Using machine learning for direct demand modeling of ridesourcing services in Chicago

Citations

Cited by:

1. Tulio Silveira-Santos & Ana Belén Rodríguez González & Thais Rangel & Rubén Fernández Pozo & Jose Manuel Vassallo & Juan José Vinagre Díaz, 2024. "Were ride-hailing fares affected by the COVID-19 pandemic? Empirical analyses in Atlanta and Boston," Transportation, Springer, vol. 51(3), pages 791-822, June.
2. Yousefzadeh Barri, Elnaz & Farber, Steven & Jahanshahi, Hadi & Beyazit, Eda, 2022. "Understanding transit ridership in an equity context through a comparison of statistical and machine learning algorithms," Journal of Transport Geography, Elsevier, vol. 105(C).
3. Soria, Jason & Stathopoulos, Amanda, 2021. "Investigating socio-spatial differences between solo ridehailing and pooled rides in diverse communities," Journal of Transport Geography, Elsevier, vol. 95(C).
   • Jason Soria & Amanda Stathopoulos, 2021. "Investigating Socio-spatial Differences between Solo Ridehailing and Pooled Rides in Diverse Communities," Papers 2105.03512, arXiv.org.
4. Shao, Qifan & Zhang, Wenjia & Cao, Xinyu & Yang, Jiawen & Yin, Jie, 2020. "Threshold and moderating effects of land use on metro ridership in Shenzhen: Implications for TOD planning," Journal of Transport Geography, Elsevier, vol. 89(C).
5. Zhang, Xiaojian & Zhou, Zhengze & Xu, Yiming & Zhao, Xilei, 2024. "Analyzing spatial heterogeneity of ridesourcing usage determinants using explainable machine learning," Journal of Transport Geography, Elsevier, vol. 114(C).
6. Karimpour, Abolfazl & Hosseinzadeh, Aryan & Kluger, Robert, 2023. "A data-driven approach to estimating dockless electric scooter service areas," Journal of Transport Geography, Elsevier, vol. 109(C).
7. Liu, Jixiang & Xiao, Longzhu, 2023. "Non-linear relationships between built environment and commuting duration of migrants and locals," Journal of Transport Geography, Elsevier, vol. 106(C).
8. Du, Qiang & Zhou, Yuqing & Huang, Youdan & Wang, Yalei & Bai, Libiao, 2022. "Spatiotemporal exploration of the non-linear impacts of accessibility on metro ridership," Journal of Transport Geography, Elsevier, vol. 102(C).
9. Jason Soria & Shelly Etzioni & Yoram Shiftan & Amanda Stathopoulos & Eran Ben-Elia, 2022. "Microtransit adoption in the wake of the COVID-19 pandemic: evidence from a choice experiment with transit and car commuters," Papers 2204.01974, arXiv.org.
10. Yang, Jiawen & Cao, Jason & Zhou, Yufei, 2021. "Elaborating non-linear associations and synergies of subway access and land uses with urban vitality in Shenzhen," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 74-88.
11. Tranos, Emmanouil & Incera, Andre Carrascal & Willis, George, 2022. "Using the web to predict regional trade flows: data extraction, modelling, and validation," OSF Preprints 9bu5z, Center for Open Science.
12. Xu, Yiming & Yan, Xiang & Liu, Xinyu & Zhao, Xilei, 2021. "Identifying key factors associated with ridesplitting adoption rate and modeling their nonlinear relationships," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 170-188.
13. Alsaleh, Nael & Farooq, Bilal, 2021. "Interpretable data-driven demand modelling for on-demand transit services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 1-22.
14. Peikun Li & Quantao Yang & Wenbo Lu, 2024. "Nonlinear Relationship of Multi-Source Land Use Features with Temporal Travel Distances at Subway Station Level: Empirical Study from Xi’an City," Land, MDPI, vol. 13(7), pages 1-16, July.
15. Rico Krueger & Michel Bierlaire & Prateek Bansal, 2022. "A Data Fusion Approach for Ride-sourcing Demand Estimation: A Discrete Choice Model with Sampling and Endogeneity Corrections," Papers 2212.02178, arXiv.org.
16. Morteza Taiebat & Elham Amini & Ming Xu, 2022. "Sharing Behavior in Ride-hailing Trips: A Machine Learning Inference Approach," Papers 2201.12696, arXiv.org.
17. Lin Zhang & Suhong Zhou & Lanlan Qi & Yue Deng, 2022. "Nonlinear Effects of the Neighborhood Environments on Residents’ Mental Health," IJERPH, MDPI, vol. 19(24), pages 1-17, December.
18. Zhang, Xiaojian & Zhao, Xilei, 2022. "Machine learning approach for spatial modeling of ridesourcing demand," Journal of Transport Geography, Elsevier, vol. 100(C).
19. Li, Shengxiao(Alex) & Zhai, Wei & Jiao, Junfeng & Wang, Chao (Kenneth), 2022. "Who loses and who wins in the ride-hailing era? A case study of Austin, Texas," Transport Policy, Elsevier, vol. 120(C), pages 130-138.
20. Du, Mingyang & Cheng, Lin & Li, Xuefeng & Liu, Qiyang & Yang, Jingzong, 2022. "Spatial variation of ridesplitting adoption rate in Chicago," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 13-37.
21. Wang, Sicheng & Du, Rui & Lee, Annie S., 2024. "Ridesourcing regulation and traffic speeds: A New York case," Journal of Transport Geography, Elsevier, vol. 116(C).
22. Kim, Sung Hoo & Mokhtarian, Patricia L., 2024. "Latent class choice models with an error structure: Investigating potential unobserved associations between latent segmentation and behavior generation," Journal of choice modelling, Elsevier, vol. 53(C).
23. Wang, Sicheng & Noland, Robert B., 2021. "What is the elasticity of sharing a ridesourcing trip?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 284-305.
24. Tulio Silveira-Santos & Thais Rangel & Juan Gomez & Jose Manuel Vassallo, 2024. "Forecasting Moped Scooter-Sharing Travel Demand Using a Machine Learning Approach," Sustainability, MDPI, vol. 16(13), pages 1-20, June.
25. Yang, Hongtai & Zheng, Rong & Li, Xuan & Huo, Jinghai & Yang, Linchuan & Zhu, Tong, 2022. "Nonlinear and threshold effects of the built environment on e-scooter sharing ridership," Journal of Transport Geography, Elsevier, vol. 104(C).
26. Wang, Sicheng & Huang, Xiao & Shen, Qing, 2024. "Disparities in resilience and recovery of ridesourcing usage during COVID-19," Journal of Transport Geography, Elsevier, vol. 114(C).