Transportation Analytics in the Era of Big Data

240 Pages·2019·8.568 MB·English

by  Satish V. Ukkusuri, Chao Yang

Complex Networks and Dynamic Systems 4
Satish V. Ukkusuri · Chao Yang Editors
Transportation
Analytics in the
Era of Big Data
Complex Networks and Dynamic Systems
Volume 4
SeriesEditor
TerryL.Friesz
PennsylvaniaStateUniversity
UniversityPark,PA,USA
Moreinformationaboutthisseriesathttp://www.springer.com/series/8854
Satish V. Ukkusuri • Chao Yang
Editors
Transportation Analytics
in the Era of Big Data
123
Editors
SatishV.Ukkusuri ChaoYang
SchoolofCivilEngineering TongjiUniversity
PurdueUniversity Shanghai,China
WestLafayette,IN,USA
ISSN2195-724X ISSN2195-7258 (electronic)
ComplexNetworksandDynamicSystems
ISBN978-3-319-75861-9 ISBN978-3-319-75862-6 (eBook)
https://doi.org/10.1007/978-3-319-75862-6
LibraryofCongressControlNumber:2018950450
©SpringerInternationalPublishingAG,partofSpringerNature2019
Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof
thematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation,
broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionorinformation
storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodology
nowknownorhereafterdeveloped.
Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication
doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant
protectivelawsandregulationsandthereforefreeforgeneraluse.
Thepublisher,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthisbook
arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor
theeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinorforany
errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional
claimsinpublishedmapsandinstitutionalaffiliations.
Printedonacid-freepaper
ThisSpringerimprintispublishedbytheregisteredcompanySpringerInternationalPublishingAGpart
ofSpringerNature.
Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland
Preface
Historicallytransportationstudieshaveprimarilyreliedonsurvey-basedapproaches
forunderstandingtravelbehaviorandurbandynamics.Thelastfewyearshasseen
an explosion of “big data” techniques due to the rapid proliferation of various
passive and mobile sensors in urban areas providing high resolution and large
dimensional data in urban transportation systems. The book will focus on recent
advances in the area of big data analytics and their applicability to solve various
issues in transportation systems planning and operations. The core algorithmic
approaches for big data techniques are based on machine learning (ML) methods.
Thesehaveprimarilybeendevelopedincomputerscienceandengineeringandhave
widely been in various engineering applications in the last few years [1–4]. These
methodsarerelativelynewinthetransportationsystemsareaandweestimatethere
areapproximatelyaround55scholarsaroundtheglobeactivelyworkinginresearch,
demonstrations,andapplicationspertainingtothevariousaspectsofbigdataurban
mobilitymodeling.
ThisbookdocumentsselectedpapersfromtheworkshoponBigDataAnalytics
forTransportationModeling,whichwasorganizedonJuly16–17,2016,atTongji
UniversitybyProfessorsSatishV.UkkusuriandChaoYang.Thisworkshopbrought
together various international experts from academia, agencies, and industry to
discuss emerging topics of interest in big data modeling. Various topics related to
big data methods, data collection and curation, and applications for planning and
operationsintransportationsystemswerepresentedattheworkshop.Theorganizers
invited a selected set of participants to contribute chapters to this edited book.
Finally, we selected about nine chapters for this book with authors from various
universities in the world. Each book chapter was peer reviewed by at least two
reviewersandwaseditedtofitwiththemainfocusofthebook.Thebookisdivided
intoninechapters.
Chapter 1 is based on utilizing tweet data for transportation planning appli-
cations. Particularly, the authors focused on estimating the localization of non-
geotagged tweets using point of interest data and other spatial variables. This
method provides rich information of both the geo-located tweets and estimates of
v
vi Preface
aggregatedlocationfromnon-geotaggedtweetsanddemonstratedtheusefulnessfor
varioustransportationapplicationssuchasincidentdetection.
Chapter2explorestheuseofsocialmediadatafromTwittertocomplementother
datasourcestounderstandthreetransportationapplications—trafficeventdetection,
humanmobilityexploration,andtrippurposeanddemandforecasting.Thechapter
discusseshowtoleveragegeolocationtweetstoextractdisplacementofpeopleand
automaticallyextracttopicsofrelevanceforpredictingeventshifts.
Chapter3iswrittenbysomeoftheoriginalcollectorsoftaxidatainNewYork
CityfromtheTaxiandLimousineCommission(TLC).Ontheonehand,thechapter
provides a historical perspective of the data collection initiative starting in 2004.
