Urban Informatics Using Mobile Network Data: Travel Behavior Research Perspectives

246 Pages·2022·17.812 MB·English

by  Santi Phithakkitnukoon

Santi Phithakkitnukoon
Urban
Informatics
Using Mobile
Network Data
Travel Behavior Research Perspectives
Urban Informatics Using Mobile Network
Data
Santi Phithakkitnukoon
Urban Informatics Using
Mobile Network Data
Travel Behavior Research Perspectives
SantiPhithakkitnukoon
DepartmentofComputer
Engineering,FacultyofEngineering
ChiangMaiUniversity
ChiangMai,Thailand
ISBN978-981-19-6713-9 ISBN978-981-19-6714-6 (eBook)
https://doi.org/10.1007/978-981-19-6714-6
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Preface
Urban informatics has emerged as an exciting and promising field requiring many
interdisciplinary perspectives that involve computation as a central part by which
methods and models are developed to generate a deeper understanding of city’s
functioning. The field of urban informatics is developing rapidly in the light of
ubiquitouscomputingandsensortechnologies.Duetoitsvariouspotentialbenefits
such as proactive urban planning, intelligent transportation systems, and traffic
management, mobility studies have become central to urban informatics. A large
part of urban informatics involves the development of data mining and machine
learning algorithms for extracting and characterizing mobility patterns from heter-
ogenous urban data sources. Opportunistic sensing data sources have recently
become valuable to urban informatics and travel behavior research, especially
mobilenetworkdataasitprovidesalongitudinalreal-lifebehaviorofagreatamount
of people. This book therefore discusses the role of mobile network data in urban
informatics, particularly how the mobile network data is utilized in the mobility
context, where approaches, models, and systems are developed for understanding
travelbehavior.Theobjectivesofthisbookarethustoevaluatetheextenttowhich
mobile network data reflects travel behavior and to develop guidelines on how to
best use such data to understand and model travel behavior. To achieve these
objectives, the book attempts toevaluate the strengths and weaknesses of this data
sourceforurbaninformaticsanditsapplicabilitytothedevelopmentandimplemen-
tationoftravelbehaviormodelsthroughaseriesofourresearchstudies.
Traditionally, survey-based information is used as an input for travel demand
modelsthatpredictfuturetravelbehaviorandtransportationneeds.Asurvey-based
approach is however costly and time-consuming, and hence its information can be
dated and limited to a particular region. Mobile network data thus emerges as a
promising alternative data source that ismassiveinboth cross-sectional and longi-
tudinal perspectives, and one that provides both broader geographic coverage of
travelersandlong-termtravelbehaviorobservation.Thetwomostcommontypesof
traveldemandmodelthathaveplayedanessentialroleinmanagingandplanningfor
transportationsystemsarefour-stepmodels andactivity-basedmodels.Thebook’s
chaptersarestructuredonthebasisofthesetraveldemandmodelsinordertoprovide
researchers and practitioners with an understanding of urban informatics and the
v
vi Preface
importantrolethatmobilenetworkdataplaysinadvancingthestate-of-the-artfrom
theperspectivesoftravelbehaviorresearch.
Chapter 1 introduces the key concepts and framework of urban informatics,
discusses the use of mobile network data in urban informatics, and provides an
outline of the book’s structure and scope. Chapters 2–6 discuss the use of mobile
network data in different steps of the four-step models, while Chap. 7 describes a
frameworkthatbenefitstheactivity-basedmodelandChap.8presentsanapplication
of mobile network data-based travel behavior analysis in the context of tourists.
Chapter2discussesaframeworkforinferringtripgeneration(step1ofthefour-step
models)thatreflectsatraveldemandforpublictransitinSenegal.Asaresult,asetof
strategiclocationsforpublictransportationserviceissuggested.Chapter3describes
amobilenetworkdata-basedframeworkfortripdistributionmodeling(step2ofthe
four-stepmodel)andevaluationoftheinfluenceofintrazonaltripsinmodelestima-
tion through a case study of mobile phone users in Senegal. Chapter 4 presents a
method forinferring migration flows based on mobile phone users inPortugal and
examinesdifferenttripdistributionmodels(step2ofthefour-stepmodel).Chapter5
discussesaframeworkforinferringtransportmodes(step3ofthefour-stepmodel)
in the context of how social network influences the choice of transport mode for
commuting, which is based on a case study of mobile phone users in Portugal.
