A Comparative Analysis of Journey Time fromGoogle Maps and Intelligent Transport System in

HongKongZhixiang He

Department of Computer ScienceCity University of Hong Kong

Hong Kongzhixiang.he@my.cityu.edu.hk

Chi-Yin ChowDepartment of Computer Science

City University of Hong KongHong Kong

chiychow@cityu.edu.hk

Jia-Dong ZhangDepartment of Computer Science

City University of Hong KongHong Kong

jzhang26@cityu.edu.hk

Abstract—In many cities, Intelligent Transport Systems (ITS)are mainly based on sensing technologies installed on roads tomonitor real-time traffic and predict journey time. Although theTransport Department in Hong Kong publishes the collectedtraffic data for the public to access, its ITS only covers somemajor roads, highways and tunnels. Recently, Web mappingservices (e.g., Google Maps and Microsoft Bing Maps) are newpopular sources for many location-based applications to accessreal-time traffic data, predicted journey time and other location-based services. In this paper, we conduct a comparative analysison the journey time data derived from Google Maps and the ITSin Hong Kong. We first describe the underlying technologies ofthese two sources, and then conduct experiments to compare thejourney time data derived from them for four route sets of a totalof 35 major routes during two weeks in Hong Kong. Experimentalresults indicate that the p-values of the journey time data fromthe two sources are consistent with each other for most routesthroughout the entire day; and the differences are acceptable.As a result, Google Maps provides high-quality real-time trafficdata in Hong Kong. Due to the high deployment cost and limitedcoverage of the ITS, Google Maps is the promising source forlocation-based applications to access real-time journey time data.

I. INTRODUCTION

Hong Kong’s road network is one of the most heavilyused in the world, a land area of 1,110 km2, a populationof about seven million, about 728,000 vehicles on 2,101 kmof roads [1]. In 2001, the Transport Department initiated anIntelligent Transport System (ITS) Strategy Review for HongKong which provided a blueprint for the development of ITSin Hong Kong to enhance the safety, efficiency, and reliabilityof the roads. Currently, the ITS in Hong Kong mainly includesfour applications:

1) Journey Time Indication System (JTIS) [2]. The JTISmonitors 22 routes, including nine routes from KowloonPeninsula (KL) to Hong Kong Island (HK) and 13 routesfrom HK to KL via the three cross-harbour tunnel-s (i.e., Cross Habour Tunnel (CH), Eastern HarbourCrossing (EH), Western Harbour Crossing (WH)). Fig. 1illustrates a journey time indicator which is located atWaterloo Road southbound near Kowloon Hospital in

Fig. 1: The journey time indicator at Waterloo Road south-bound near Kowloon Hospital in Kowloon Peninsula (KL).

KL. This journey time indicator shows the journey timeof three routes from that location to HK via EH, WHand CH [2].

2) Speed Map Panels (SMPs) [3]. The SMPs providethe journey time and traffic speed level of 13 majorroutes from New Territories (NT) to KL, with differentcolors for different speed levels. Fig. 2 depicts a SMP,which indicates the journey time (in minutes) from thatlocation to Kowloon via Tai Lam Tunnel and Yuen LongHighway [3].

3) Hong Kong eRouting) [4]. eRouting is a web portalthat provides driving routes, real-time traffic conditionsand parking information for pre-trip planning.

4) Hong Kong eTransport [5]. The Hong Kong eTransportis another web portal that shows the real-time trafficinformation including special traffic news, traffic speedson major roads to the three cross-harbour tunnels andmajor routes from NT to KL in Hong Kong.

The above mentioned applications require traffic data col-lected from physical sensing technologies (e.g., sensor-basedand video-based technologies), and hence, the major disadvan-tages of ITS are high deployment and maintenance costs, andlimited coverage about 14.26% (i.e., the ITS in Hong Kongcovers 35 major routes with about 300 kilometers (km) of

Fig. 2: The speed map panel (SMP) at San Tin Highwaysouthbound (near Pok Wai Road) in New Territories (NT).

