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Human mobility in space from three modes of public transportation

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  • Jiang, Shixiong
  • Guan, Wei
  • Zhang, Wenyi
  • Chen, Xu
  • Yang, Liu

Abstract

The human mobility patterns have drew much attention from researchers for decades, considering about its importance for urban planning and traffic management. In this study, the taxi GPS trajectories, smart card transaction data of subway and bus from Beijing are utilized to model human mobility in space. The original datasets are cleaned and processed to attain the displacement of each trip according to the origin and destination locations. Then, the Akaike information criterion is adopted to screen out the best fitting distribution for each mode from candidate ones. The results indicate that displacements of taxi trips follow the exponential distribution. Besides, the exponential distribution also fits displacements of bus trips well. However, their exponents are significantly different. Displacements of subway trips show great specialties and can be well fitted by the gamma distribution. It is obvious that human mobility of each mode is different. To explore the overall human mobility, the three datasets are mixed up to form a fusion dataset according to the annual ridership proportions. Finally, the fusion displacements follow the power-law distribution with an exponential cutoff. It is innovative to combine different transportation modes to model human mobility in the city.

Suggested Citation

  • Jiang, Shixiong & Guan, Wei & Zhang, Wenyi & Chen, Xu & Yang, Liu, 2017. "Human mobility in space from three modes of public transportation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 227-238.
    • Handle: RePEc:eee:phsmap:v:483:y:2017:i:c:p:227-238
    DOI: 10.1016/j.physa.2017.04.182
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    References listed on IDEAS

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    Cited by:

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    2. 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).
    3. Songkorn Siangsuebchart & Sarawut Ninsawat & Apichon Witayangkurn & Surachet Pravinvongvuth, 2021. "Public Transport GPS Probe and Rail Gate Data for Assessing the Pattern of Human Mobility in the Bangkok Metropolitan Region, Thailand," Sustainability, MDPI, vol. 13(4), pages 1-29, February.
    4. Xia, Dong & Zheng, Linjiang & Tang, Yi & Cai, Xiaolin & Chen, Li & Sun, Dihua, 2022. "Dynamic traffic prediction for urban road network with the interpretable model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    5. Sui, Yi & Shao, Fengjing & Yu, Xiang & Sun, Rencheng & Li, Shujing, 2019. "Public transport network model based on layer operations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 984-995.
    6. 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.
    7. Zheng, Linjiang & Xia, Dong & Zhao, Xin & Tan, Longyou & Li, Hang & Chen, Li & Liu, Weining, 2018. "Spatial–temporal travel pattern mining using massive taxi trajectory data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 24-41.

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