Chapter 6 presents and examines a set of different inference models for route
assignment(step4ofthefour-stepmodel)basedonmobilenetworkusersinLisbon,
Portugal.Fromtheactivity-basedapproach’spointofview,Chap.7describeshowa
daily activity pattern is inferred and investigates on the influence of weather
conditionsonthechoiceofdailyactivities,throughacasestudyofamobilenetwork
inJapan.Chapter8presentsaframeworkforinferringtouristictripsbasedonmobile
phoneusersinJapananddiscusseshowtheinferredtripsareusedinthecontextofa
touristbehavioranalysis,whichincludesastatisticalanalysis,suchastouristflows,
top tourist destinations and origins, top destination types, top modes of transporta-
tion in terms of time spent and distance traveled, as well as a visual analytics tool
developed based on this analysis. Finally, Chapter 9 provides an outlook on the
future directions of the mobile network data-based urban informatics from the
perspective of travel behavior research, including mobile network data
characteristics, data collection, data uncertainty and privacy, and travel behavior
patternmining.
Thisbookaimsatadvanced undergraduates,graduate students,researchers,and
practitionersinurbaninformatics,particularlythosewhoareinterestedinutilization
ofmobilenetworkdataintravelbehaviorresearch.Ihopethatthereadersfindthis
bookusefulasanoverviewofurbaninformaticsandapracticalguidelineforusing
mobilenetworkdataintravelbehaviorresearch.
ChiangMai,Thailand SantiPhithakkitnukoon
Acknowledgments
Research studies discussed in this book are from fruitful collaborations with my
colleagues over the years. So, I would like to thank Dr. Merkebe Demissie,
Dr. Francisco Antunes, Dr. Rui Gomes, Dr. Carlos Bento, Dr. Zbigniew Smoreda,
Dr.TuckLeong,Dr.TeerayutHoranont,Dr.ApichonWitayangkurn,Dr.Juggapong
Natwichai,Prof.LinaKattan,Prof.CarloRatti,Prof.YoshihideSekimoto,andProf.
RyosukeShibasakifortheirsupport.Iwouldalsoliketothankmystudents,Titipat
Sukhvibul, Soranan Hankaew, and Pitchaya Sakamanee, who worked with me in
studiesdiscussedinthisbook.
IwouldliketothankmycolleaguesattheDepartmentofComputerEngineering,
Chiang Mai University for their constant support of my teaching and research.
SpecialthankstoDr.SanpawatKantabutra(Bobby)forgivingmehisbookProblem
SolvinginAlgorithms:AResearchApproach,whichgreatlyinspiredmetowritemy
ownbook.Thisbook’sstructureisverymuchinspiredbyhisbook.
IwouldliketoexpressmygratitudetoProf.CarloRattiforintroducingmetothis
interestingfieldofurbaninformaticsresearchwhenIwasapostdoctoralresearcher
inhisSenseableCityLabatMIT,andforhislong-termsupportofmyresearchand
career.
Tomydearestwife,Raktida—thankyouforallyoursupportandencouragement.
Youhavebeenmymainsupportsincedayoneofthisbook-writingjourney.I’mso
blessedofhavingyou.Dearmomanddad,Iwishyouwerestillheretoseethisbook.
Imissyoubothsomuch.
ManythankstoSpringer’seditorialserviceteam,NickZhu,JayeshKalleri,Ellen
Seo, and Periyanayagam Leoselvakumar for helping me throughout the whole
publishingprocess.
vii
Contents
1 TheOverviewofMobileNetworkData-DrivenUrban
Informatics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 UrbanInformatics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . 2
1.2 TraditionalMethodsinTravelBehaviorUnderstanding. . . . . . . . . 4
1.3 MobileNetwork-BasedTravelBehaviorDataSensing. . . . . . . . . 6
1.4 MobileNetworkData-BasedTravelBehaviorInference. . . . . . . . 9
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2 InferringPassengerTravelDemandUsingMobilePhoneCDR
Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.1 MotivationandStateoftheArt. . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.2 CaseStudyAreaandDataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.2.1 CaseStudyArea. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.2.2 TransitProfileofCaseStudyArea. . . . . . . . . . . . . . . . . . 22
2.2.3 Dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.3 MethodologyandResults. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.4 Validation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.5 DiscussionofPotentialApplications. . . . . . . . . . . . . . . . . . . . . . 33
2.5.1 ImprovingtheCurrentPracticeofUrbanParatransit
Service. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.5.2 ProvidingIndicatorsforPotentialHighOrderPublic
TransportDevelopment. . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.5.3 Cost-EffectiveTransportPlanningApproach. . . . . . . . . . . 38
2.6 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3 ModelingTripDistributionUsingMobilePhoneCDRData. . . . . . . 45
3.1 MotivationandStateoftheArt. . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.2 Methodology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.2.1 CaseStudyRegionandDataset. . . . . . . . . . . . . . . . . . . . 49
3.2.2 StayandPass-byAreaIdentification. . . . . . . . . . . . . . . . . 50
3.2.3 SignificantLocationDetection. . . . . . . . . . . . . . . . . . . . . 51
3.2.4 TripDetection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
iixx
x Contents
3.2.5 TripTypes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.2.6 TripCorrection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.2.7 TripExpansion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.2.8 TripDistributionModeling. . . . . . . . . . . . . . . . . . . . . . . . 57
