地铁乘车需求的影响因素外文文献翻译.docx
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地铁乘车需求的影响因素外文文献翻译.docx
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地铁乘车需求的影响因素外文文献翻译
地铁乘车需求的影响因素外文翻译2019
英文
DoesdemandforsubwayridershipinManhattandependontherainfallevents?
ShirinNajafabadi,AliHamidi
Abstract
TheNortheastUnitedStates,particularlyNewYorkStatehasexperiencedanincreaseinextremedailyprecipitationduringthepast50years.Recenteventssuchas Hurricane IreneandSuperstormSandy,haverevealedvulnerabilitytotheintenseprecipitationwithinthetransportationsector.InthescaleofNewYorkCity,wheretransitsystemisthemostdominantmodeoftransportationanddailymobilityofmillionsofpassengersdependsonit,anydisruptioninthetransitservicewouldresultingridlocksandmassivedelays.Toassesstheimpactsofrainfallonthesubwayridership,wemergedhighresolutionradarrainfallandsubwayridershipdatatoconductadetailedanalysisforeachofthe116subwaystationsattheboroughofManhattan.Theanalysisiscarriedoutonbothhourlyanddailyresolutionlevel,whereaspatial-temporalBayesianmulti-levelregressionmodelisusedtocapturetheunderlyingdependencybetweentheparameters.Theestimationresultsareobtainedthrough MarkovChainMonteCarlo samplingmethod.Theresultsfordailyanalysisindicatethatduringweekdays,transitridershipinthestationslocatedincommercialzonesarelesssensitivetotherainfallcomparedtotheonesinresidentialzones.
Keywords:
Bayesianmulti-levelregressionmodel,Subwayridership,MCMCsampling,Radarrainfall
Largecitiesaroundtheworldrelyonpublictransportationinfrastructuretomaintainagoodlevelofserviceandtoincreasemobilityandeconomicproductivity.Inyear2015,morethan10.7billiontransittripswerereportedintheUnitedStates(Matthew Dickens,2016).AccordingtotheAmericanPublicTransportationAssociation(APTA)every$1investedinpublictransportationgeneratesapproximately$4ineconomicreturnsthroughincreasedemploymentrate,businesssales,andenhancedpropertyvalues.Thefullreturnoninvestmentfortransportationsystemscanonlybeachievedwhencitiesoptimizeandplanthemaintenanceandgrowthoftheirpublictransportationsystemsthroughbetterrealizationofthedemandlevelfortransportationalongwiththeidentificationofotherinfluentialparameters.Transportationsystemperformancedependsonthegeometryofthenetwork,aswellasotherexternalfactorssuchasaccidents,operationaltearandwear,disruptionsondependentsystems(e.g.thepowergridforsubways),effectiveautomobileregulation,weather,etc.(DeGrangeetal.,2012).TheobjectiveoftheworkpresentedhereistoprogressfurthertheunderstandingofimpactsofrainfalleventsonthesubwayridershiplevelinManhattan,NewYork.
Duringthepast50years,NewYorkStatehasexperiencedanincreaseinextremedailyprecipitation(Hortonetal.,2011)alsoithasbeenheavilyaffectedbyunusualweatherpatternssuchas Hurricane IreneandSuperstormSandy.InNewYorkCity,withover4Milliondailycommutetrips(MossandQing,2012),theevidenceintheaftermathofextremeweatherpatternshaverevealedvulnerabilitiestointenseprecipitationwithinthetransportationnetwork,yieldingenormouseconomiclossesfortheCity(Brian Tumulty,2012).Abetteranalysistoquantifytheeffectsofvariousweatherconditionsonthetransportationnetworkwouldresultinwell-informedandefficientpolicy-makingdecisions.Literatureinrecognitionofweatherinfluenceontransportationandmobilitycanbeclassifiedintotwogroups.Thefirstgroupoftheliteraturemeasurestheinfluenceofweatherontheperformanceoftransportsystems,whilethesecondgroupstudiesitsbehavioralimpactsoncommunities.Studiesontheinfluenceofweatherovertheperformanceoftransportationnetworksbrushoverlargespectrumoftopicsincludingtrafficflowandroadcapacity(e.g. Kyteetal.,2001; Mashrosetal.,2014; Mazeetal.,2006),infrastructureperformance(e.g. KoetseandRietveld,2009),trafficsafety(e.g. AndreescuandFrost,1998; KoetseandRietveld,2009)andchangesinthequalityofservice(e.g. Coolsetal.,2010; KhattakandDePalma,1997).Thesestudieshaveshedsomelightonthemostimportantaspectsoftransportationsystemsandserviceaffectedbyweather,whicharevaluableinformationforcityadministrators.Ingeneral,traveldemand,trafficsafety,andthetrafficflowarethethreedominantfactorsimpactedbyadverseweather(Mazeetal.,2006; Mashrosetal.,2014).AccordingtoastudyconductedinManchestercityby JaroszweskiandMcNamara(2014),duringrainfallstherateofroadaccidentsincreasesby50%.Meanwhile,throughamorecomprehensivestudycarriedoutbyHofmannandO'Mahonyonvariousperformancemeasuresoftransitsystems,itisrevealedthatbadweatherconditionsdegradethelevelofserviceoftransportationsystems(HofmannandO'Mahony,2005).
