the published papers reviewed in the research, and other areas; which include

government, military, educational, hospital and healthcare, urban/community planning and

administration.

Frank, et al, (2000) developed a SDSS that attempts to resolve the conflict between

population at risk and efficiency concerns in transporting hazardous materials. The focus of

the research was on risk mitigation through route selection techniques. In achieving this

objective, however, Frank, et al (2000) has to minimize travel time while other criteria; total

population at risk, distance, accident and consequence are constrained. In the process, they

came up with a desktop application capable of handling realistic network data, while offering

12 | P a g e

sophisticated route generating heuristics. The approaches developed are also able to handle

data manipulation, data and solution visualisation, user interfaces as well as optimisation

heuristics.

Integrating technological approach in some aspects of supply chain management can

therefore improve the entire process and help save valuable time in transit. Miller, et al,

(1999) report a GIS-based decision support system for dynamic congestion modelling and

shortest path routing in time-critical logistics. The system predicts network flow at detailed

temporal resolutions and solves for the combined departure time and shortest path required

for a shipment to arrive at its destination by a given deadline. Yi-Hwa, et al, (2001) also

report a GIS-based decision support system for analysis of route choice in congested urban

road network.

In follows that GIS and SDSS is applicable and highly desirable in the planning and

implementation of supply chain management issues

3.4 Freight and Logistics Management in Nigeria

Freight transportation maintains core relations with urban areas since the city is

concomitantly a unit of production, distribution and consumption which requires strategies in

ensuring efficient freight movements (Rodrigue, Comtois, & Slack, 2009). A number of

studies have also been conducted in the area of freight and logistic management in Nigeria.

Apart from Somuyiwa & Adewoye, 2010; Akomolafe, et al, 2009, these have been limited to

the application of traditional quantitative and /or qualitative approach and did not investigate

the impacts of information and communication technologies (ICT) in ameliorating

transportation problems in Nigeria. Okoko (2008) carried out an analysis of the spatial pattern

of urban goods movement in Akure, Ondo state. He employed gravity and linear

programming modelling to obtain predictions on the spatial pattern of urban goods

movement, using time as the impedance factor. Somuyiwa & Dosunmu (2008) using

qualitative and quantitative approach, underscores the need for modal shift from the exclusive

use of road for freight transportation to other modes of transport so as to reduce traffic

congestion experienced in and around the Apapa port in Lagos. In conclusion, Somuyiwa &

Dosunmu (2008) advocate the need for the development of other modes of transport,

particularly, rail and inland water ways to facilitate freight transport between the port and all

the geo-political zones in the country. Fadare & Ayantoyinbo, (2010) also reported a study on

the impacts of road traffic congestion on freight movement in Lagos metropolis. The

identified the effects of traffic congestion on freight movement in the metropolis to include

longer travel time resulting in decrease in vehicle utilisation, decrease fuel efficiency, higher

cost of freight operation, shrink in market coverage and less reliable pick-up and delivery

times for truck operators.

Ogunsanya, (2002) argues that urban traffic congestion in Lagos metropolis is a

symptom of a malfunctioning urban traffic system which can be explained by poor traffic

management, route inadequacy, absence of traffic and transportation planning, upsurge in

urban transport demand, and human misuse of transport infrastructure. Ogunsanya, (2002)

also notes that roadside and on-road parking, roadside trading and total disregard for traffic

regulations by road users are significant human contribution to traffic problem. These factors

are prevalent in most developing countries. The urban traffic situation has substantial

negative impact on urban residents and business transactions. These include loss of time and

restricted accessibility to business and social opportunities in cities.

13 | P a g e

Somuyiwa, (2010) however, investigated the impact of freight flows on the city

logistics in a megacity of developing economy. The focus was on the economic cost of

inefficiency cause by the flow of freight in the metropolis rather than the effect of congestion

on freight (Fadare & Ayantoyinbo, 2010). Somuyiwa, (2010) concludes that there is need for

a more rigorous planning of urban resources in order to minimise congestion caused by

freight movement across the city.

Akomolafe, et al, (2009) demonstrate the use of geospatial technology in enhancing

road monitoring and safety. They generated digital map of major highway in Nigeria to assist

traffic personnel in road safety and monitoring activities. Somuyiwa and Adewoye (2010),

adopting a descriptive approach, however, provided a theoretical background necessary for

effective logistic information system in an industrial outfit.

4.0 Geographical Themes in Location Based Services Studies

4.1 Time-Geography

Time-geography, initially proposed by Hagerstrand (1970), was developed to study

the relationships between human activities and various constraints in space-time context. It

provides a framework for investigating spatial interaction in space and time with time

constraints and assumes that an individual’s activities are limited by various constraints.

