Xiaoxu Chen
Postdoctoral Fellow, HEC Montréal, Canada
Ph.D. (2024) in Civil Engineering, McGill University, Canada
M.S. (2020) in Transportation Engineering, Tongji University, China
B.Eng. (2017) in Traffic Engineering, Harbin Institute of Technology, China
Welcome! I am Xiaoxu Chen (陈晓旭), a Postdoctoral Fellow in Logistics and Operations Management at HEC Montréal, working with Prof. Yossiri Adulyasak and Prof. Jean-François Cordeau on stochastic optimization in supply chain management. Before joining HEC Montréal, I worked with Prof. Lijun Sun and Prof. Martin Trépanier on statistical models with applications in transportation.
Research interests
I am interested in statistics and optimization for transportation and supply chain management. Currently, I am particularly interested in discrete choice models, spatiotemporal data modeling, and data-driven optimization under uncertainty. In the domain of transportation, I focus on probabilistic demand and travel time forecasting, statistical inference of origin-destination matrices (inverse problem), statistical modeling of passenger trip assignment in metro networks (inverse problem), and statistical modeling of driving behavior. In supply chain management, I explore problems such as statistical demand modeling and stochastic multi-echelon inventory optimization, with an emphasis on incorporating uncertainty into decision-making processes.
News
- Jun 2025: Our paper “Bayesian inference of time-varying origin-destination matrices from boarding and alighting counts for transit services” (authors: Xiaoxu Chen, Zhanhong Cheng, Lijun Sun) was accepted by Transportation Research Part B: Methodological. [Full-text]
- Feb 2025: Our paper “Conditional forecasting of bus travel time and passenger occupancy with Bayesian Markov regime-switching vector autoregression” (authors: Xiaoxu Chen, Zhanhong Cheng, Alexandra M. Schmidt, Lijun Sun) was accepted by Transportation Research Part B: Methodological. [Full-text]
- Feb 2025: Our paper “Understanding bus delay patterns under different temporal and weather conditions: A Bayesian Gaussian mixture model” (authors: Xiaoxu Chen, Saeid Saidi, Lijun Sun) was accepted by Transportation Research Part C: Emerging Technologies. [Full-text]
- Jan 2025: I attended the 104th Transportation Research Board Annual Meeting (TRB2025) in Washington D.C., USA. Prof. Sun, thanks for your support! My collabrator Zhanhong Cheng and I presented our work in the poster session.
- Oct 2024: I am glad to give a talk on “Bayesian inference of time-varying origin-destination matrices from boarding and alighting counts for transit services” at MIT Urban Mobility Lab. [Slides]
- Sep 2024: I am glad to give a talk on “Conditional forecasting of bus travel time and passenger occupancy with Bayesian Markov regime-switching vector autoregression” at HEC Montréal. [Slides]
- Sep 2024: I attended Conference in Emerging Technologies in Transportation Systems (TRC-30) in Crete, Greece. I presented our work on the Podium session. [Slides]
- Sep 2024: I began my role as a Postdoctoral Scholar at McGill University, advised by Prof. Lijun Sun and Prof. Martin Trépanier.
Found more in archived news
Selected works
- Chen, X., Schmidt, A. M., Ma, Z., & Sun, L. (2025). Bayesian spatiotemporal modeling of passenger trip assignment in metro networks. (Under review in Annals of Applied Statistics) [Full-text]
- Chen, X., Cheng, Z., & Sun, L. (2025). Bayesian inference of time-varying origin-destination matrices from boarding and alighting counts for transit services. Transportation Research Part B: Methodological, 199, 103278. [Full-text][Slides]
- Chen, X., Cheng, Z., Schmidt, A. M., & Sun, L. (2025). Conditional forecasting of bus travel time and passenger occupancy with Bayesian Markov regime-switching vector autoregression. Transportation Research Part B: Methodological, 192, 103147. [Full-text] [Slides] (Best Paper Award from TRB AED60 Statistical and Econometric Methods Committee🏅)
- Chen, X., Saidi, S., & Sun, L. (2025). Understanding bus delay patterns under different temporal and weather conditions: A Bayesian Gaussian mixture model. Transportation Research Part C: Emerging Technologies, 171, 105000. [Full-text]
- Chen, X., Cheng, Z., Jin, J. G., Trépanier, M., & Sun, L. (2023). Probabilistic forecasting of bus travel time with a Bayesian Gaussian mixture model. Transportation Science, 57(6), 1516-1535. [Full-text] [Slides]
- Chen, X., Zhang, C., Cheng, Z., Hou, Y., & Sun, L. (2023). A bayesian gaussian mixture model for probabilistic modeling of car-following behaviors. IEEE Transactions on Intelligent Transportation Systems, 25(6), 5880-5891. [Full-text]