Working Papers

• Shen, M., Xiao, F., Gu, W., Ye, H. Cognitive hierarchy in day-to-day network flow dynamics. [https://arxiv.org/abs/2409.11908]
• Xiao, Z., Ye, H., Chung, E. Solving train eco-driving problems to global optimality. [https://ssrn.com/abstract=4928445]

Published Papers

(including errata, source code and author accepted manuscripts)

1. Wu, F., Ye, H., Bektas, T., Dong, M., 2025. New and tractable formulations for the eco-driving and the eco-routing-and-driving problems. European Journal of Operational Research 321 (2), 445-461.
2. Zou, X., Chung, E., Ye, H., Zhang, H., 2024. Deep learning for traffic prediction and trend deviation identification: A case study in Hong Kong. Data Science for Transportation 6, 27.
3. Zhou, B., Li, S., Xu, M., Ye, H., 2024. Investigating the influence of herd effect on the logit stochastic user equilibrium problem. Transportation Research Part E 192, 103743.
4. Bi, X., Wang, R., Ye, H., Hu, Q., Bu, S., Chung, E., 2023. Real-time scheduling of electric bus flash charging at intermediate stops: A deep reinforcement learning approach. IEEE Transactions on Transportation Electrification 10 (3), 6309-6324.
5. Liang, J., Ke, J., Wang, H., Ye, H., Tang, J., 2023. A Poisson-based distribution learning framework for short-term prediction of food delivery demand ranges. IEEE Transactions on Intelligent Transportation Systems 24 (12), 14556-14569.
6. Ye, H., 2022. On stochastic-user-equilibrium-based day-to-day dynamics. Transportation Science 56 (1), 103–117. [Author Accepted Manuscript]
7. Wu, F., Bektaş, T., Dong, M., Ye, H., Zhang, D., 2021. Optimal driving for vehicle fuel economy under traffic speed uncertainty. Transportation Research Part B 154, 175-206. [Presentation]
8. Ye, H., Xiao, F., Yang, H., 2021. Day-to-day dynamics with advanced traveler information. Transportation Research Part B 144, 23-44. [Author Accepted Manuscript]
9. Ye, H., Xiao, F., Yang, H., 2018. Exploration of day-to-day route choice models by a virtual experiment. Transportation Research Part C 94, 220-235, and ISTTT22 (poster), Illinois, USA, 2017.
10. Ye, H., Yang, H., 2017. Rational behavior adjustment process with boundedly rational user equilibrium. Transportation Science 51 (3), 968-980.
11. Ye, H., Liu, R., 2017. Nonlinear programming methods based on closed-form expressions for optimal train control. Transportation Research Part C 82, 102-123. [Codes of Case Studies]
12. Ye, H., Liu, R., 2016. A multiphase optimal control method for multi-train control and scheduling on railway lines. Transportation Research Part B 93, 377-393. [Codes of Case Studies]
13. Xiao, F., Yang, H., Ye, H., 2016. Physics of day-to-day network flow dynamics. Transportation Research Part B 86, 86-103. [Errata]
14. Wang, X.L., Ye, H., Yang, H., 2015. Decentralizing Pareto-efficient network flow/speed patterns with hybrid schemes of speed limit and road pricing. Transportation Research Part E 83, 51-64.
15. Ye, H., Yang, H., Tan, Z.J., 2015. Learning marginal-cost pricing via a trial-and-error procedure with day-to-day flow dynamics. Transportation Research Part B 81, 794-807, and ISTTT21 (lectern), Japan, 2015.
16. Yang, H., Ye, H., Li, X., Zhao, B., 2015. Speed limits, speed selection and network equilibrium. Transportation Research Part C 51, 260-273. [Errata] [Codes of Case Studies]
17. Ye, H., Yang, H., 2013. Continuous price and flow dynamics of tradable mobility credits. Transportation Research Part B 57, 436-450, and ISTTT20 (lectern), The Netherlands, 2013.