3.3 ResultsandDiscussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.3.1 TravelDistances. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.3.2 TripDistributionModels. . . . . . . . . . . . . . . . . . . . . . . . . 60
3.3.3 Log-LinearModel-BasedApproaches. . . . . . . . . . . . . . . . 65
3.3.4 TripDistanceDistribution. . . . . . . . . . . . . . . . . . . . . . . . 67
3.4 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4 InferringandModelingMigrationFlowsUsingMobilePhoneCDR
Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.1 MotivationandStateoftheArt. . . . . . . . . . . . . . . . . . . . . . . . . . 76
4.2 Methodology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.2.1 Dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.2.2 Subjects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.2.3 MigrationFlowInference. . . . . . . . . . . . . . . . . . . . . . . . . 80
4.2.4 MigrationFlowModelling. . . . . . . . . . . . . . . . . . . . . . . . 85
4.3 Results. . . . . . . .. . . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . . .. . 91
4.3.1 Log-Linearmodel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
4.3.2 GravityModel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4.3.3 RadiationModel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
4.4 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
5 InferringSocialInfluenceinTransportModeChoiceUsingMobile
PhoneCDRData. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
5.1 MotivationandStateoftheArt. . . . . . . . . . . . . . . . . . . . . . . . . . 104
5.1.1 SocialInfluenceonTravelBehavior. . . . . . . . . . . . . . . . . 106
5.1.2 MobileSensingApproachinBehaviorAnalysis. . . . . . . . 107
5.2 Methodology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
5.2.1 SubjectSelection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
5.2.2 ResidenceandWorkLocationInference. . . . . . . . . . . . . . 108
5.2.3 SocialTieStrengthInference. . . . . . . . . . . . . . . . . . . . . . 110
5.2.4 TransportModeInference. . . . . . . . . . . . . . . . . . . . . . . . 113
5.3 Results. . . . . . . .. . . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . . .. . 117
5.3.1 CommuteModeChoicesofSocialTies. . . . . . . . . . . . . . . 117
5.3.2 SocialDistance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
5.3.3 PhysicalDistance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
5.3.4 Ego-NetworkEffect. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
5.4 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
Contents xi
6 InferringRouteChoiceUsingMobilePhoneCDRData. . . . . . . . . . 131
6.1 MotivationandStateoftheArt. . . . . . . . . . . . . . . . . . . . . . . . . . 132
6.2 Methodology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
6.2.1 Dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
6.2.2 ResidenceandWorkLocationInference. . . . . . . . . . . . . . 134
6.2.3 RouteChoices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
6.2.4 RouteChoiceInferenceMethods. . . . . . . . . . . . . . . . . . . 136
6.3 Results. . . . . . . .. . . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . . .. . 147
6.3.1 Interpolation-BasedMethods. . . . . . . . . . . . . . . . . . . . . . 147
6.3.2 ShortestDistance-BasedMethods. . . . . . . . . . . . . . . . . . . 149
6.3.3 VoronoiCell-BasedMethods. . . . . . . . . . . . . . . . . . . . . . 151
6.3.4 VisitedVoronoiCell-BasedMethods. . . . . . . . . . . . . . . . 152
6.3.5 ResultSummary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
6.4 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
7 AnalysisofWeatherEffectsonPeople’sDailyActivityPatterns
UsingMobilePhoneGPSData. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
7.1 MotivationandStateoftheArt. . . . . . . . . . . . . . . . . . . . . . . . . . 162
7.2 Methodology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
7.2.1 Datasets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
7.2.2 Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
7.3 Results. . . . . . . .. . . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . . .. . 168
7.3.1 WeatherEffectsonMobilityandStopDuration. . . . . . . . . 169
7.3.2 WeatherEffectsonActivitiesatDifferentTimes
oftheDay. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
7.3.3 WeatherEffectsonActivitiesinDifferentAreas. . . . . . . . 174
7.4 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
8 AnalysisofTouristBehaviorUsingMobilePhoneGPSData. . . . . . 183
8.1 MotivationandStateoftheArt. . . . . . . . . . . . . . . . . . . . . . . . . . 184
8.2 Methodology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
8.2.1 Dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
8.2.2 ResidenceandWorkplaceLocationDetection. . . . . . . . . . 186
8.2.3 TouristicTripInference. . . . . . . . . . . . . . . . . . . . . . . . . . 190
8.3 AnalysisofTouristBehavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . 191
8.3.1 AmountofTouristicTrips. . . . . . . . . . . . . . . . . . . . . . . . 192
8.3.2 TimeSpentatDestination. . . . . . . . . . . . . . . . . . . . . . . . 197
8.3.3 ModeofTransportation. . . . . . . . . . . . . . . . . . . . . . . . . . 200
8.3.4 RelationshipBetweenPersonalMobilityandTravel
Behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
8.4 AnalysisofSimilarityinTravelBehavior. . . . . . . . . . . . . . . . . . . 207
8.5 Application. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210
8.6 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213