2104 km of trafficable roads in Hong Kong) [6]).With the rapid development of Web technologies, Web

mapping services (WMSs) (e.g., Google Maps and MicrosoftBing Maps) become more and more popular in recent years.WMSs provide Application Programming Interfaces (APIs)for users to access mapping data, traffic data, and otherlocation-based services and developers to build their location-based applications. Since WMSs provide cost-effective andefficient sources for accessing traffic data, they have beenused in various areas, e.g., logistics and transport networksystems [7]–[9], network traffic monitoring [10], intelligenttourism system [11], and location-based services [9], [12],[13]. Recently, a comparative analysis has been conductedfor routes generated by different WMSs [14]. Furthermore,another comparative analysis has been made for the journeytime between WMSs and the ITS using loop detectors dataand electronic toll tag data on the New Jersey Turnpike [15];its results show that journey time measurements from physicalsensors and WMSs are highly correlated.

ITS with traditional traffic surveillance methods has severallimitations, including high setup and maintenance costs forboth hardware and software requirements and limited coverageof the road network. In contrast, Google Maps, one of themost popular WMSs, collects real-time traffic data from itslarge number of mobile users with GPS equipment and probevehicles [15]; also, it has a much larger coverage than tradi-tional methods. A few previous work studied for comparisonof section of highway between ITS and WMSs, unfortunately,there is no city-scale comparative analysis between them.Google Maps is studied in this work because other WMSs suchas Microsoft Bing Maps [16] do not provide real-time trafficinformation in Hong Kong. In the work, our main contributionscan be summaried as follows:

• We present a traffic data collecting approach using Webmapping services (e.g. Google Maps), considering theadvantages of these sources (e.g., large coverage, real-time).

• We conduct a city-scale comparative analysis betweenITS and WMSs. Specifically, we conduct a comparativeanalysis for the correlations of the real-time journeytime data of Google Maps and the ITS in Hong Kong,which will answer the question: “Could we use the real-time traffic data from Google Maps to model the trafficconditions in Hong Kong?”.

• This study can be also similarly applied for studyingreliability of traffic data collected from WMSs in othercites with ITS which provides real-time traffic data, e.g.,New Yor City.

This paper is organized as follows. Section II describes thebackground including traditional traffic surveillance (e.g., JTISand SMPs in Hong Kong) and WMSs, and highlights relatedwork. Section III presents obejct analysis including traffic dataextraction of the journey time from Google Maps and the ITSin Hong Kong (Section III-A) and epxeriments (Section IV).Finally, Section V concludes this paper and discusses ourfuture research directions.

II. BACKGROUND AND RELATED WORK

In this section, we introduce the traffic surveillance tech-nologies used in ITS and WMSs (Sections II-A and II-B), andthen highlight related work (Section II-C).

A. Traffic Surveillance Technologies in ITS

Traffic surveillance technologies used in ITS can be cate-gorized in two major approaches:• Stationary sensing technologies: sensors are deployed on

roads, for example, inductive loop detectors, road-sidedetectors such as autoscope video detection and automaticvehicle identification (AVI) systems; and

• Mobile sensing technologies: sensors are attached tovehicles, for example, probe vehicles and smart devices,also known as floating-car based technologies.

There are strengths and drawbacks in these two approaches.Although stationary sensing technologies could reach highaccurate measurement of journey time, they have not beenwidely installed in the road network due to the high de-ployment and maintenance costs. On the other hand, mobilesensing technologies with traffic data collected from mobiledevices offer high quality of traffic information with almostno additional costs and cover a much larger portion of theroad network, while more noise and missing values exist inthe data [17]. The Transport Department of Hong Kong takesthe stationary sensing technologies (using autoscope and AVI)to monitor the traffic conditions of the major roads in the roadnetwork of Hong Kong. Two systems, namely, JTIS [2] andSMPs [3], are used by the ITS of Hong Kong to measure thereal-time journey time of a total of 35 major routes. We willprovide more technical details about the JTIS and SMPs.