Time geography addresses the question of how participating in activity at a given place and

time affects abilities to participate in activities at other places and times (Miller, 2005; Kwan,

2004). Transport and information and communication technologies facilitate activity

participation by improving the efficiency of trading time for space in interaction. Janelle &

Gillespie, (2004) refer to this process as space-time-adjusting processes. As Yu & Shaw,

(2007) put it, Hagerstrand and his colleagues argue that time should not be considered only as

an external factor when we examine human activities. Time, as essential as space, should be

included explicitly in the process of examining human interaction. Treating time as a term

equal to space, the framework adopts a three-dimensional orthogonal coordinate system, with

time as the third dimension added to a two-dimensional spatial plane. The space dimension is

used to measure locational changes of objects, while the time dimension is used to order the

sequence of events and to synchronise human activities.

Hagerstrand’s (1970) formulation of time-geography had three central principles; viz,

that human life is temporally and spatially ordered; that human life has both a physical and

social dimension and that the activities constituting human life are limited by certain

temporal and spatial constraints that condition various individual and group-based

combinations of possible activities. Hagerstrand, (1975a) identified eight fundamental

conditions necessary for any “precise theoretical research”. These conditions are;

i. the indivisibility of the human being

ii. the limited length of each human life

iii. the limited ability of human beings to take part in more than one task at a time

iv. the fact that every task has a duration

v. the fact that movement between points in space consumes time

vi. the limited packing capacity of space

vii. spatial units of any scale must have a limited outer size

viii. the fact that every situation is inevitably rooted in past situations

Time-geography also implies that an individual’s activities in space and time are

conditioned by three types of constraints; capacity constraints, coupling constraints and

14 | P a g e

authority constraints (Hagerstrand, 1970; Guys, 1998). Capacity constraint limits the

activities of the individual through both his own biological make up and also the capacity of

the tools he can command. Coupling constraint arise because it is necessary that individuals,

tools and materials are bound together at given places and at given time. And authority

constraint refers to limitations and control of access. These occur at different levels to

produce hierarchies of accessibility.

Miller, (1999) identified the constraints-oriented approach, attraction-accessibility

measures and benefit measures as three complementary perspectives of measuring

accessibility with respect to rigour. As he puts it, while a constraints-oriented approach treats

each opportunity as equal without distinguishing differences among attractiveness and travel

costs, attraction-accessibility and benefit measures generally do not consider temporal

constraints or the time available for activity participation at locations.

Janelle & Gillespie, (2004) opine that four interrelated time-space concepts; time-

space convergence, time-space compression, human extensibility and trackability, are

fundamental to understanding the coupling between space-time-adjusting technologies and

the processes that eventually shape altered states of regional and community organization.

They stated further that these processes permit the restructuring of human interactions at all

geographic scales and may subvert the usual constraints imposed by distance, spatial

contiguity and temporal continuity.

Time-space convergence as Janelle & Gillespie, (2004) put it is the rate at which

travel time between places declines in response to transport and communication innovation

and investment. Time-space compression on the other hand, implies the accelerating

throughput of events in daily individual life. This leads to intensified pace of existence

whereby people are able to overcome time-space constraints through technology as

information is made available anywhere at any time. The concept of human extensibility

describes how individuals and institutions are able to project their presence and ideas beyond

their immediate locales using technology.

Janelle & Gillespie, (2004) identified two dimensions of the concept of human

extensibility. First is the fact that it present both opportunities and threats simultaneously and

secondly, it afford the individual the opportunity to maintain a multitude of personal

networks almost at will, regardless of their specific location at any point in time.

Trackability has enabled the individual to maintain mobility and connectedness with

others, coupled with flexibility of activity in time and space. This connectedness is facilitated

by such technologies as automobiles, cell phone, personal digital assistance and computers.

These tools also provide a means for tracking in details an individual’s spatial and temporal

interactions.

The concepts advanced by Janelle & Gillespie, (2004) have implications in transport

analysis and management. They can assist in real-time transport monitoring, control and

management.

