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Exploring regional sustainable commuting patterns based on dockless bike-sharing data and POI data

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  • Wang, Ruoxuan
  • Wu, Jianping
  • Qi, Geqi

Abstract

The dockless bike-sharing system, which provides a sustainable transportation mode available anytime and anywhere, has played an increasingly important role in urban daily commuting. However, urban structure complexity and job-housing imbalances present difficulties in understanding the characteristics of bicycling travel demand, especially with respect to the commuting patterns in megacities. Moreover, the functional properties of a region implying the different travel purposes of citizens have rarely been considered and incorporated into pattern extraction and analysis. Based on dockless bike-sharing data and point of interest (POI) data, this paper designed a regional indicator system with 24 features and established a set of random forest models to evaluate feature significance for different commuting purposes. The city of Shanghai was used as a case study, and sustainable commuting patterns were regionally extracted. The regional purpose composition can also be effectively derived with the average optimal accuracy of RMSE = 0.0734. The analysis results can help traffic operators and managers not only discover the regional commuting patterns of bike-sharing mobility but also interpret the patterns more specifically and purposefully. In terms of practical applications, the research could benefit the scientific and reasonable allocation of shared bicycles at the regional level to improve individual travel efficiency and comprehensively increase the attractiveness of such a sustainable travel mode.

Suggested Citation

  • Wang, Ruoxuan & Wu, Jianping & Qi, Geqi, 2022. "Exploring regional sustainable commuting patterns based on dockless bike-sharing data and POI data," Journal of Transport Geography, Elsevier, vol. 102(C).
    • Handle: RePEc:eee:jotrge:v:102:y:2022:i:c:s0966692322001181
    DOI: 10.1016/j.jtrangeo.2022.103395
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    1. Cai, Xiao & Gu, Xinyue & Silm, Siiri & Hadachi, Amnir & Jin, Tanhua & Witlox, Frank, 2025. "Differences in bike-sharing usage and its associations with station-surrounding characteristics: A multi-group analysis using machine learning techniques," Journal of Transport Geography, Elsevier, vol. 125(C).
    2. Feng, Jiajia & Liang, Yuebing & Hao, Qi & Xu, Ke & Qiu, Waishan, 2024. "Comparing effectiveness of point of interest data and land use data in theft crime modelling: A case study in Beijing," Land Use Policy, Elsevier, vol. 147(C).
    3. Gong, Mengjie & Xin, Rui & Yang, Jian & Wang, Jiaoe & Li, Tingting & Zhang, Yujuan, 2024. "Spatio-temporal dynamics and recovery of commuting activities via bike-sharing around COVID-19: A case study of New York," Journal of Transport Geography, Elsevier, vol. 121(C).

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