1) Journey Time Indication System (JTIS): In Hong Kong,the JTIS has provided the current traffic information in termsof journey time via displays on the major routes since 2003. Itis helpful for drivers to make a choice from various availableroutes. The journey time is updated every two minutes andis displayed on the corresponding journey time indicator, asillustrated in Fig. 1. There are two types of traffic detectorsused by the JTIS to collect real-time traffic information.(1) The first type is link speed detectors (e.g., AVI detectors)which use the automatic license plate recognition technologyand collect records at the toll-gates of tunnels and bridgesin Hong Kong. (2) The second type of traffic detectors is

Fig. 3: The 10 physical journey time indicators are installedon the major routes to display the latest journey time of eachcross-harbour tunnel.

spot speed detectors (e.g. autoscope) which use the videoimage processing technology with the autoscope detectorsinstalled on the major routes. The journey time estimation ofthe JTIS is the integration of three journey time estimationmethods [18], namely, estimation based on the autotoll tagdata [19], estimation based on the autoscope data, and theoff-line estimation [20]. The off-line estimation method isnecessary for roads without or with less real-time data. Amongthese three methods, the weights of the first and secondmethods are dependent on the sample sizes of the autotolltag and the autoscope data, respectively, whereas the lowestpriority is given to the off-line estimation method.

Specifically, there are 22 major routes are monitored bythe JTIS, including nine routes from HK to KL via the threecross-harbour tunnels and 13 routes from KL to HK throughthese three tunnels. The journey time of each of these routescan be retrieved online [21]. In addition to online access, 10sets of physical journey time indicators are installed on themajor routes, i.e., four sets in HK and six sets in KL, whichprovide the latest journey time of each cross-harbour tunnel,as depicted in Fig. 3.

2) Speed Map Panels (SMPs): Two types of detectors areused to collect the real-time traffic data for the SMPs, namely,link speed and spot speed detectors. Particularly, there are39 link speed detectors using the automatic license platerecognition technology, and 96 spot speed detectors usingthe video image processing technology. Link and spot speeddetectors are installed at the source and the destination ofeach link. For link speed detectors, the longest and shortestdistances between two consecutive detectors are 9.4 km and1.1 km, respectively. The distance of spot speed detectors isnot over 1.2 km.

SMPs are installed in NT at critical diversion points ofstrategic routes to KL to provide drivers with real-time trafficconditions and estimated journey time. The traffic conditionsare indicated by three congestion levels with different colors;red, amber and green colors indicate congested, slow and

Fig. 4: The five SMPs are installed in New Territories (NT).

smooth traffic conditions, respectively. Five SMPs are installedin NT for 13 routes from NT to KL, in which two sets ofSMPs, each with two routes, are installed in New TerritoriesWest (NTW) [22]; and the other three sets of SMPs, each withthree routes, are installed in New Territories East (NTE) [22],as illustrated in Fig. 4. The estimated journey time displayedon each SMP can also be retrieved online [21].

B. Web Mapping Services (WMSs)

WMSs (e.g., Google Maps and Microsoft Bing Maps) pro-vide APIs to support various web services such as directions,distance matrix, and geocoding. It also efficiently visualizeslarge amounts of datasets and provides real-time traffic datato users. To the best of our knowledge, only Google Mapsoffers free real-time traffic data in Hong Kong for the public(with usage limits), so we provide more technical details aboutGoogle Maps and compare it with the ITS in Hong Kong.Google Maps was launched in February 2005 and its APIwas launched in June 2005. Google Maps API supports threemain platforms: Web, Android and iOS. Its directions APIprovides two types of journey time estimation methods: thetypical journey time obtained from historical data and thereal-time journey time based on live traffic conditions andhistorical data. So far, over 100 million active web sites andapplications use Google Maps API. Meanwhile, Google MapsAPI is the most popular WMS API from a survey in 2015 [23].Google Maps mainly uses the crowdsourcing method to collectthe real-time data via mobile GPS devices to improve theaccuracy of traffic reporting [24]. Since mobile devices withGPS like smart phones demonstrate high penetration rates,they have become the most effective sensors of tracking ourdaily movements [25]. Data gathering from mobile devices isat low-cost and can be updated in a continuous or incrementalmanner.

C. Related Work

We first highlight related work that used WMSs in location-based applications, and then discuss the findings from othercomparative analyses.

