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Investigation on structural and spatial characteristics of taxi trip trajectory network in Xi’an, China

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  • Yang, Yu
  • He, Ze
  • Song, Zouying
  • Fu, Xin
  • Wang, Jianwei

Abstract

An urban taxi transport network provides convenience for urban residents, and is also an important part of interconnecting suburbs and inner cities. Based on the GPS trajectory data of 14, 139 taxis, a kind of urban trip complex network was constructed. The clustering coefficient, average shortest path, vertex intensity, network density, K-core and other indexes that reflect the complex network characteristics of the taxis were studied, and the topological properties and geographical characteristics were analyzed. Xi’an’s taxi transport network has small-world characteristics and group characteristics, and that the average taxi trip is mainly 5.756 km. Distribution of vertex intensity in the traffic zones (minimum spatial analysis unit of transport network study) of the taxi network is uneven, which presents “fewer vertexes in large zones but more in the small ones, ” characteristic. The traffic zones with high cohesion are shaped as British Union Jack. Obviously, the research results indicate the interaction relationship between the topology structure and spatial differentiation of taxi trip trajectory network, and reveals urban resident activities’ spatial characteristics, movement rules and the mutual influence of urban functions’ spatial layout and resident activities, which may serve as references for those trying to optimize the taxi transport network and taxi operation management.

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  • Yang, Yu & He, Ze & Song, Zouying & Fu, Xin & Wang, Jianwei, 2018. "Investigation on structural and spatial characteristics of taxi trip trajectory network in Xi’an, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 755-766.
    • Handle: RePEc:eee:phsmap:v:506:y:2018:i:c:p:755-766
    DOI: 10.1016/j.physa.2018.04.096
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    References listed on IDEAS

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    Cited by:

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    2. Yang Yang & Zhenzhou Yuan & Xin Fu & Yinhai Wang & Dongye Sun, 2019. "Optimization Model of Taxi Fleet Size Based on GPS Tracking Data," Sustainability, MDPI, vol. 11(3), pages 1-19, January.
    3. Li, Ze-Tao & Nie, Wei-Peng & Cai, Shi-Min & Zhao, Zhi-Dan & Zhou, Tao, 2023. "Exploring the topological characteristics of urban trip networks based on taxi trajectory data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    4. Lyu, Tao & Wang, Yuanqing & Ji, Shujuan & Feng, Tao & Wu, Zhouhao, 2023. "A multiscale spatial analysis of taxi ridership," Journal of Transport Geography, Elsevier, vol. 113(C).
    5. Hu, Beibei & Xia, Xuanxuan & Sun, Huijun & Dong, Xianlei, 2019. "Understanding the imbalance of the taxi market: From the high-quality customer’s perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    6. Peng, Peng & Yang, Yu & Cheng, Shifen & Lu, Feng & Yuan, Zimu, 2019. "Hub-and-spoke structure: Characterizing the global crude oil transport network with mass vessel trajectories," Energy, Elsevier, vol. 168(C), pages 966-974.
    7. Xu Li & Bin Lv & Binke Lang & Qixiang Chen, 2022. "Exploring the Cascading Failure in Taxi Transportation Networks," Sustainability, MDPI, vol. 14(20), pages 1-14, October.

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