Behavioralimpactsofadverseweatherconditionsarestudiedthroughmeasuringitsimpactsonpublictransportdemand(e.g. Guoetal.,2003; Singhaletal.,2014; Zhouetal.,2017),modalshift(e.g. KhattakandDePalma,1997; KoetseandRietveld,2009),androuteanddestinationchoice(e.g. Guoetal.,2003).Astudyshowstheadverseweatherconditionschangesmodechoice,routechoice,anddeparturetimeofautomobilecommutersinBrussels(KhattakandDePalma,1997).Dependingonthetrippurpose,thechangeinthemobilitybehaviorduringdifferentweatherconditionssuchasrain,snow,temperatureandfogwouldvary(Coolsetal.,2010).
Ourstudyfocusesontheinfluenceofrainfallconditionsonsubwayridership.Despiteitsimportance,thedependencybetweenweatherconditionsandtransitridershiphasseldombeeninvestigated,especiallyatafinerresolutionusingspatiallydistributedrainfalldata.Existingstudieshavelimitedtheirattentiontomeasuringtheeffectsofweatherconditionsontheridershipdemandoftransitsystemonaggregatelevel(daily)(e.g., Aranaetal.,2013; Changon,1996; Guoetal.,2003; Kashfietal.,2013; StoverandMccormack,2012).Thesestudieseithermeasurethedirectandindirectimpactsofextremeweatherevents(e.g.,increaseintraveltime,waitingtime,operationaldelaysinthesystem)ontransitusers(Guoetal.,2003; HofmannandO'Mahony,2005). Aranaetal.,2013,analyzedchangesinthenumberofdiscretionarytrips(measuredviadailybusridershipovertheweekends)inresponsetoweatherconditions,wheretheanalysisdemonstratesdecreaseintransitridershipinrainyandwindydaysandincreaseduringhotdays.Trippurposeplaysacrucialroleinridershipdemandunderadverseweathercondition;travelersaremorelikelytopostponeleisuretripswhilemandatoryandworktripsarelesslikelytobedeferred(Coolsetal.,2010; KhattakandDePalma,1997; Mazeetal.,2006). KhattakandDePalma(1997) foundthattransitridershipmayincreaseduringextremeweatherconditionsduetoshiftsfromothermodesoftransportationsuchasautomobiles,walking,andbiking. Table1 presentsmoredetailsonsomeotherstudiesfocusedonassessingtheimpactsofweatherinstabilitiesontransitridership.Amongstthesestudies,itisworthnotingthat StoverandMccormack(2012) foundrainfalltobethemostinfluentialweatherfactor(amongwind,temperature,rainandsnow)leadingtoridershipdecrease. Kashfietal.(2013) conductedadailyanalysisandfoundastrongnegativerelationshipbetweenrainfallandridershipdemand.
Allthesestudiespresentimportantqualitativeandquantitativeinformationontheimpactofweatheronridership,buttheanalysisweremostlyconductedatalargespatialscale-e.g.citylevelaggregatescale-whichisnotnecessarilyaproperscaleforpredictingtransitdemand.Itispreferablefortransitplannerstoanalyzetransitdemandanalysisatthetransitstoplevel,asitisthespatialscalebywhichtransitusersusethesystem(Dilletal.,2013).Usingspatialpointscalelevelisalsousefultoconnecttransitdemandwithlandusecharacteristicsofthestations.Fewstudieswereconductedatadetailedspatialortemporalresolution.Forexample, Singhaletal.(2014),utilizedhourlyridershipdatatoevaluatethesystem-levelridershipinManhattan.However,theydidnotincludespatiallydetailedweatherdataintheiranalyses.Similarly, Zhouetal.(2017) employeddisaggregatelevelsmart-cardinformationtoinvestigatetheimpactofweatherontransitridershipinShenzhen,China,reportingthatrainfallhasanegativeimpactonridershipduringoff-peakhoursandduringweekends.Majorityofthestudiespresentedin Table1 appliedstepwiselinearregressionsmodelsorsimilarstatisticalmodelstoestimateridershipindifferentweatherconditions(e.g. Aranaetal.,2013; Guoetal.,2003; Sabiretal.,2008; Singhaletal.,2014).Furthermore,mostexistingstudiesusedsurvey-baseddatawithrelativelysmallsamplesizeasanexample. Coolsetal.(2010)conductedasurveyovertwoyearswith586respondentsfocusedondailyaggregateridershipwithaggregatedweatherconditionsdatacollectedfromfewweatherstationsinthestudyareaofinterest.Forinstance Singhaletal.(2014),utilizedinformationcollectedfromoneweatherstationintheiranalysis.While Zhouetal.,2017 useddisaggregatelevelsmart-cardinformationandtheirdatasourcewaslimitedtoonemonth.
Afullyfunctionalmodelofweatherpatternsovertransitridershiprequiresanunderstandingofdetailedspatialtemporaldemandvariabilitytopredictfutureridershipdemandinvariousweatherconditions.Thecurrentstudyisastepinthisdirection.ThemaingoalofthispaperistofindouthowsubwayridershipinManhattanischangingduringrainfalleventsandisitbeingimpactedbythetimingofserviceandspatial
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