1) Location-based Applications: WMSs have be used invarious location-based applications. Here are some examples.(a) Logistics and transport network systems. Google MapsAPI and WebGIS were used to build a logistics networksystem that supports information exchange between goodsvehicles, freight scheduling, real-time tracking of goods ve-hicle and friendly visualization on maps [7]. Google MapsDirections API was used to estimate an origin-destinationjourney time matrix for the transport network system [9].Furthermore, Google Maps Distance Matrix API was utilizedto study the journey time variability and build the origin-destination journey time skim matrix that provides impedancesbetween zones (i.e., travel time and distance) for Kaunas City,Lithuania [8]. (b) Network traffic monitoring. SURFmap is anetwork traffic monitoring framework that adds geographicalinformation to a message by mapping its Internet Protocol (IP)address to a location, and then uses Google Maps to visualizethe locations of a set of messages, in order to better understandthe geographical characteristics of the network traffic [10].(c) Intelligent tourism system. Google Maps API was usedto provide the detailed route instructions between points ofinterest in an intelligent tourism system [11]. (d) Location-based services. MapQuest Map API and Google Maps APIwere used for spatial mashups with query optimizations tosupport location-based queries (i.e., k-nearest-neighbor andshortest path queries [9], [12], [13]).

2) Comparative Analyses: A comparative analysis com-pared three WMSs (i.e., Google Maps, Microsoft Bing Maps,and MapQuest) in terms of their features, returned routes andjourney time to explore their behaviors in generating routes inthe road network maps of the city of Pittsburgh, Pennsylvania,USA [14]. The three WMSs tend to return more same routeswith the fastest journey time as the route criterion. If theyreturn the same route, the distance and journey time of theirroutes are not significantly different. Another comparativeanalysis compared the traffic conditions based on the datafrom physical sensors (i.e., the data from loop detectors andelectronic tag readers collected along the New Jersey Turnpike(NJTPK)) and the data from virtual sensors (i.e., MicrosoftBing Maps and MapQuest) [15]. The results of the statisticalcomparisons show that the virtual sensor approach comes withalmost no additional cost while the quality of its obtaineddata is quite satisfactory compared to the physical sensors.This paper is the first one to conduct a city-scale comparativeanalysis on journey time data from Google Maps and physicalsensors from the ITS in Hong Kong.

III. COMPARATIVE ANALYSIS OF JOURNEY TIME

The object of this study is Hong Kong. This section firstlyintroduce the approaches for extracting data from ITS andGoogle Maps (i.e., sec. III-A); then the experiments andanalysis are presented in sec. IV.

A. Data Extraction

We crawled the journey time data from the ITS in HongKong and Google Maps during two weeks from May 24, 2018

to June 6, 2018 (i.e., 10 weekdays and four weekends).1) The ITS in Hong Kong: The journey time data from

the ITS in Hong Kong (i.e., JTIS and SMPs) can be crawledonline [21]. The update rate of the journey time data is twominutes. The traffic data include the following attributes [26]:• Location ID - The unique ID of a journey time indicator;• Destination ID - The unique ID of the destination of a

journey time indicator;• Capture date - The time of generating the journey time

data;• Journey type - The type of display content;• Journey data - The journey time data in minutes;• Color ID - The colour code for the journey time data;

and• Journey description - The description of journey time data

for a specific journey type.Each response contains the journey time data of the 35 routes,as listed in Table I. For the sake of clarity, we give a routenumber to each route monitored by the ITS in Hong Kong,i.e., from R1 to R35.

2) Google Maps Directions API: We use the Google MapsDirections API [27] to extract traffic data from Google Maps.The Directions API is a service that returns multiple directionsconsisting of a series of waypoints with corresponding distanceand time for an origin-destination pair. In our analysis, we senda request for each route listed in Table I to Google Maps via itsDirections API to retrieve its journey time every two minutes.Each request supports the following parameters:• Origin - The longitude and latitude of an origin;• Destination - The longitude and latitude of a destination;• Waypoints - An array of waypoints that alter a route by

routing it through the specified location(s) (optional);• Mode - The mode of transport (optional); and• Departure time - The desired time of departure (optional).

For each request of route R, we specify (1) the start andend locations of R as its origin and destination, respectively,(2) an array of locations as the waypoints to ensure that thereturned route is the same as R, (3) “driving” as the modeof transport, and (4) the current time as the departure timeto retrieve the estimated journey time data based live trafficdata and historical data. Note that the Google Directions APIrequest time is aligned with the update time of the journeytime data collected from the ITS in Hong Kong.

A response to the Google Maps Directions API request isa Java Script Object Notation (JSON) object that includes thefollowing root elements:• The field of geocoded waypoints is an array of geocoded

waypoints with details about the geocoding of origin,destination and waypoints; and

• The field of routes consists of nested legs and steps. Legsis an array that specifies a single leg of the journey fromthe origin to the destination in the returned route alongwith the distance and journey time estimated based onthe current and historical traffic conditions. A separate legwill be present for each waypoint or destination specified

TABLE I: The description of the 35 routes monitored by the ITS in Hong Kong.

