Understanding transit ridership in an equity context through a comparison of statistical and machine learning algorithms
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DOI: 10.1016/j.jtrangeo.2022.103482
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Cited by:
- Tanmay Ghosh & Nithin Nagaraj, 2024. "Evaluating the Determinants of Mode Choice Using Statistical and Machine Learning Techniques in the Indian Megacity of Bengaluru," Papers 2401.13977, arXiv.org.
- Tao, Sui & Cheng, Long & He, Sylvia & Witlox, Frank, 2023. "Examining the non-linear effects of transit accessibility on daily trip duration: A focus on the low-income population," Journal of Transport Geography, Elsevier, vol. 109(C).
- Tao Hu & Haoyu Song, 2022. "Analysis of Influencing Factors and Distribution Simulation of Budget Hotel Room Pricing Based on Big Data and Machine Learning from a Spatial Perspective," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
- Tang, Tianli & Gu, Ziyuan & Yang, Yuanxuan & Sun, Haobo & Chen, Siyuan & Chen, Yuting, 2024. "A data-driven framework for natural feature profile of public transport ridership: Insights from Suzhou and Lianyungang, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).
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Keywords
Machine learning; Statistical models; Transit equity; Travel behaviour; Travel mode choice;All these keywords.
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