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A comparison of the predictive potential of artificial neural networks and nested logit models for commuter mode choice

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  1. Rasaizadi, Arash & Farzin, Iman & Hafizi, Fateme, 2022. "Machine learning approach versus probabilistic approach to model the departure time of non-mandatory trips," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
  2. Balla, Bhavani Shankar & Sahu, Prasanta K., 2023. "Assessing regional transferability and updating of freight generation models to reduce sample size requirements in national freight data collection program," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
  3. Wang, Shenhao & Wang, Qingyi & Bailey, Nate & Zhao, Jinhua, 2021. "Deep neural networks for choice analysis: A statistical learning theory perspective," Transportation Research Part B: Methodological, Elsevier, vol. 148(C), pages 60-81.
  4. Liu, Yicong & Loa, Patrick & Wang, Kaili & Habib, Khandker Nurul, 2023. "Theory-driven or data-driven? Modelling ride-sourcing mode choices using integrated choice and latent variable model and multi-task learning deep neural networks," Journal of choice modelling, Elsevier, vol. 48(C).
  5. Huang, Yuqiao & Gao, Linjie & Ni, Anning & Liu, Xiaoning, 2021. "Analysis of travel mode choice and trip chain pattern relationships based on multi-day GPS data: A case study in Shanghai, China," Journal of Transport Geography, Elsevier, vol. 93(C).
  6. Ha, Tran Vinh & Asada, Takumi & Arimura, Mikiharu, 2019. "Determination of the influence factors on household vehicle ownership patterns in Phnom Penh using statistical and machine learning methods," Journal of Transport Geography, Elsevier, vol. 78(C), pages 70-86.
  7. Salon, Deborah, 2008. "Neighborhoods, Cars, and Commuting in New York City: A Discrete Choice Approach," Institute of Transportation Studies, Working Paper Series qt1673h3w3, Institute of Transportation Studies, UC Davis.
  8. Liang Tang & Chenfeng Xiong & Lei Zhang, 2015. "Decision tree method for modeling travel mode switching in a dynamic behavioral process," Transportation Planning and Technology, Taylor & Francis Journals, vol. 38(8), pages 833-850, December.
  9. Smeele, Nicholas V.R. & Chorus, Caspar G. & Schermer, Maartje H.N. & de Bekker-Grob, Esther W., 2023. "Towards machine learning for moral choice analysis in health economics: A literature review and research agenda," Social Science & Medicine, Elsevier, vol. 326(C).
  10. Nijkamp, Peter & Reggiani, Aura & Tsang, Wai Fai, 2004. "Comparative modelling of interregional transport flows: Applications to multimodal European freight transport," European Journal of Operational Research, Elsevier, vol. 155(3), pages 584-602, June.
  11. Zheng Zhu & Xiqun Chen & Chenfeng Xiong & Lei Zhang, 2018. "A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choice," Transportation, Springer, vol. 45(5), pages 1499-1522, September.
  12. Shi, Haolun & Yin, Guosheng, 2018. "Boosting conditional logit model," Journal of choice modelling, Elsevier, vol. 26(C), pages 48-63.
  13. Saiyad, Gulnazbanu & Srivastava, Minal & Rathwa, Dipak, 2022. "Exploring determinants of feeder mode choice behavior using Artificial Neural Network: Evidences from Delhi metro," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
  14. Nasrin, Sharmin & Bunker, Jonathan, 2024. "Gender equality through sustainable transport policy," Transport Policy, Elsevier, vol. 149(C), pages 59-79.
  15. Salon, Deborah, 2009. "Neighborhoods, cars, and commuting in New York City: A discrete choice approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(2), pages 180-196, February.
  16. Habibi, Shiva & Sundberg, Marcus & Karlström, Anders, 2013. "An empirical study of predicting car type choice in Sweden using cross-validation and feature-selection," Working papers in Transport Economics 2013:13, CTS - Centre for Transport Studies Stockholm (KTH and VTI), revised 23 Apr 2014.
  17. Sifringer, Brian & Lurkin, Virginie & Alahi, Alexandre, 2020. "Enhancing discrete choice models with representation learning," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 236-261.
  18. Jiajia Zhang & Tao Feng & Harry Timmermans & Zhengkui Lin, 2023. "Improved imputation of rule sets in class association rule modeling: application to transportation mode choice," Transportation, Springer, vol. 50(1), pages 63-106, February.
  19. Han, Yafei & Pereira, Francisco Camara & Ben-Akiva, Moshe & Zegras, Christopher, 2022. "A neural-embedded discrete choice model: Learning taste representation with strengthened interpretability," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 166-186.
  20. Melvin Wong & Bilal Farooq, 2019. "ResLogit: A residual neural network logit model for data-driven choice modelling," Papers 1912.10058, arXiv.org, revised Feb 2021.
  21. Khaled J. Assi & Md Shafiullah & Kh Md Nahiduzzaman & Umer Mansoor, 2019. "Travel-To-School Mode Choice Modelling Employing Artificial Intelligence Techniques: A Comparative Study," Sustainability, MDPI, vol. 11(16), pages 1-12, August.
  22. Alwosheel, Ahmad & van Cranenburgh, Sander & Chorus, Caspar G., 2018. "Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis," Journal of choice modelling, Elsevier, vol. 28(C), pages 167-182.
  23. Shaheen, Susan & Kemmerer, Charlene, 2008. "Smart Parking Linked to Transit: Lessons Learned from the Field Test in San Francisco Bay Area of California," Institute of Transportation Studies, Working Paper Series qt2bd6m65k, Institute of Transportation Studies, UC Davis.
  24. Daisik Nam & Jaewoo Cho, 2020. "Deep Neural Network Design for Modeling Individual-Level Travel Mode Choice Behavior," Sustainability, MDPI, vol. 12(18), pages 1-19, September.
  25. Paz, Alexander & Arteaga, Cristian & Cobos, Carlos, 2019. "Specification of mixed logit models assisted by an optimization framework," Journal of choice modelling, Elsevier, vol. 30(C), pages 50-60.
  26. S. Van Cranenburgh & S. Wang & A. Vij & F. Pereira & J. Walker, 2021. "Choice modelling in the age of machine learning -- discussion paper," Papers 2101.11948, arXiv.org, revised Nov 2021.
  27. Nasrin, Sharmin & Bunker, Jonathan, 2021. "Analyzing significant variables for choosing different modes by female travelers," Transport Policy, Elsevier, vol. 114(C), pages 312-329.
  28. Eltoukhy, Abdelrahman E.E. & Wang, Z.X. & Chan, Felix T.S. & Fu, X., 2019. "Data analytics in managing aircraft routing and maintenance staffing with price competition by a Stackelberg-Nash game model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 143-168.
  29. Yafei Han & Francisco Camara Pereira & Moshe Ben-Akiva & Christopher Zegras, 2020. "A Neural-embedded Choice Model: TasteNet-MNL Modeling Taste Heterogeneity with Flexibility and Interpretability," Papers 2002.00922, arXiv.org, revised Jul 2022.
  30. Salon, Deborah, 2006. "Cars and the City: An Investigation of Transportation and Residential Location Choices in New York City," University of California Transportation Center, Working Papers qt1br223vz, University of California Transportation Center.
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