Route No. R1 R2 R3 R4 R5 R6 R7 R8 R9Route ID∗ H1-CH H1-EH H2-CH H2-EH H2-WH H3-CH H3-WH H11-CH H11-EHRoute No. R10 R11 R12 R13 R14 R15 R16 R17 R18Route ID K01-CH K01-WH K02-CH K02-EH K03-CH K03-EH K03-WH K04-CH K04-WHRoute No. R18 R20 R21 R22 R23 R24 R25 R26 R27Route ID K05-CH K05-EH K06-CH K06-WH SJ1-LRT SJ1-SMT SJ1-TSCA SJ2-LRT SJ2-TCTRoute No. R28 R29 R30 R31 R32 R33 R34 R35Route ID SJ2-TSCA SJ3-LRT SJ3-TCT SJ3-TSCA SJ4-TKTL SJ4-TKTM SJ5-TWCP SJ5-TWTM∗A-B: A is the ID of a journey time indicator (i.e., Location ID) and B is a Destination ID; CH: Cross Harbour Tunnel; EH: Eastern Harbour Crossing;WH: Western Harbour Crossing; LRT: Lion Rock Tunnel; SMT: Shing Mun Tunnel; TCT: Tate’s Cairn Tunnel; TKTL: Ting Kau, via Tai LamTunnel; TKTM: Ting Kau, via Tuen Mun Road; TSCA: Tsing Sha Control Area; TWCP: Tsuen Wan via Castle Peak; and TWTM: Tsuen Wan viaTuen Mun.

(1) Routes:

origin-

destination

pairs and

waypoints

Google

Directions

API

JSON

Object

Google Maps Server

Geocoded

waypoints,

Routes (duration,

and distance, etc.)

(5) Stored

in CSV file

(2) Directions

API Request

(3) Response

in JSON

(4) Parse JSON

Fig. 5: The procedure of retrieving traffic data using GoogleMaps Directions API.

in the request. Each leg contains a series of steps; eachstep describes a specific, single instruction on the journey.

The response results are automatically saved as CSV fileformat, and the entire procedure is illustrated in Fig. 5.

IV. EXPERIMENTS

In this section, we present the experiment settings (Sec-tion IV-A), analyze experimental results (Section IV-B), andsummarize the findings (Section IV-C).

A. Experiment Settings

We discuss the journey time data of the routes studied inour comparative analysis and the methodology for evaluation.

1) Routes: The JTIS in Hong Kong provides estimatedjourney time for 35 routes, routes R1 to R35, as listed inTable I, in which nine routes from HK toward KL via thethree cross-harbour tunnels (HK-KL) (i.e., routes R1 to R9),13 routes from KL to HK via the three cross-harbour tunnels(KL-HK) (i.e., routes R10 to R22), and 13 routes from NTtoward KL (NT-KL) (i.e., routes R23 to R35). Each of thelength is from 3 to 27 km and the overall total length is300 km. The coordinates of the journey time indicators ofthe JITS and SMPs are represented in the coordinate systemof the HK 1980 Grid. We converted their coordinates intothe coordinate system of WGS 1984 (EPSG:4326) [28], andmanually plotted their coordinates in Google Maps accordingto the converted coordinates and the corresponding names oflocations. Likewise, the coordinates of route destinations wereplotted based on the same procedure.

We also compare the journey time of highway routesseparately because they have less noise than other types of

routes, e.g., no traffic light and one-way direction. Among the35 routes, five routes starting from NT to KL that consistof highway sections (i.e., routes R27, R30, R32, R33 andR35), which are called highway routes, denoted as NT-KL-H. On the other hand, we name the rest routes from NT toKL as general routes, denoted as NT-KL-G. Therefore, NT-KLis further divided into two sets, NT-KL-G and NT-KL-H. Inother words, four sets of routes (HK-KL, KL-HK, NT-KL-G,and NT-KL-H) are considered in our experiments. In addition,we also divide the 35 routes into three length sets based ontheir length. Routes R1, R3, R6, R9, R11, R12, R14, R18 andR21 are in set (0 - 5km); routes R2, R4, R5, R7, R8, R10,R15, R17, R19, R20, R22 to R27 and R31 are in set (5-10km); and routes R13, R16, R28, R29, R30, and R32 to R35are in set (>10 km).

