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Analysis of Spatial and Temporal Characteristics of Citizens’ Mobility Based on E-Bike GPS Trajectory Data in Tengzhou City, China

Author

Listed:
  • Yixiao Li

    (Chinese Academy of Surveying and Mapping, Beijing 100830, China
    Beijing No.4 High School International Campus, Beijing 100031, China)

  • Zhaoxin Dai

    (Chinese Academy of Surveying and Mapping, Beijing 100830, China)

  • Lining Zhu

    (Chinese Academy of Surveying and Mapping, Beijing 100830, China)

  • Xiaoli Liu

    (Chinese Academy of Surveying and Mapping, Beijing 100830, China)

Abstract

Environmentally friendly shared transit systems have become ubiquitous at present. As a result, analyzing the ranges and tracts of human activities and gatherings based on bike share data is scientifically useful. This paper investigates the spatial and temporal travel characteristics of citizens based on real-time-extracted electric bikes (e-bikes) Global Positioning System (GPS) data from May to July in 2018 in the central area of Tengzhou City, Shandong Province, China. The research is conducive for the exploration of citizens’ changes in mobility behaviors, for the analysis of relationships between mobility changes and environmental or other possible factors, and for advancing policy proposals. The main conclusions of the study are as follows. First, in general, citizens’ travelling is featured by rides that are less than 10 min, shorter than 5 km, and with a speed between 5 km/h and 20 km/h. Second, in terms of temporal characteristics, monthly e-bike usage and citizens’ mobility are positively correlated with temperature in May and negatively correlated with temperature in July; an overall negative correlation is also manifested between the e-bike usage (mobility) and air quality index; daily usage reaches a trough on Tuesday and a peak on Friday, indicating the extent of mobility on respective days; e-bike usage and human outdoor behaviors are significantly lowered in rainy weather than in sunny weather; hourly rides reach a peak at 18:00 (more human activities) and a trough at 2:00 (less activities), and average hourly riding speed maximizes at 5:00 and minimizes around 8:00 and 17:00. Third, for spatial characteristics, destinations (D points) during morning rush hour and regions where e-bikes are densely employed are concentrated mainly in mid-north and middle parts of the central area (major human gatherings), and the rides have a diffusing pattern; e-bike origin–destination (O–D) trajectories radiate mostly towards the mid-north and the east during evening rush hour. In addition, 9.4% of the total trips to work areas during morning rush hour represent spillover commuting, indicating that separations between jobs and residential are not severe in the central area of Tengzhou City and commuting is relatively convenient.

Suggested Citation

  • Yixiao Li & Zhaoxin Dai & Lining Zhu & Xiaoli Liu, 2019. "Analysis of Spatial and Temporal Characteristics of Citizens’ Mobility Based on E-Bike GPS Trajectory Data in Tengzhou City, China," Sustainability, MDPI, vol. 11(18), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:18:p:5003-:d:266806
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    References listed on IDEAS

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    1. Faghih-Imani, Ahmadreza & Eluru, Naveen, 2016. "Incorporating the impact of spatio-temporal interactions on bicycle sharing system demand: A case study of New York CitiBike system," Journal of Transport Geography, Elsevier, vol. 54(C), pages 218-227.
    2. Cools, Mario & Creemers, Lieve, 2013. "The dual role of weather forecasts on changes in activity-travel behavior," Journal of Transport Geography, Elsevier, vol. 28(C), pages 167-175.
    3. Xiaolu Zhou, 2015. "Understanding Spatiotemporal Patterns of Biking Behavior by Analyzing Massive Bike Sharing Data in Chicago," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-20, October.
    4. Kang‐Rae Ma & David Banister, 2006. "Excess Commuting: A Critical Review," Transport Reviews, Taylor & Francis Journals, vol. 26(6), pages 749-767, May.
    5. Zhou, Jiangping & Murphy, Enda & Long, Ying, 2014. "Commuting efficiency in the Beijing metropolitan area: an exploration combining smartcard and travel survey data," Journal of Transport Geography, Elsevier, vol. 41(C), pages 175-183.
    6. Corcoran, Jonathan & Li, Tiebei & Rohde, David & Charles-Edwards, Elin & Mateo-Babiano, Derlie, 2014. "Spatio-temporal patterns of a Public Bicycle Sharing Program: the effect of weather and calendar events," Journal of Transport Geography, Elsevier, vol. 41(C), pages 292-305.
    7. Chengming Li & Zhaoxin Dai & Weixiang Peng & Jianming Shen, 2019. "Green Travel Mode: Trajectory Data Cleansing Method for Shared Electric Bicycles," Sustainability, MDPI, vol. 11(5), pages 1-14, March.
    8. Xie, Xiao-Feng & Wang, Zunjing Jenipher, 2018. "Examining travel patterns and characteristics in a bikesharing network and implications for data-driven decision supports: Case study in the Washington DC area," Journal of Transport Geography, Elsevier, vol. 71(C), pages 84-102.
    9. Kou, Zhaoyu & Cai, Hua, 2019. "Understanding bike sharing travel patterns: An analysis of trip data from eight cities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 785-797.
    10. Gimenez-Nadal, José Ignacio & Molina, José Alberto & Velilla, Jorge, 2015. "Excess Commuting in the US: Differences between the Self-Employed and Employees," IZA Discussion Papers 9425, Institute of Labor Economics (IZA).
    11. Mingyang Du & Lin Cheng, 2018. "Better Understanding the Characteristics and Influential Factors of Different Travel Patterns in Free-Floating Bike Sharing: Evidence from Nanjing, China," Sustainability, MDPI, vol. 10(4), pages 1-14, April.
    12. Gavin Fynn Lohry & Alice Yiu, 2015. "Bikeshare in China as a public service: Comparing government‐run and public‐private partnership operation models," Natural Resources Forum, Blackwell Publishing, vol. 0(1), pages 41-52, February.
    13. Jie Bao & Chengcheng Xu & Pan Liu & Wei Wang, 2017. "Exploring Bikesharing Travel Patterns and Trip Purposes Using Smart Card Data and Online Point of Interests," Networks and Spatial Economics, Springer, vol. 17(4), pages 1231-1253, December.
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