Detecting vehicle traffic patterns in urban environments using taxi trajectory intersection points

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    摘要 Detectinganddescribingmovementofvehiclesinestablishedtransportationinfrastructuresisanimportanttask.Ithelpstopredictperiodicaltrafficpatternsforoptimizingtrafficregulationsandextendingthefunctionsofestablishedtransportationinfrastructures.Thedetectionoftrafficpatternsconsistsnotonlyofanalysesofarrangementpatternsofmultiplevehicletrajectories,butalsooftheinspectionoftheembeddedgeographicalcontext.Inthispaper,weintroduceamethodforintersectingvehicletrajectoriesandextractingtheirintersectionpointsforselectedrushhoursinurbanenvironments.Thosevehicletrajectoryintersectionpoints(TIP)arefrequentlyvisitedlocationswithinurbanroadnetworksandaresubsequentlyformedintodensity-connectedclusters,whicharethenrepresentedaspolygons.Forrepresentingtemporalvariationsofthecreatedpolygons,weenrichthesewithvehicletrajectoriesofothertimesofthedayandadditionalroadnetworkinformation.Inacasestudy,wetestourapproachonmassivetaxiFloatingCarData(FCD)fromShanghaiandroadnetworkdatafromtheOpenStreetMap(OSM)project.ThefirsttestresultsshowstrongcorrelationswithperiodicaltrafficeventsinShanghai.Basedontheseresults,wereasonouttheusefulnessofpolygonsrepresentingfrequentlyvisitedlocationsforanalysesinurbanplanningandtrafficengineering.
    机构地区 不详
    出版日期 2017年04月14日(中国Betway体育网页登陆平台首次上网日期,不代表论文的发表时间)
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