Two entities are crucial in time geography analysis; these are the space-time path and

the space-time prism (Miller, 2005). The space-time path traces the movement of an object in

space and time. Space-time path tends to convey information about an individual’s activity

space. Ellegard, (1999) argue that everything done in time-geography, which includes “doing

nothing”, is regarded as an activity. This implies that every point in the space-time path is

associated with an activity. More than one activity can equally take place simultaneously

(Shaw & Yu, 2008). Kwan & Lee (2003); Kwan, (2004) demonstrate the use of GIS-based

geo-visualization methods in the description and analysis of human activity patterns in space-

time. In the process, Kwan, (2004) highlighted recent developments in GIS-based geo-

computation and 3-D geo-visualization methods. The space-time path shows the constraints

imposed by activities that occur in specific space and time and the need to consume time

15 | P a g e

when moving between activities. Shaw & Yu, (2008) also demonstrated the ability of space

time GIS for organizing complex activity and interaction data as spatio

an integrated space-time environment. They presented a time

which was the representation of individual activities and interactions as spatio

processes in a hybrid physical-virtual space. Their design was based on a set of extended

time-geographic concepts aimed at accommodating human

communication technologies.

Figure 3: Space-time path and space-time prism. (Adopted from Yu, 2006)

Space-time prism measures the ability to reach locations in space and time given the

location and duration of fixed activities. Space

orthogonal space-time coordinate system formed by the possible locations that an individual

can travel within a given time frame. When it is projected onto a two

result is a potential path area (Yu, 2006; Miller, 2005a)

Space-time stations are locations containing resources required for activities such as

eating, shopping, sleeping, work, etc. if the path is vertical, the person is conducting a

stationary activity. The person

relatively shallow slope indicates that less time is required per unit space when moving. This

implies a more efficient transportation system, which is the goal of most societies.

4.2 Spatial interaction Model

Spatial interaction models are useful in predicting spatial choices reflected in flows of

passengers, freight or information between an origin and a destination. These models are

expressed as transport demand/supply relationships over a geogr

interaction covers a wide variety of movements such as journey to work, migrations, the

market areas of retailing activities, international trade and freight distribution.

The assumption of spatial interaction models is that flows a

attributes of the locations of origins, the attributes of the locations of destination and the

friction of distance between the concenred origins and destinations. The general formular of

the spatial interaction model is;

Where:

Tij is interaction between location i (origin) and location j(destination). Its units of

measurement are varied and can involve people, tons of freight, traffic volume, etc. it

also has to do with time periods e.g. weekly, monthly or yearly.

when moving between activities. Shaw & Yu, (2008) also demonstrated the ability of space

time GIS for organizing complex activity and interaction data as spatio-temporal processes in

time environment. They presented a time-space GIS design, the focus of

which was the representation of individual activities and interactions as spatio

virtual space. Their design was based on a set of extended

geographic concepts aimed at accommodating human activities through information

time prism. (Adopted from Yu, 2006)

time prism measures the ability to reach locations in space and time given the

activities. Space-time prism has also been described as the

time coordinate system formed by the possible locations that an individual

can travel within a given time frame. When it is projected onto a two-dimensional space, the

potential path area (Yu, 2006; Miller, 2005a)

time stations are locations containing resources required for activities such as


is moving among stations, if the path is not vertical. A



action Model



expressed as transport demand/supply relationships over a geographical space. Spatial


market areas of retailing activities, international trade and freight distribution.

The assumption of spatial interaction models is that flows are functions of the



�� ,�, ��

is interaction between location i (origin) and location j(destination). Its units of


also has to do with time periods e.g. weekly, monthly or yearly.

when moving between activities. Shaw & Yu, (2008) also demonstrated the ability of space-

temporal processes in

S design, the focus of

which was the representation of individual activities and interactions as spatio-temporal

virtual space. Their design was based on a set of extended

activities through information

time prism measures the ability to reach locations in space and time given the

time prism has also been described as the

time coordinate system formed by the possible locations that an individual

dimensional space, the

time stations are locations containing resources required for activities such as


is moving among stations, if the path is not vertical. A





aphical space. Spatial


re functions of the



is interaction between location i (origin) and location j(destination). Its units of


16 | P a g e

Vi is attributes of the location of origin i. variables often used to express these

attributes are socio-economic in nature e.g population, number of jobs available,

industrial output or gross demestic product.

Wj is attributes of the location of destination j. it also uses soco-economic units of

measurement.

Sij is attributes of seperation between the location of origin i and the location of

destination j. this is also known as friction of distance. Distance, transport costs or

travel time are variables often used in expressing these attributes.

4.2.1 Gravity and Entropy Maximization

Gravity model, based on Newton’s theory of gravity, was one of the first attempts at

addressing movements and flows across space. It computes the number of trips between

origin i and destination j (Tij) as a simple function of the sizes of the origin and destination

(Pi and Pj), and the distance between them (dij) using a scaling factor k.

