Logic-Driven Traffic Big Data Analytics: Methodology and Applications for Planning

296 Pages·2022·10.857 MB·English

by  Shaopeng Zhong, Daniel (Jian) Sun

Shaopeng Zhong
Daniel (Jian) Sun
Logic-Driven
Traffic Big
Data Analytics
Methodology and Applications
for Planning
Logic-Driven Traffic Big Data Analytics
·
Shaopeng Zhong Daniel (Jian) Sun
Logic-Driven Traffic Big
Data Analytics
Methodology and Applications for Planning
ShaopengZhong Daniel(Jian)Sun
SchoolofTransportationandLogistics CollegeofFutureTransportation
DalianUniversityofTechnology Chang’anUniversity
Dalian,China Xi’an,China
InstituteofSmartCityandIntelligent InstituteofNationalSecurity
Transportation ShanghaiJiaoTongUniversity
SouthwestJiaotongUniversity Shanghai,China
Chengdu,China
NationalNaturalScienceFoundationofChina71971038 71701030
FundamentalResearchFundsfortheCentralUniversitiesofChinaDUT20GJ210
NationalNaturalScienceFoundationofChina72150410445 52172319 71971138
ISBN978-981-16-8015-1 ISBN978-981-16-8016-8 (eBook)
https://doi.org/10.1007/978-981-16-8016-8
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Tomyfamily,especiallymylovingwife,
DandanLi
—ShaopengZhong
Tomyfamilyandmymom
—Daniel(Jian)Sun
Foreword by Tien Fang Fwa
Trafficcongestionandtraveldelaysareperennialproblemsinpracticallyallmajor
cities in the world. Transportation engineers equipped with modern highway and
traffic engineering theories have not been able to provide solutions to tackle these
problemseffectivelytothesatisfactionofthegeneralpublic.Withtheadvancesin
computer technologies and rapid increase in computing power, coupled with the
growth of and innovations in artificial intelligence, information and telecommu-
nication technologies, there evolves a host of new tools and analytics that allows
transportationengineerstodevelopnewsolutionsandgainnewinsightsintotheold
problems.Bigdataanalyticsisoneoftheseexcitingandpromisingtoolsthathave
generatedmuchinterestamongtransportationresearchers.Likeallnewtechnologies,
bigdataanalyticscomeswithmanychallenges,someofwhichmaynotbeapparent
togeneralusersandevenresearchers.
Intheabove-mentionedcontext,thisbookbytwoexperiencedprofessionalsoffers
averytimelyreadingfortransportationengineeringpractitionersandresearchers.It
helpsthereadertogainadeeperunderstandingofthevalueofbigdataapplicationsin
solvingurbantrafficproblems,andbebetterpreparedtobenefitfromthenewoppor-
tunitiesandassociatedchallenges.Dr.Zhonghasmorethan20yearsofprofessional
experience in the field of sustainable transportation planning, land use and trans-
portation integration modeling, urban transport network analysis, and logic-driven
transportbigdataanalysis.Dr.Sun,mycolleagueintheCollegeofFutureTransporta-
tion,Chang’anUniversity,conductsresearchonsmartcityandintelligenttransporta-
tion systems, including risk and resilience of urban transportation infrastructures,
transportationenvironment,transportbigdataanalyticsandsimulation.
Thebookfocusesonthemethodsandapplicationsofmulti-sourcetrafficbigdata.
Itfirstintroducesthetheoryoflanduseandtransportationintegration,andpresents
therelevantstatisticalmodelsandmethods(Chap.1).Avaluablefeatureofthebook
istheinformativecasestudieswhicharedealtwithinnecessarydetail.Theyprovide
usefulreferencesforthereadertoappreciatethecauseandeffectrelationshipbetween
events,andhowandwhythedata-drivenmethodworks.Casestudiesareintroduced
from the following perspectives, including travel demand analysis (Chaps. 2 and
3),trafficcongestionpatterndetectionandrelatedpolicyanalysis(Chaps.4and5);
vii
viii ForewordbyTienFangFwa
traveltimeestimation(Chaps.6and7);drivers’behaviorandroadsafety(Chaps.8–
10);andurbantrafficemission(Chaps.11and12).
Thewell-structuredbookpresentsanidealreadingforgraduateresearchstudents
inthefieldsoftrafficengineeringandtransportationplanning.Itwouldalsobeagood
referenceforresearchersinthesamefieldswhoareinterestedinbigdataapplications.
T.F.Fwa
EmeritusProfessor,National
UniversityofSingapore
DistinguishedProfessor
Dean,CollegeofFutureTransportation
Chang’anUniversity
Xi’an,China
Foreword by Feng Xiao
Thisisanauthoritativeacademicmonographontheminingmethodsandapplications
ofmulti-sourcetrafficbigdata.Existingresearchontrafficbigdataanalysisoften
adopts data-driven methods, which leads us to only know what it is, but not why.
Thisbookattemptstolinktrafficphenomenatothereasonsbehinditbasedontraffic
big data, which can be a good reference for future research. I believe that every
researcherinthefieldoftransportationcangetenlightenmentfromthisbook.
FengXiao
Professor,SchoolofBusiness
Administration
SouthwesternUniversityofFinance
andEconomics
Chengdu,China
ix
Preface
Big data plays an increasingly important role in urban transportation. The core is
to provide a new “traffic information environment” and strong data support for a
comprehensiveandaccurateevaluationoftheoperationofthetransportationsystem.
Howtoovercomethescarcityofavarietyofempiricaldataandlimiteddatasources,
diagnosing the bottleneck problems hindering urban transportation development,
thusestablishingappropriateplanningmethodsunderthebackgroundofmulti-source
informationisnotonlythefrontiertopicintransportationplanning,butalsothefuture
developmentdirectionoftheindustry.
Inaddition,theseparationofdifferenturbanlandusesinspaceistherootoftravel
demand.Inturn,urbantransportationsystemisanimportantfactoraffectingurban
land use. There is a very complex interaction and restriction relationship between
urbantransportationandbuiltenvironment(landuse),whichconstitutesa“spiral”
interactionmechanismamongurbanbuiltenvironment,accessibility,transportation
facilities,andtraveldemand.However,theexistingtrafficbigdataanalysismodels
andmethodsoftenignoretheinteractivefeedbackrelationshipbetweenurbanbuilt
environment and travel behavior, so that the existing research results often have
certainlimitationsandcannotexploreandfindtherootcausebehindthephenomenon.
Thisbookisuniqueinthatitstartsfromtherelationshipbetweenurbanbuiltenvi-
ronmentandtravelbehaviorandfocusesonanalyzingtheoriginoftrafficphenomena
behindthedatathroughmulti-sourcetrafficbigdata,whichmakesthebookdifferent
fromthepreviousdata-driventrafficbigdataanalysisliteratures.
The book focuses on understanding, estimating, predicting, and optimizing
mobilitypatterns.Readerscanfindmulti-sourcetrafficbigdataprocessingmethods,
relatedstatisticalanalysismodels,andpracticalcaseapplicationsfromthisbook.
Thisbookbridgesthegapbetweentrafficbigdata,statisticalanalysismodels,and
mobilitypatternanalysiswithasystematicinvestigationoftrafficbigdata’simpact
onmobilitypatternsandurbanplanning.
xi
xii Preface
Academicandpracticingplannerswouldbeinterestedinreadingthebook.Itcan
also be used as a reference for students majoring in traffic engineering, urban and
regionalplanning,andtransportationplanningandmanagement.
Dalian,China ShaopengZhong
Xi’an,China Daniel(Jian)Sun