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Analysis on spatiotemporal urban mobility based on online car-hailing data

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  • Zhang, Bin
  • Chen, Shuyan
  • Ma, Yongfeng
  • Li, Tiezhu
  • Tang, Kun

Abstract

As an emerging travel mode, online car-hailing plays an increasingly important role in people's daily travel. Car-hailing data provide a new source to study human mobility in urban areas. This study focuses on identifying the distribution of regions with high travel intensity and the correlation between travel intensity and points of interest (POIs), based on the online car-hailing data collected in Chengdu, China. Firstly, the whole city area was divided into 16,100 uniform blocks and the number of pick-up and drop-off activities in each block was counted. Then, all POIs were categorized into 13 types and the number of different types of POIs in each block was counted. On this basis, the grade of travel intensity and POIs density in each block was identified according to the number of travel activities and POIs respectively. Finally, the correlation between the travel intensity and the POIs density was explored with ordered logistic regression. Experiment results showed that regions with high travel intensity are mainly distributed within the Second Ring Road, while those in the suburbs of city are usually the large transportation hubs, such as airports and train stations. Different types of POIs have different impacts on the online car-hailing travel intensity, and the density of traffic facilities has the greatest impact, including pick-up and drop-off, followed by density of scenic spot. The densities of service facilities and sports facility have an impact on the intensity of pick-up, while the impact on the intensity of drop-off is not significant. The density of company has no significant impact on the intensity of neither pick-up nor drop-off. These findings can contribute to a better understanding of online car-hailing travel activities and their relation with the urban space, and can provide useful information for urban planning and location-based services.

Suggested Citation

  • Zhang, Bin & Chen, Shuyan & Ma, Yongfeng & Li, Tiezhu & Tang, Kun, 2020. "Analysis on spatiotemporal urban mobility based on online car-hailing data," Journal of Transport Geography, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:jotrge:v:82:y:2020:i:c:s0966692319301103
    DOI: 10.1016/j.jtrangeo.2019.102568
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    as
    1. Camille Roth & Soong Moon Kang & Michael Batty & Marc Barthélemy, 2011. "Structure of Urban Movements: Polycentric Activity and Entangled Hierarchical Flows," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-8, January.
    2. Yang, Zhisen & Yang, Zaili & Yin, Jingbo, 2018. "Realising advanced risk-based port state control inspection using data-driven Bayesian networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 38-56.
    3. Liu, Xi & Gong, Li & Gong, Yongxi & Liu, Yu, 2015. "Revealing travel patterns and city structure with taxi trip data," Journal of Transport Geography, Elsevier, vol. 43(C), pages 78-90.
    4. Zhao, Pengjun & Li, Peilin, 2019. "Travel satisfaction inequality and the role of the urban metro system," Transport Policy, Elsevier, vol. 79(C), pages 66-81.
    5. Hahn, Jin-Seok & Kim, Hyoung-Chul & Kim, Joon-Ki & Ulfarsson, Gudmundur F., 2016. "Trip making of older adults in Seoul: Differences in effects of personal and household characteristics by age group and trip purpose," Journal of Transport Geography, Elsevier, vol. 57(C), pages 55-62.
    6. Zhang, Xiaohu & Xu, Yang & Tu, Wei & Ratti, Carlo, 2018. "Do different datasets tell the same story about urban mobility — A comparative study of public transit and taxi usage," Journal of Transport Geography, Elsevier, vol. 70(C), pages 78-90.
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    5. Jincheng Wang & Qunqi Wu & Feng Mao & Yilong Ren & Zilin Chen & Yaqun Gao, 2021. "Influencing Factor Analysis and Demand Forecasting of Intercity Online Car-Hailing Travel," Sustainability, MDPI, vol. 13(13), pages 1-19, July.
    6. Ma, Yongfeng & Zhang, Ziyu & Chen, Shuyan & Pan, Yingjiu & Hu, Shuqin & Li, Zeyang, 2021. "Investigating the impact of spatial-temporal grid size on the microscopic forecasting of the inflow and outflow gap in a free-floating bike-sharing system," Journal of Transport Geography, Elsevier, vol. 96(C).
    7. Chen, Chao & Feng, Tao & Ding, Chuan & Yu, Bin & Yao, Baozhen, 2021. "Examining the spatial-temporal relationship between urban built environment and taxi ridership: Results of a semi-parametric GWPR model," Journal of Transport Geography, Elsevier, vol. 96(C).
    8. Xueyu Mi & Chunjiao Dong & Ning Li & Yi Lin & Chunfu Shao & Bosong Fan, 2021. "Operating Safety Evaluation of Battery-Electric Taxi Based on Spatio-Temporal Speed Parameters," Sustainability, MDPI, vol. 13(23), pages 1-10, December.
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    10. Chen, Qingqing & Chuang, I-Ting & Poorthuis, Ate, 2021. "Entangled footprints: Understanding urban neighbourhoods by measuring distance, diversity, and direction of flows in Singapore," SocArXiv b2y75, Center for Open Science.
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