Expanding a(n) (electric) bicycle-sharing system to a new city: Prediction of demand with spatial regression and random forests
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DOI: 10.1016/j.jtrangeo.2020.102692
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References listed on IDEAS
- 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.
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Cited by:
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- Wang, Xudong & Cheng, Zhanhong & Trépanier, Martin & Sun, Lijun, 2021. "Modeling bike-sharing demand using a regression model with spatially varying coefficients," Journal of Transport Geography, Elsevier, vol. 93(C).
- Kyoungok Kim, 2024. "Discovering spatiotemporal usage patterns of a bike-sharing system by type of pass: a case study from Seoul," Transportation, Springer, vol. 51(4), pages 1373-1407, August.
- Shuo Zhang & Li Chen & Yingzi Li, 2021. "Shared Bicycle Distribution Connected to Subway Line Considering Citizens’ Morning Peak Social Characteristics for Urban Low-Carbon Development," Sustainability, MDPI, vol. 13(16), pages 1-19, August.
- Duan, Yimeng & Zhang, Shen & Yu, Zhuoran, 2021. "Applying Bayesian spatio-temporal models to demand analysis of shared bicycle," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
- Imhof, Sebastian & Blättler, Kevin, 2023. "Assessing spatial characteristics to predict DRT demand in rural Switzerland," Research in Transportation Economics, Elsevier, vol. 99(C).
- Yiling Deng & Pengjun Zhao, 2023. "The determinants of shared bike use in China," Transportation, Springer, vol. 50(1), pages 1-23, February.
- Mühlematter, Dominik J. & Wiedemann, Nina & Xin, Yanan & Raubal, Martin, 2024. "Spatially-aware station based car-sharing demand prediction," Journal of Transport Geography, Elsevier, vol. 114(C).
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Keywords
E-bike sharing; Bicycle sharing; Demand prediction; Vehicle sharing; Spatial regression; Random forests;All these keywords.
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