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Revealing intra-urban travel patterns and service ranges from taxi trajectories

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  • Zhang, Shen
  • Tang, Jinjun
  • Wang, Haixiao
  • Wang, Yinhai
  • An, Shi

Abstract

As an important transport tool, taxi plays a significant role to meet travel demand in urban city. Understanding the travel patterns of taxis is important for addressing many urban sustainability challenges. Previous research has primarily focused on examining the statistical properties of taxi trips to characterize travel patterns, while it may be more appropriate to explore taxi service strategies on seasonal, weekly or daily time scale. Therefore, intra-urban taxi mobility is investigated by examining taxi trajectory data that were collected in Harbin, China, while 12-week corresponding to 12-month is chosen as the sampling period in our study. The multivariate spatial point pattern analysis is firstly adopted to characterize and model the spatial dependence, and infer significant positive spatial relationships between the picked up points (PUPs) and the dropped off points (DOPs). Secondly, the points of interest (POIs) are identified from DOPs using the emerging hot spot detection technique, then the taxi services and movement patterns surrounding POIs are further examined in details. Moreover, our study builds on and extends the existing work to examine the statistical regularities of trip distances, and we also validate and quantify the impacts posed by airport trips on the distance distributions. Finally, the movement-based kernel density estimation (MKDE) method is proposed to estimate taxis' service ranges within three isopleth levels (50, 75 and 95%) between summer/weekday and winter/weekend from taxi driver's perspective, and season as well as temperature factors are identified as the significant effect within certain service range levels. These results are expected to enhance current urban mobility research and suggest some interesting avenues for future research.

Suggested Citation

  • Zhang, Shen & Tang, Jinjun & Wang, Haixiao & Wang, Yinhai & An, Shi, 2017. "Revealing intra-urban travel patterns and service ranges from taxi trajectories," Journal of Transport Geography, Elsevier, vol. 61(C), pages 72-86.
    • Handle: RePEc:eee:jotrge:v:61:y:2017:i:c:p:72-86
    DOI: 10.1016/j.jtrangeo.2017.04.009
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    References listed on IDEAS

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    3. Sun, Lu & Liu, Xinmin, 2023. "Mining of interactions between travel demand and land use mixture using multi-source data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 629(C).
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    6. Helai Huang & Jialing Wu & Fang Liu & Yiwei Wang, 2020. "Measuring Accessibility Based on Improved Impedance and Attractive Functions Using Taxi Trajectory Data," Sustainability, MDPI, vol. 13(1), pages 1-23, December.
    7. Dong, Shuoxuan & Zhou, Yang & Chen, Tianyi & Li, Shen & Gao, Qiantong & Ran, Bin, 2021. "An integrated Empirical Mode Decomposition and Butterworth filter based vehicle trajectory reconstruction method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    8. Wong, R.C.P. & Szeto, W.Y., 2018. "An alternative methodology for evaluating the service quality of urban taxis," Transport Policy, Elsevier, vol. 69(C), pages 132-140.
    9. Rongxiang Su & Zhixiang Fang & Ningxin Luo & Jingwei Zhu, 2018. "Understanding the Dynamics of the Pick-Up and Drop-Off Locations of Taxicabs in the Context of a Subsidy War among E-Hailing Apps," Sustainability, MDPI, vol. 10(4), pages 1-24, April.
    10. Xia, Dawen & Jiang, Shunying & Yang, Nan & Hu, Yang & Li, Yantao & Li, Huaqing & Wang, Lin, 2021. "Discovering spatiotemporal characteristics of passenger travel with mobile trajectory big data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    11. Yang, Qiaoli & Shi, Zhongke, 2018. "The evolution process of queues at signalized intersections under batch arrivals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 413-425.
    12. Tang, Jinjun & Zhang, Shen & Chen, Xinqiang & Liu, Fang & Zou, Yajie, 2018. "Taxi trips distribution modeling based on Entropy-Maximizing theory: A case study in Harbin city—China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 430-443.
    13. Yanyan Chen & Zheng Zhang & Tianwen Liang, 2019. "Assessing Urban Travel Patterns: An Analysis of Traffic Analysis Zone-Based Mobility Patterns," Sustainability, MDPI, vol. 11(19), pages 1-15, October.
    14. Lei Zhang & Guoxing Zhang & Zhizheng Liang & Ekene Frank Ozioko, 2018. "Multi-features taxi destination prediction with frequency domain processing," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-22, March.
    15. Foissaud, Nicolas & Gioldasis, Christos & Tamura, Shun & Christoforou, Zoi & Farhi, Nadir, 2022. "Free-floating e-scooter usage in urban areas: A spatiotemporal analysis," Journal of Transport Geography, Elsevier, vol. 100(C).
    16. Zhang, Shen & Liu, Xin & Tang, Jinjun & Cheng, Shaowu & Qi, Yong & Wang, Yinhai, 2018. "Spatio-temporal modeling of destination choice behavior through the Bayesian hierarchical approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 537-551.
    17. Yuhui Zhao & Xinyan Zhu & Wei Guo & Bing She & Han Yue & Ming Li, 2019. "Exploring the Weekly Travel Patterns of Private Vehicles Using Automatic Vehicle Identification Data: A Case Study of Wuhan, China," Sustainability, MDPI, vol. 11(21), pages 1-17, November.
    18. Jiayu Qin & Gang Mei & Lei Xiao, 2020. "Building the Traffic Flow Network with Taxi GPS Trajectories and Its Application to Identify Urban Congestion Areas for Traffic Planning," Sustainability, MDPI, vol. 13(1), pages 1-18, December.

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