Enhancing Mode-Choice Models with Conformal Prediction: Uncertainty Quantification and Decision Support Using Tree-Based Machine Learning
References
- Ben-Akiva, M. E. B., Lerman, S.R. (1985) Discrete choice analysis: theory and application to predict travel demand. Cambridge London: MIT Press.
- Bishop, C. M. (2006) Pattern recognition and machine learning. Springer Science+Business Media, LLC.
- Breiman, L. (2001) Random Forests. Machine Learning, 45, 5-32. DOI: 10.1023/A:1010950718922.
- Chen, C., Qin, C., Ouyang, C., Li, Z., Wang, S., Qiu, H., Chen, L., Tarroni, G., Bai, W., Rueckert, D. (2022) Enhancing MR image segmentation with realistic adversarial data augmentation. Medical Image Analysis, 82,102597, DOI: 10.1016/j.media.2022.102597.
- Chen, T., Guestrin, C. (2016) XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (KDD ‘16). Association for Computing Machinery, 785–794. DOI:10.1145/2939672.2939785.
- Cheng, L., Chen, X., De Vos, J., Lai, X., Witlox, F. (2019) Applying a random forest method approach to model travel mode choice behavior. Travel Behaviour and Society, 14, 1-10, DOI: 10.1016/j.tbs.2018.09.002.
- Eldafrawi, M., Varghese, K. K., Afsari, M., Babapourdijojin, M. (2024) Machine learning-enhanced conformal prediction approach for road traffic accident severity assessment: A case study of Rome. DOI: 10.2139/SSRN.4679159.
- Forman, G., Scholz, M. (2010) Apples-to-apples in cross-validation studies: Pitfalls in classifier performance measurement. ACM SIGKDD Explorations Newsletter, 12(1), 49–57. DOI: 10.1145/1882471.1882479.
- Frank, K., Massey, JR. (1951) The Kolmogorov-Smirnov test for goodness of fit. Journal of the American Statistical Association, 46(253), 68–78. DOI:10.2307/2280095.
- Gammerman, A., Vovk, V., Vapnik, V. (1998) Learning by transduction. In: Proceedings of Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI ‘98), Madison, July 1998. San Francisco: Morgan Kaufmann Publishers Inc, 148–155. arXiv:1301.7375.
- Hagenauer, J., Helbich, M. (2017) A comparative study of machine learning classifiers for modeling travel mode choice. Expert Systems with Applications, 78, 273–282. DOI: 10.1016/j.eswa.2017.01.057.
- Hasan, M., Dipto, A. Z., Islam, M. S., Sorwar, A., Alam, S. (2019) A smart semi-automated multifarious surveillance bot for outdoor security using thermal image processing. Advances in Networks, 7(2), 21–28. DOI: 10.11648/j.net.20190702.12.
- Jahangiri, A., Rakha, H. A. (2015) Applying machine learning techniques to transportation mode recognition using mobile phone sensor data. IEEE Transactions on Intelligent Transportation Systems, 16(5), 2406—2417. DOI:10.1109/TITS.2015.2405759.
- Japkowicz, N., Shah, M. (2011) Evaluating learning algorithms: A classification perspective. Cambridge University Press. DOI: 10.1017/CBO9780511921803.
- Kohavi, R. (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th international joint conference on Artificial intelligence, 2, 1137–1143. San Francisco: Morgan Kaufmann Publishers Inc. DOI: 10.5555/1643031.1643047.
- McFadden, D. (1973) Conditional logit analysis of qualitative choice behavior. Available at: https://eml.berkeley.edu/reprints/mcfadden/zarembka.pdf.
- McFadden, D., Train, K. (2000) Mixed MNL models for discrete response. Journal of Applied Econometrics, 15(5), 447–470. DOI:10.1002/1099-1255(200009/10)15:5<;447::AIDJAE570>3.0.CO;2-1.
- Papadopoulos, H., Proedrou, K., Vovk, V., Gammerman, A. (2002) Inductive confidence machines for regression. In: Proceedings of the 13th European Conference on Machine Learning (ECML’02). Springer, 45-–356. DOI:10.1007/3-540-36755-1_29.
- Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., Gulin, A. (2018) CatBoost: Unbiased boosting with categorical features. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montréal, December 2018. New York: Curran Associates Inc., 6639–6649. arXiv:1706.09516.
- Ravindran, R., Santora, M. J., Jamali, M. M. (2022) Camera, LiDAR, and Radar Sensor Fusion Based on Bayesian Neural Network (CLR-BNN). IEEE Sensors Journal, 22(7), 6964–6974. DOI: 10.1109/jsen.2022.3154980.
- Salas, P., De la Fuente, R., Astroza, S., Carrasco, J.A. (2022) A systematic comparative evaluation of machine learning classifiers and discrete choice models for travel mode choice in the presence of response heterogeneity. Expert Systems with Applications, 193(1784), 116253. DOI:10.1016/j.eswa.2021.116253.
- Shafer, G., Vovk, V. (2008) A tutorial on conformal prediction. Journal of Machine Learning Research, 9, 371–421. arXiv:0706.3188.
- Vovk, V., Gammerman, A., Shafer, G. (2005) Algorithmic learning in a random world. 1st edition. Berlin: Springer Verlag. DOI:10.1007/b106715.
- Wilson, E. B. (1927) Probable inference, the law of succession, and statistical inference. Journal of the American Statistical Association, 22(158), 209–212. DOI: 10.2307/2276774.
- Zhao, X., Yan, X., Yu, A., Van Hentenryck, P. (2020) Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models. Travel Behaviour and Society, 20, 22–35. DOI:10.1016/j.tbs.2020.02.003.
- Zheng, S., Fang, Z., Zhao, L. (2024) Incorporating uncertainty quantification into travel mode choice modeling: A Bayesian neural network (BNN) approach and an uncertainty-guided active survey framework. DOI:10.48550/arXiv.2406.10948.
Language: English
Page range: 352 - 361
Published on: Nov 21, 2025
Published by: Transport and Telecommunication Institute
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
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© 2025 Ramin Bohlouli, Ken Koshy Varghese, Guido Gentile, Mohamed Eldafrawi, published by Transport and Telecommunication Institute
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.