Public Transit Based Commuting Travel Time Impact Models - Details

Author：

Liu, Z. (Liu, Z..) | Weng, J. (Weng, J..) | Tu, Q. (Tu, Q..) | Zhang, L. (Zhang, L..)

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Scopus

Abstract：

Commuting　travel　is　the　main　part　of　urban　residents＇　travel.　Based　on　massive　public　transport　multi-source　data,　the　public　transport　commuting　travel　time　was　extracted.　According　to　the　supply　and　demand　theory　of　urban　transportation,　a　set　of　influencing　factors　for　commuting　travel　time　was　established.　The　correlation　analysis　was　applied　to　identify　the　influencing　factors　of　the　model,　and　the　preliminary　analysis　result　showed　that　subway　network　has　a　most　obvious　influence　on　the　commuting　time　of　public　transportation.　Considering　the　different　situations　of　traffic　zones　with　or　without　subway　station　the　paper　separately　proposed　two　influence　models　of　commuting　travel　time,　to　analyze　the　influence　rules　of　various　factors　on　commuting　travel　time　quantitatively.　The　conclusion　provides　effective　data　support　for　the　development　of　urban　public　transportation.　©　2018　American　Society　of　Civil　Engineers.

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[ 1 ] [Liu, Z.]Beijing Key Laboratory of Traffic Engineering, Beijing Univ. of Technology, Beijing, 100124, China
[ 2 ] [Liu, Z.]Qingdao City Planning and Design Institute, Qingdao, 266000, China
[ 3 ] [Weng, J.]Beijing Key Laboratory of Traffic Engineering, Beijing Univ. of Technology, Beijing, 100124, China
[ 4 ] [Weng, J.]Qingdao City Planning and Design Institute, Qingdao, 266000, China
[ 5 ] [Tu, Q.]Beijing Key Laboratory of Traffic Engineering, Beijing Univ. of Technology, Beijing, 100124, China
[ 6 ] [Tu, Q.]Qingdao City Planning and Design Institute, Qingdao, 266000, China
[ 7 ] [Zhang, L.]Beijing Key Laboratory of Traffic Engineering, Beijing Univ. of Technology, Beijing, 100124, China
[ 8 ] [Zhang, L.]Qingdao City Planning and Design Institute, Qingdao, 266000, China

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Source ：

CICTP 2018: Intelligence, Connectivity, and Mobility - Proceedings of the 18th COTA International Conference of Transportation Professionals

Year： 2018

Page： 901-912

Language： English

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ESI Highly Cited Papers on the List： 0 Unfold All

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30 Days PV： 5

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建筑与城市规划学院本学院/部未明确归属的数据

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