2) Measurements: Similar to [15], we use Wilcoxonsigned-rank test to statistically examine the two-paired sam-ples from Google Maps and the ITS in Hong Kong. Thenull hypothesis H0 is that the two datasets are equivalentand α = 0.1 in this work. In addition, the correlations ofthese two estimates are evaluated in terms of four measuresof performance. (1) We use difference (D) to measure thecorrelations of the journey time derived from Google Maps andthe ITS at a certain time slot. The definition D is D = x− y,where x and y are the journey time data derived from the ITSin Hong Kong and Google Maps during the same time slot,respectively. To avoid positive and negative offsets, (2) meanabsolute difference (MAD), (3) mean absolute relative differ-ence (MRD), and (4) mean absolute difference over length(MAD/L) are used to measure the changes over a period [18],i.e., MAD = 1

n

∑ni |xi−yi|, MRD = 1

n

∑ni|xi−yi|

xi×100%,

and MAD/L = MADLength , where n is number of observations,

xi and yi are the journey time retrieved from the ITS in HongKong and Google Maps during i-th time slot, respectively, andLength is the length of a route (in km). Note that we considerthe journey time data retrieved from the ITS in Hong Kong asbenchmarks in our experiments.

B. Experiment Results

We take 10 minutes as an interval in our experiments, whichmeans each value is a mean value of five values since wecrawled journey time data every two minutes. The journeytime data derived from Google Maps and ITS in Hong Kongare compared and shown in Figures 6 and 7 in a weekday and

TABLE II: p-values: Wilcoxon signed-rank test to compare journey time data from Google Maps and the ITS in Hong KongWeekdays Weekends24-May 25-May 28-May 29-May 30-May 31-May 1-Jun 4-Jun 5-Jun 6-Jun 26-May 27-May 2-Jun 3-Jun

HK-KL1 0.001 0 0 0 0 0.006 0.026 0 0 0.944 0 0.011 0 0.2672 0 0 0 0.001 0 0 0 0 0 0 0 0.001 0 0.0633 0.072 0.303 0.084 0.144 0.009 0 0.203 0.039 0.982 0.002 0.012 0 0.755 04 0 0 0 0 0 0 0 0 0 0 0 0.002 0 0.0025 0 0 0 0 0 0.083 0.084 0 0 0 0 0 0 06 0 0 0 0 0 0 0 0 0 0 0 0 0 07 0 0.012 0.918 0.468 0.557 0.417 0.270 0.431 0.003 0.850 0 0.094 0.004 08 0 0 0 0 0 0 0 0 0 0 0 0 0 09 0.308 0.198 0.075 0.081 0.837 0.639 0.345 0.398 0.026 0.004 0.016 0 0.220 0

KL-HK10 0 0 0 0 0 0 0 0 0 0 0 0 0 011 0 0 0 0 0 0 0 0 0 0 0 0 0 012 0 0 0 0 0 0 0 0 0 0 0 0 0 013 0 0 0 0 0 0 0 0 0 0 0 0.132 0 0.38014 0 0 0 0 0 0 0 0 0 0 0 0 0 015 0 0 0 0.008 0 0 0 0 0 0 0 0 0.007 016 0 0 0 0.112 0 0 0 0.042 0.002 0 0 0.431 0 0.63917 0.005 0.022 0.058 0.023 0.015 0.013 0.582 0.252 0.410 0.118 0 0 0 018 0 0 0 0 0 0 0 0 0 0 0 0.001 0 0.00819 0.547 0.014 0.254 0.453 0.366 0.451 0.039 0.927 0 0.108 0.001 0 0 0.17720 0 0 0 0 0 0 0 0 0 0 0 0 0 021 0 0 0 0 0 0 0 0 0 0 0 0 0 022 0 0 0 0 0 0 0 0 0 0 0 0 0 0.002NT-KL-G23 0 0 0 0 0 0 0 0 0 0 0 0 0 024 0 0 0 0 0 0 0 0 0 0 0 0 0 025 0 0 0 0 0 0 0 0 0 0 0 0 0 026 0 0 0 0 0 0 0 0 0 0 0 0 0 028 0 0 0 0 0 0 0 0 0 0 0 0 0 029 0 0 0 0 0 0 0 0 0 0 0 0 0 031 0 0 0 0 0 0 0 0 0 0 0 0 0 034 0 0 0 0 0.024 0 0.021 0.693 0.022 0.009 0 0 0.273 0NT-KL-H27 0 0 0 0 0 0 0 0 0 0 0 0 0 030 0 0 0 0 0 0 0 0 0 0 0 0 0 032 0.010 0 0 0 0 0 0 0 0 0 0 0 0 033 0 0 0 0 0 0 0 0 0 0 0 0 0 035 0 0 0 0 0 0 0 0 0 0 0 0 0 0