On the other hand, transportation network providers have fought hard to prevent
theirdatafrombeingreleasedtotransportationregulators,frustratingtheirmission
to implement policy, make and enforce regulations. The chapter highlights issues
related to data accuracy, security, privacy, transparency, and compliance and the
needforthird-partyindependentinstitutionstoauditandmaintainthisdata.
Chapter 4 discusses a big data collection method using inertial measurement
units (IMUs) to estimate vehicle path, detect traffic stops, and classify traffic-
related events. The chapter discusses methods to estimate the vehicle trajectory
from IMU and Bluetooth data and the mathematical problems that arise for an
accurate estimation of such network-wide problems. The chapter discusses how
to use this type of data to estimate traffic network states and at the same time
maintainingtheprivacyofthedata.
Chapter5discussesthedata,methods,andapplicationsoftrafficsourcepredic-
tion,whichmayprovideanewwaytobetterunderstandthetrafficcongestion.The
chapter discusses the “static driver source” that can be applied to urban planning
and“dynamicdriversource”thatcansupporttrafficmanagementandcontrol.The
chapteralsodiscusses“passengersource”inpublictransitsystem.
Chapter 6 discusses a sequential K-means clustering algorithm that utilizes
smart card data to categorize Beijing subway stations, which are clustered into
ten groups that are classified under three categories, i.e., employment-oriented,
dual-peak, and residence-oriented stations. The chapter employs a geographically
weighted regression model to determine the correlation effect between peak-hour
passengerflowandland-usedensity.Thefindingsofthischapterprovideinsightful
information and theoretical foundations for rail transportation network design and
operationsmanagement.
Chapter7discussestherisingconcernforlocationprivacywiththeemergenceof
GPScapablemobiledevicesandtheincreasingdemandofcontextualservicessuch
aslocation-basedservices.Thechapterdiscussesthemethodofgeospatialanalyses
asanevaluationtooloftheimpactofnoise-basedalgorithmsonlocationdata.The
chapteridentifiesathresholdofnoisesettingssothatprivacycanbeprovidedwhile
geo-statisticinferencesarenotaffectedgreatly.
Chapter 8 discusses PErsonal TRansport Advisor (PETRA) EU FP7 project,
which is to develop an integrated platform to supply urban travelers with smart
journeyandactivityadvices,onamulti-modalnetwork.Thechapterdiscussesthe
architectureofPETRAplatformandpresentsresultsobtainedbyapplyingPETRA
Preface vii
to two different use cases, namely journey planning under uncertainty and smart
tourismadvisorwithcrowdbalancing.TheresultsofapplyingPETRAintwocities,
RomeandVenice,arealsopresented.
Chapter 9 is on mobility pattern identification using mobile phone call record
data (CRD) of 60 days obtained from Shenzhen, China. The chapter discusses
representative features that were captured from each pattern in both weekday
and weekend. The mobility pattern discussed in this chapter provides a new way
to understand travel behavior, which plays a crucial role in urban planning and
epidemiccontrol.
In summary, the chapters in the book provide a rigorous understanding of
big data methods, analytics, and applications to various transportation planning,
operations,andcontrolproblems.Furthermore,someofthechaptersdiscussnovel
datacollectionmethodsanddataprivacyissues,whichareimportantasweinnovate
inthisareainthefuture.
Finally, we would like to acknowledge various people who have contributed
to the completion of this book. We thank all authors who submitted their work
for consideration. In addition, we thank the dozens of referees for their important
work in reviewing the papers. We would also like to acknowledge the financial
supportprovidedfortheworkshopbyFundamentalResearchFundsoftheCentral
Universities. We also thank Dr. Xiangdong Xu for support during the workshop
preparation.SeveralstudentsXianyuanZhan,XinwuQian,FenfanYan,andYulaing
Zhangalsohelpedwiththeworkshoporganization.
WestLafayette,IN,USA SatishV.Ukkusuri
Shanghai,China ChaoYang
December2017
References
1. P.S.Earle,D.C.Bowden,M.Guy,Twitterearthquakedetection:Earthquakemonitoringina
socialworld.Ann.Geophys.54,708–715(2011).
2. N. Naik, R. Raskar, C.A. Hidalgo, Cities are physical too: Using computer vision to mea-
sure the quality and impact of urban appearance. Am. Econ. Rev. 106, 128–132 (2016).
doi:10.1257/aer.p20161030
3. X.Zhan,X.Qian,S.V.Ukkusuri,Agraphbasedapproachtomeasuringtheefficiencyofurban
taxiservicesystem.IEEETrans.Intell.Transp.Syst.17(9),2479–2490(2016).
4. X.Qian,S.V.Ukkusuri,Timeofdaypricingintaximarkets.IEEETrans.Intell.Transp.Syst.
18(6),1610–1622(2017).