��

��

This formulation then gave way to a more general one that recognizes that the

relationships embedded in the equation above may vary across trips and within the

socioeconomic attributes of zones. Though the gravity model has been described as providing

reasonably accurate estimates of spatial flows, it does not possess any theoretical background

in individual travel behaviour.

Entropy maximization models represent improvement over the gravity model. These

set of models developed by Wilson (1974) resulted in a family of spatial interaction models

such as the production-constraint model, attraction-constraint model and the doubly-

constraint model. The models identify constrainst at the origin and destination zones giving

rise to such concepts as origin constraints models and destination constrained models. They

are referred to as single constrained models. Ayeni, (2000) notes that it is possible to derive

other models by accounting for additional constraints. Entropy maximization model derives

its name from the concept of entropy in thermodynamics or statistical physics and maximizes

a function that is similar to the entropy function in physics. It also owes its current

formulation to information theory which is an off shoot of the broad area of communications

engineering.

4.2.2 Urban Transport Models

The transport problem is prevalent in urban areas, particularly in developing

countries. Traffic congestion, inadequate and often, poorly maintained vehicles and roads are

some ways this problem manifest in cities. Miller, et al, (1999) categorised causes of traffic

congestion into immediate and long-term structural causes. They noted that immediate causes

of urban traffic congestion include; rapid population and job growth in metropolitan areas,

more intensive use of automobiles, failure to build new roads and failure to make drivers bear

the full cost of driving. While long-term structural causes include a desire for low-density

neighbourhoods, firms’ preference for low-density workplaces and travellers’ desire for

private vehicles.

Transportation networks, as Fisher (2003) noted, are flow networks. They are

characteristed by their topology and flow attributes e.g. capacity constraints, path choice and

17 | P a g e

link cost functions. These networks, as Bell & Lida, (1997) state represent the movement of

people, vehicles or goods. Transportation network models have therefore been described as

special types of network problems (Imam, Elsharawy, Gomah, & Samy, 2009).

Fisher,(2003) states that data modelling involves three levels of abstration;

conceptual, logical and physical levels. Transport networks have been widely modelled in the

literature, many of which focuses on transport demand modelling. Gentile & Vigo, (2007)

note that in the case of freight shipment in urban areas, demand generators are the

destinations e.g. shops, warehouses, etc while for passenger transport, the generators are the

origins of the trips. Wang & Cheng (2001) developed a spatio-temporal data model to support

activity-based transport demand modelling in a GIS environment. Kim & Chon, (2005) used

the binary logit model in modelling driver’s route diversion behaviour and real-time traffic

data. They observed that drivers prefer shorter routes in terms of travel time when

considering diversion. Their result also show that on-site information has significant

influence on route diversion behaviour, though non-site information from the media also

affect drivers’decisions. Crocco, et al, (2010) employed an integrated approach in modelling

freight and passengers demand for transportation services. They argue that by using an

integrated approach, aggregate movement could be minimized as goods and passengers travel

using the same vehicle. This, they believe will reduce external costs of mobility (pollution,

congetsion, and other negative externalities).

Puckett, (2009), highlights developments in the network modelling and empirical

freight travel behaviour applications. He noted that in other to improve our understanding of

the determinants of freight travel behaviour, there is need to employ a range of tools and

approaches since most aspects of these problems are inter-related in nature. Puckett, (2009)

advocates an expansion of both scale and scope of freight travel behaviour research that is

centered on policy impacts.

The influence of landuse on transport demand in urban areas is also evident in the

literature (Hunt & Simmonds, 1993; Sivakumar, 2007; Wilson, 1998). This has been

highlighted by Sivakumar, (2007) and Wegener, (2004) who posit that spatial development

determines the need for spatial interaction, or transport, but transport, by the accessibility it

provides, also determines spatial development. Wegener, (2004) states further, however, that

it is difficult to empirically isolate impacts of landuse on transport or vice versa as a result of

other factors that equally changes simultaneously.

The importance of the relationship that exists between landuse and transportation led

to the development of a series of models that are collectively refered to as transport/landuse

models (TLUM) or landuse-transport models (LU-T models). These models have, however

been criticised on the basis of a number of shortcomings e.g. excessive spatial aggregation,

excessive reliance on static equilibrium assumptions among others.

5.0 Research Gaps

The rapid evolution of information and communication technology and its impact on

business and social life cannot be overemphasised. ICT has revolutionised how businesses

are conducted as well as how people in society relate with one another. This impact is felt in

all spheres of human endeavour, transportation therefore, cannot be an exception.

