Examining nonlinearity in population inflow estimation using big data: An empirical comparison of explainable machine learning models
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DOI: 10.1016/j.tra.2023.103743
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- Lv, Huitao & Li, Haojie & Chen, Yanlu & Feng, Tao, 2023. "An origin-destination level analysis on the competitiveness of bike-sharing to underground using explainable machine learning," Journal of Transport Geography, Elsevier, vol. 113(C).
- Zhao, Yingrui & Hu, Songhua & Zhang, Ming, 2024. "Evaluating equitable Transit-Oriented development (TOD) via the Node-Place-People model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 185(C).
- Hu, Songhua & Xiong, Chenfeng & Ji, Ya & Wu, Xin & Liu, Kailun & Schonfeld, Paul, 2024. "Understanding factors influencing user engagement in incentive-based travel demand management program," Transportation Research Part A: Policy and Practice, Elsevier, vol. 186(C).
- Yuhan Zhang & Youqi Wang & Yiru Bai & Ruiyuan Zhang & Xu Liu & Xian Ma, 2023. "Prediction of Spatial Distribution of Soil Organic Carbon in Helan Farmland Based on Different Prediction Models," Land, MDPI, vol. 12(11), pages 1-15, October.
- Peng, Qiao & Bakkar, Yassine & Wu, Liangpeng & Liu, Weilong & Kou, Ruibing & Liu, Kailong, 2024. "Transportation resilience under Covid-19 Uncertainty: A traffic severity analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
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Keywords
Explainable machine learning; Interpretability; Nonlinearity; Mobile device location data; Travel demand;All these keywords.
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