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The analysis of urban taxi operation efficiency based on GPS trajectory big data

Author

Listed:
  • Dong, Xianlei
  • Zhang, Min
  • Zhang, Shuang
  • Shen, Xinyi
  • Hu, Beibei

Abstract

With the development of social economy, a series of problems such as an imbalance of supply and demand, unreasonable resource allocation and low operation efficiency appear in the urban taxi market. Based on the GPS trajectory data of taxis, this paper calculates and analyzes the operation efficiency of taxis in terms of time and space dimensions in Chengdu, Sichuan Province by using the capacity utilization rate. The results show that, firstly, the weekly variation trend of taxi capacity utilization rate is obviously periodic and its daily variation trend is unbalanced, which is reflected in the morning peak, afternoon peak, evening peak and night peak. The daily variation trend of taxi capacity utilization rate is significantly different between workdays and weekends, and the capacity utilization rate on weekends is much lower. Secondly, there is a spatial imbalance in the distribution of taxi capacity utilization rate. The capacity utilization rate of the main urban district is relatively high, and that of the other urban districts negatively correlates with the economic level and population density. The supply and demand of taxis are balanced within the various functional zones but unbalanced between different functional zones. It is suggested that the relevant departments shall reasonably allocate taxi resources according to the imbalance of taxi capacity utilization rate in time and space to improve the overall operation efficiency, and integrate market mechanism with government regulations to promote the sustainable development of urban taxis.

Suggested Citation

  • Dong, Xianlei & Zhang, Min & Zhang, Shuang & Shen, Xinyi & Hu, Beibei, 2019. "The analysis of urban taxi operation efficiency based on GPS trajectory big data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 528(C).
    • Handle: RePEc:eee:phsmap:v:528:y:2019:i:c:s0378437119308453
    DOI: 10.1016/j.physa.2019.121456
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    Citations

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    Cited by:

    1. Changwei Yuan & Yaxin Duan & Xinhua Mao & Ningyuan Ma & Jiannan Zhao, 2021. "Impact of the mixed degree of urban functions on the taxi travel demand," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-21, March.
    2. Zhao, Zhiyuan & Yao, Wei & Wu, Sheng & Yang, Xiping & Wu, Qunyong & Fang, Zhixiang, 2023. "Identifying the collaborative scheduling areas between ride-hailing and traditional taxi services based on vehicle trajectory data," Journal of Transport Geography, Elsevier, vol. 107(C).
    3. Xuanxuan Xia & Kexin Lin & Yang Ding & Xianlei Dong & Huijun Sun & Beibei Hu, 2020. "Research on the Coupling Coordination Relationships between Urban Function Mixing Degree and Urbanization Development Level Based on Information Entropy," IJERPH, MDPI, vol. 18(1), pages 1-24, December.
    4. Hu, Beibei & Zhang, Shuang & Ding, Yang & Zhang, Min & Dong, Xianlei & Sun, Huijun, 2021. "Research on the coupling degree of regional taxi demand and social development from the perspective of job–housing travels," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 564(C).
    5. 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).
    6. Yang, Qiaoli & Qiao, Zheng & Yang, Bo & Shi, Zhongke, 2021. "Modeling and uncovering the passenger–taxi dynamic queues at taxi station with multiple boarding points using a Markovian environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    7. Yang, Qiaoli & Yang, Bo & Qiao, Zheng & Tang, Min-an & Gao, Fengyang, 2021. "Impact of possible random factors on queue behaviors of passengers and taxis at taxi stand of transport hubs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    8. Yujie Guo & Ying Chen & Yu Zhang, 2024. "Enhancing Demand Prediction: A Multi-Task Learning Approach for Taxis and TNCs," Sustainability, MDPI, vol. 16(5), pages 1-14, March.
    9. Xiong, Ziyue & Jian Li, & Wu, Hangbin, 2021. "Understanding operation patterns of urban online ride-hailing services: A case study of Xiamen," Transport Policy, Elsevier, vol. 101(C), pages 100-118.
    10. Zhai, Wei & Bai, Xueyin & Peng, Zhong-ren & Gu, Chaolin, 2019. "A bottom-up transportation network efficiency measuring approach: A case study of taxi efficiency in New York City," Journal of Transport Geography, Elsevier, vol. 80(C).

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