a weekend, respectively. Due to space limitation, we selectfour routes from the four route sets, i.e., R7 from HK-KL,R16 from KL-HK, R29 from NT-KL-G and R32 from NT-KL-H. Obviously, the journey time curves of Google Mapsand the ITS have similar trend throughout the entire day. Wecan see that the difference the differences are varied overthe time of day in the weekday and weekend. This meansthat the traffic characteristics in weekday and weekend arenot consistent. Therefore, we analyze the differences betweenthe journey time derived from Google Maps and the ITS inHong Kong using Wilcoxon signed-rank test and three metrics(MAD, MRD and MAD/L (average MAD of journey time perkm)).

1) Wilcoxon Signed-Rank Test: We test the consistency oftwo samples derived from the journey time data collected fromGoogle Maps and the ITS in Hong Kong using Wilcoxonsigned-rank test. Wilcoxon signed-rank test can be applied todetermine whether two samples having the same distributionbut does not provide the detailed differences of values. We willuse MAD, MRD and MAD/L to reflect their true differences inthe next section. Table II shows the p-values of pair-samplesderived from two journey time estimates for the four routesets (i.e., HK-KL, KL-HK, NT-KL-G, and NT-KL-H) with atotal of 35 routes in 10 weekdays and 4 weekends. As mostof p-values for the 35 routes are zero or close to zero, weconclude that journey time derived from Google Maps arealmost consistent with that obtained from the ITS in Hong

TABLE III: Comparison of journey time differences for thefour route sets in weekdays and weekends

Route Set MAD (minutes) MAD/L (minutes) MRD (%)HK-KL 2.00 0.59 15.03KL-HK 2.12 0.62 18.71

Weekdays NT-KL-G 1.04 0.31 9.23NT-KL-H 1.02 0.30 7.59All 1.69 0.45 14.00HK-KL 1.25 0.37 11.40KL-HK 1.97 0.58 20.42

Weekends NT-KL-G 0.73 0.21 7.73NT-KL-H 0.70 0.21 5.83All 1.32 0.34 13.12

Kong.2) MAD, MRD and MAD/L: Tables III presents the differ-

ences of journey time derived from Google Maps and the ITSin Hong Kong for weekdays and weekends. Besides MADand MRD, we also present the MAD of journey time per km(MAD/L). Clearly, the differences of route sets HK-KL andKL-HK are much higher than the differences of route sets NT-KL-G and NT-KL-H in terms of the three metrics. Specifically,NT-KL-H has the smallest MAD/L 0.3 minutes among all theroute sets and KL-HK has the largest MAD/L 0.62 minutes.Meanwhile, among the four route sets, the largest MAD is2.12 minutes and the largest value of MRD is 18.71%, whichare in the range of acceptance.

Figures 8 gives the average differences of all the routeschanging over time of day in weekdays and weekends. During

(a) Journey time (R7) (b) Journey time (R16) (c) Journey times (R29) (d) Journey times (R32)

Fig. 6: Journey time changing over time of day (30 May 2018, weekday).

(a) Journey time (R7) (b) Journey time (R16) (c) Journey time (R29) (d) Journey time (R32)Fig. 7: Journey time changing over time of day (3 June 2018, weekend)

weekdays, the differences increase tremendously during rushhours, around 8am to 10am and 6pm to 8pm. We can seethat the average differences increase as more vehicles are onthe roads during rush hours. The average differences are smallfrom 11pm to 6am; the speed is close to the speed limit duringthat period. During weekends, there is no peak during themorning, but MAD starts increasing at 8am and reaches itspeak around 6pm. We may explain that people do not need togo out that early as in weekdays, and they trend to drive togo out or go home for dinners.

