Identifying the collaborative scheduling areas between ride-hailing and traditional taxi services based on vehicle trajectory data - Details

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Abstract：

Traditional　taxi　services　(TTSs)　play　an　important　role　in　satisfying　daily　travel　demands.　The　rapid　growth　of　ride-hailing　services　(RHSs)　has　increased　the　convenience　of　customized　travel.　However,　the　volume　of　empty　ride-hailing　vehicles　has　increased.　Identifying　the　collaborative　scheduling　areas　(CoSAs)　between　RHSs　and　TTSs　can　further　improve　the　efficiency　of　urban　travel　services.　Therefore,　we　propose　a　method　to　identify　CoSAs　based　on　the　travel　demand　and　vehicle　supply　of　TTSs　and　RHSs　derived　from　trajectory　data.　We　first　optimize　and　make　the　temporal　resolution　of　the　trajectories　of　different　types　of　vehicles　uniform　based　on　the　shortest　path　algorithm.　Then,　the　indicators　describing　travel　demand　and　vehicle　supply　are　defined　and　calculated.　Finally,　the　areas　with　a　high　vehicle　supply　of　one　type　and　a　low　vehicle　supply　and　high　travel　demand　of　the　other　type　are　identified　as　CoSAs.　A　dataset　for　Xiamen　Island　indicates　that　the　CoSAs　of　TTSs　to　RHSs　can　provide　potential　routes　to　pick　up　passengers　that　are　41%　and　11%　shorter　than　the　actual　routes　at　9:00　and　18:00,　respectively.　The　constructed　method　can　also　improve　the　CoSA　identification　results.　©　2023　Elsevier　Ltd

Keyword：

Car-hailing Collaborative scheduling area Ride-hailing Taxi Trajectory data

Community：

[ 1 ] [Zhao, Z.]Academy of Digital China (Fujian), Fuzhou University, Fuzhou, 350108, China
[ 2 ] [Zhao, Z.]Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou, 350108, China
[ 3 ] [Zhao, Z.]National Engineering Research Centre of Geospatial Information Technology, Fuzhou University, Fuzhou, 350108, China
[ 4 ] [Zhao, Z.]The Digital Economy Alliance of Fujian, Fuzhou, 350108, China
[ 5 ] [Yao, W.]Academy of Digital China (Fujian), Fuzhou University, Fuzhou, 350108, China
[ 6 ] [Yao, W.]Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou, 350108, China
[ 7 ] [Yao, W.]National Engineering Research Centre of Geospatial Information Technology, Fuzhou University, Fuzhou, 350108, China
[ 8 ] [Wu, S.]Academy of Digital China (Fujian), Fuzhou University, Fuzhou, 350108, China
[ 9 ] [Wu, S.]Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou, 350108, China
[ 10 ] [Wu, S.]National Engineering Research Centre of Geospatial Information Technology, Fuzhou University, Fuzhou, 350108, China
[ 11 ] [Wu, S.]The Digital Economy Alliance of Fujian, Fuzhou, 350108, China
[ 12 ] [Yang, X.]School of Geography and Tourism, Shaanxi Normal University, Xi'an, 710119, China
[ 13 ] [Wu, Q.]Academy of Digital China (Fujian), Fuzhou University, Fuzhou, 350108, China
[ 14 ] [Wu, Q.]Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou, 350108, China
[ 15 ] [Wu, Q.]National Engineering Research Centre of Geospatial Information Technology, Fuzhou University, Fuzhou, 350108, China
[ 16 ] [Fang, Z.]State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China

Reprint 's Address：

[Fang, Z.]State Key Laboratory of Information Engineering in Surveying, China

Email：

zxfang@whu.edu.cn

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Source ：

Journal of Transport Geography

ISSN： 0966-6923

Year： 2023

Volume： 107

5 . 7

JCR@2023

5 . 7 0 0

JCR@2023

ESI HC Threshold：17

JCR Journal Grade：1

CAS Journal Grade：2

Cited Count：

WoS CC Cited Count：

SCOPUS Cited Count： 3

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 0

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