IDEAS home Printed from https://ideas.repec.org/r/eee/transe/v36y2000i3p155-172.html
   My bibliography  Save this item

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

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  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. 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.
  3. 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.
  4. 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).
  5. 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.
  6. 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).
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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).
  14. 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.
  15. 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).
  16. 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).
  17. 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.
  18. 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.
  19. Shi, Haolun & Yin, Guosheng, 2018. "Boosting conditional logit model," Journal of choice modelling, Elsevier, vol. 26(C), pages 48-63.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. 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.
  27. 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.
  28. 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.
  29. Nasrin, Sharmin & Bunker, Jonathan, 2021. "Analyzing significant variables for choosing different modes by female travelers," Transport Policy, Elsevier, vol. 114(C), pages 312-329.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.