In terms of lengths, all the 35 routes are grouped into inthree sets: 0-5 km, 5-10 km and >10 km. Table IV showsthe differences of two estimates changing over the length of aroute. All MAD, MAD/L and MRD values decrease with theincreasing length. The shorter a route is, the more dynamicaltraffic condition could be. All shorter routes (0-5 km) arelocated in urban areas (Kowloon Peninsula and Hong KongIsland) which are with more dynamic environments (e.g., moretraffic lights and points of interest). For example, the journeytime of a shorter route with traffic lights varies tremendously.On the other hand, most of longer routes (>10 km) are insuburb areas, where routes have better traffic conditions (e.g.,the travel speed is close to the speed limit) that are good forestimation. Therefore, the differences of two estimates for aroute with a longer length are smaller.

C. Discussion

This section discusses the differences of the journey timedata from Google Maps and the ITS in Hong Kong in termsof spatial and temporal factors. (1) Spatial factors. HongKong consists of three areas, namely Hong Kong Island(HK), Kowloon Peninsula (KL) and New Territories (NT).The locations of areas may be one of the reasons causing

Fig. 8: Average journey time differences changing over timeof day ( weekdays and weekends)

different differences in the four route sets since HK and KLare the most crowded urban areas among the three areasin Hong Kong and New Territories is located near suburbs.Route sets KL-HK and HK-KL are in urban areas where thereare much more dynamic environments, such as more trafficlights, bidirectional directions, and many popular points ofinterest. Route sets NT-KL-G and NT-KL-H are from NT toKL, namely a part of sections of each route are in suburbarea which provides better traffic conditions. These factorsmay cause the result that the journey time differences in twoestimates for urban routes are larger than that for suburbsroutes. On the other hand, highway routes have better drivingconditions such as higher speed limit and no traffic light whichmay lead to the result that the journey time of NT-KL-H fromtwo estimates are closer than that of NT-KL-G. (2) Temporalfactors. In weekdays, there is a regular daily schedule for mostpeople in Hong Kong, e.g., going to work and school. Suchregular schedules easily cause traffic congestions during rushhours in weekdays, so drivers suffer from longer journey timeon same route than non-rush hours. Whereas, in weekends,the schedules of people are very flexible. One of salient

characteristics is that the differences of two estimates duringthe rush hours in the morning are not significant; however, thedifferences during the rush hours in the evening (about 6pm)are much larger.

TABLE IV: Journey time differences changing over the lengthof a route

Length (km) MAD (mins.) MAD/L (mins.) MRD (%)0-5 1.95 0.57 18.785-10 1.845 0.542 15.22>10 1.12 0.329 6.93

V. CONCLUSION

In this paper, we introduce a traffic data collecting approachusing Web mapping services and present a comparative anal-ysis of the journey time data from Google Maps and theITS in Hong Kong. The ITS monitors 35 major routes andprovides estimated journey time every two minutes; and thus,we also retrieve the journey time data on these routes fromGoogle Maps through its Directions API. In the experiments,we measure consistencies the journey time data from thesetwo sources in terms of Wilcoxon signed-rank, and furtherevaluate their actual differences in terms of mean absolutedifference, mean absolute relative difference and mean abso-lute difference over length. Experimental results show that thejourney time from Google Maps and the ITS are consistentwith each other during most of time slots both in weekdaysand weekends; their differences are very small and acceptable.This study can be also similarly applied for other cites withITS which provides traffic data. As far, for many governmentsof cities, it is still not easy to afford the very high costs onconventional ITS equipments and their long-term maintenance.However, Google Maps that are considered as virtual sensorscan conveniently provide real-time traffic data almost aroundworld.

There are two research directions for this work. (1) We planto investigate the predictability of Google Maps without livedata. For example, we issue a Directions API request for aroute with a future timestamp as its departure time, and thencompare its returned journey time with the real-time journeytime results retrieved from the ITS in Hong Kong and GoogleMaps. (2) We will study the adaptability of Google Maps totraffic accidents. For example, compare the journey traffic datafrom Google Maps and the ITS in Hong Kong for a routewhere a traffic accident takes place.

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