Contents
1 BeyondGeotaggedTweets:ExploringtheGeolocalisationof
TweetsforTransportationApplications................................... 1
JorgeDavidGonzalezPaule,YeranSun,
andPiyushimita(Vonu)Thakuriah
1.1 Introduction ............................................................. 1
1.2 Background.............................................................. 2
1.3 Data...................................................................... 5
1.4 Methodology ............................................................ 6
1.5 Results................................................................... 12
1.6 Conclusions ............................................................. 17
References..................................................................... 19
2 SocialMediainTransportationResearchandPromising
Applications .................................................................. 23
ZhenhuaZhangandQingHe
2.1 SocialMediaExplosion................................................. 23
2.2 ApplicationsBasedonSocialMedia................................... 26
2.3 Result.................................................................... 33
2.4 DiscussionsonFutureImprovementsandApplications .............. 41
References..................................................................... 42
3 GroundTransportationBigDataAnalyticsandThirdParty
Validation:SolutionsforaNewEraofRegulationandPrivate
SectorInnovation ............................................................ 47
MatthewW.Daus
3.1 PartI:HistoryofTaxiData............................................. 47
3.2 PartII:TheAdventoftheTNCMovementandTNCData........... 56
3.3 PartIII:GroundTransportationandtheFutureofBigData.......... 69
3.4 ConclusionandRecommendations:ANeedforThirdParty
Validation................................................................ 75
ix
x Contents
4 APrivacy-PreservingUrbanTrafficEstimationSystem ................ 81
TianLei,AlexanderMinbaev,andChristianG.Claudel
4.1 Introduction ............................................................. 81
4.2 SystemOverview ....................................................... 83
4.3 UserPrivacyAnalysis................................................... 87
4.4 InertialMeasurementUnitBasedTrafficFlowMonitoring........... 89
4.5 IMUCalibrationandTrajectoryEstimation........................... 90
4.6 DistributedComputingforTrafficStateEstimation................... 93
4.7 Conclusion .............................................................. 100
References..................................................................... 102
5 Data,Methods,andApplicationsofTrafficSourcePrediction ......... 105
ChengchengWangandPuWang
5.1 Introduction ............................................................. 105
5.2 Data...................................................................... 106
5.3 Methods ................................................................. 109
5.4 Applications............................................................. 115
5.5 DiscussionandConclusions............................................ 118
References..................................................................... 119
6 AnalyzingtheSpatialandTemporalCharacteristicsofSubway
PassengerFlowBasedonSmartCardData .............................. 121
XiaoleiMa,JiyuZhang,andChuanDing
6.1 Introduction ............................................................. 121
6.2 SubwayStationClassification.......................................... 122
6.3 CorrelationBetweenPassengerFlowandLandUse.................. 136
6.4 Conclusions ............................................................. 149
References..................................................................... 150
7 AnInitialEvaluationoftheImpactofLocationObfuscation
MechanismsonGeospatialAnalysis....................................... 153
PedroWightmanandMayraZurbarán
7.1 Introduction ............................................................. 153
7.2 Location-BasedServices................................................ 154
7.3 LocationPrivacyProtectionMechanisms.............................. 157
7.4 ExploratorySpatialDataAnalysis ..................................... 168
7.5 Methodology ............................................................ 170
7.6 Analysis of Experiments (the original one, Pinwheel
withmaximumradiussetto500mandto1km)...................... 171
7.7 ConclusionsandOpenProblems....................................... 177
References..................................................................... 179
8 PETRA:ThePErsonalTRansportAdvisorPlatformandServices.... 181
Michele Berlingerio, Veli Bicer, Adi Botea, Stefano Braghin,
FrancescoCalabrese,NunoLopes,RiccardoGuidotti,
FrancescaPratesi,andAndreaSassi
8.1 Introduction ............................................................. 181
8.2 RelatedWork............................................................ 183
Contents xi
8.3 UseCasesOverview.................................................... 185
8.4 ModellingUncertaintyinMulti-ModalJourneyPlanning............ 187
8.5 PETRASystemComponents........................................... 193
8.6 CaseStudy:Rome ...................................................... 199
8.7 CaseStudy:Venice...................................................... 204
8.8 Conclusion .............................................................. 214
References..................................................................... 215
9 MobilityPatternIdentificationBasedonMobilePhoneData .......... 217
ChaoYang,YuliangZhang,SatishV.Ukkusuri,andRongrongZhu
9.1 Introduction ............................................................. 217
9.2 DataandMethodologies................................................ 218
9.3 ResultsandDiscussions ................................................ 223
9.4 Conclusions ............................................................. 231
References..................................................................... 232
Index............................................................................... 233