Database is a critical consideration in the design and implementation of SDSS

(Armstrong & Densham, 1990). Similarly, the capture, modelling, maintenance and querying

of location information form a critical aspect of LBSs. The quality of the spatial data

18 | P a g e

employed in any spatial analysis will determine how good the results obtained from such

analysis will turn out to be. The data related to the location therefore, need to be capture in a

manner that enhances the efficiency of service provisioning and management. A number of

issues are inherent in the design of database for the purpose of location-based services. Some

of the more apparent issues in LBS literature include irregular dimension hierarchy,

imprecise location data, and partial containment between dimension values, among many

others. The database forms the foundation in LBS and SDSS applications, appropriate data

model must therefore be selected for application design objectives to be achieved. Apart from

Jensen, et al, (2001) and Jensen, et al, (2004) very little research has been conducted in the

area of database design and implementation as it relates to location-based services. It is

therefore important to further investigation database design issues related to SDSS and LBS

applications

The application of ICTs in transportation analysis and management is data-driven.

This may usually, require importing data from various sources for the creation of spatial

databases. There is no empirical evidence in the literature to show that attempts have been

made to focus on conducting research on specific methodologies for acquiring data relevant

for transport analysis in developing countries in general and Nigeria in particular. The need

for standard databases for managing transportation related issues cannot be over emphasized.

There is need, therefore to consider investigating spatial database design that is able to

specifically address transport management and modelling issues in developing countries.

Increasing urban traffic congestion has resulted in the need for vehicle routing in

urban environment. The vehicle routing problems are usually addressed using travel and

service times. Most of these models presented in the literature are static models assume pre-

defined travel and service times. These travel and services times are usually derived from

shortest path analysis on road networks with known values on each link in the network

(Giaglis, et al, 2004). Such assumptions are usually not realistic in urban areas where traffic

conditions are constantly changing during the day (Fleischmann, Gnutzmann, & Sandvob, 2004).

Technology can, however, be employed in ameliorating urban transport problem by

assisting business managers to predict areas liable to congestion at various periods of the day

and suggest alternative routes and hence improve supply chain and logistic management

while enhancing customer satisfaction. The goal of supply chain management is to

strategically manage the process of acquisition, movement and storage of materials, parts and

finished inventory through the organisation and its marketing channels in ways that current

and future profit is maximized through cost-efficient fulfilment of orders. The goal of an

efficient supply chain management strategy may not simple be to maximise profit, but in the

process, minimise average delivery and/or pick up time. It is therefore important to

investigate how ICT can assist in managing transport related issues in the urban environment.

As Sivakumar, (2007) noted, most of the transport demand models today treat freight

in an aggregate and ad-hoc manner. Therefore, only few studies have been conducted that

attempt to development behaviourally realistic model systems of freight demand that can be

integrated into landuse-transport models. This is as a result of inadequate awareness of the

importance of freight transportation, lack of appropriate data for modelling and complexity of

freight movements into, out of and within cities. More research is therefore required on the

behavioural aspects of freight transportation modelling.

Yu, (2006) states that human activities and spatial interaction largely takes place in

some physical space, with the physical presence or contact of the participants involved.

19 | P a g e

Today, however, with development in information and communication technologies, there

are significant changes in the way human interactions take place. Much of the interaction

today takes place in what has been referred to as virtual space (Adams, 1995; Miller, 2005;

Shaw & Yu, 2008; Yu, 2006;). The implications of virtual interaction pattern of individuals

and groups on travel and route choice behaviour will definitely provide an interesting insight

into our understanding of travel demand.

Though some work has been done on the use of information communication

technology in transportation analysis (Crainic, Gendreau, & Potvin, 2008; Powell,

Bouzaiene-Ayari & Simao, 2007; Wisetjindawat, et al, 2006), none has been reported that

specifically applied an integration of SDSS and LBS in addressing freight management

issues. It will be necessary to investigate how an integration of these technologies can assist

decision makers and field officials in making critical choices, particularly in situations where

time is a major constraint.

6.0 Conclusion

SDSS and LBS are ICT applications that are useful in addressing a number of spatial

problems. While SDSS are designed to assist decision-makers in addressing spatial problems

that are usually unstructured and have conflicting, incommensurate and multiple objective

criteria, LBS are services provided to mobile users taking into cognisance their present

location in space. An integration of these two technologies, therefore, will be useful in

addressing spatial problems that are of particular interest to the modern nomadic man as it

librates him from the constraint imposed by physical space by enabling him work and play in

virtual space.

This paper has therefore attempted to show, through a survey of literature, the

possibility for this type of system in applying SDSS and LBS in systems to address

challenges encountered by transport operators.

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