Xin (Bruce) Wu

Dr. Wu is a researcher specializing in transportation modeling and logistics management. Dr. Wu’s research interests include transportation management and planning, travel demand modeling, transportation system optimization, supply chain management, logistics system design, and railway operations. He has extensive experience developing computational models and applying optimization methods to solve complex challenges in multimodal transportation and logistics systems.

I am Xin (Bruce) Wu, a Research Associate working at the Department of Civil and Environmental Engineering, Villanova University. I am a researcher specializing in transportation modeling and logistics management. My work focuses on developing innovative OR models, advanced analytical methods, and optimization algorithms to address complex challenges in transportation systems, mobility services, supply chain networks, and logistics operations. I received my Ph.D. in Transportation Engineering from Beijing Jiaotong University, where I was recognized as an outstanding doctoral graduate. I have held research positions in both academia and industry. I served as research scholars at Villanova University, the University of Maryland and Arizona State University, where I contributed extensively to projects funded by government agencies such as the Fedral Transit Administration (FTA), Federal Highway Administration (FHWA), the Arizona Department of Transportation (ADOT), the Maricopa Association of Governments (MAG), the Maryland Department of Transportation (MDOT), and the Northern Virginia Transportation Authority (NVTA). Before my research experience in the United States, Dr. Wu worked at the Beijing Transport Institute, where he participated in large-scale transportation modeling and policy analysis projects. My research interests include transportation management and planning, travel demand modeling, transportation system optimization, supply chain management, logistics system design, and railway operations. He has extensive experience developing computational models and applying optimization methods to solve complex challenges in multimodal transportation and logistics systems.

CONTACT

  • 📧 Email: xwu03@villanova.edu; xinwu8592@gmail.com
  • 🌐 My GitHub
  • 📞 Phone：+1 480-410-5902
  • 🎓 Google Scholar

EDUCATION

  • Ph.D., Transportation Engineering (Transportation planning and management), Beijing Jiaotong University, Beijing, China, 2018
  • B.S., Transportation Engineering (International shipment), Dalian Maritime University, Dalian, China, 2009

PUBLICATIONS

He has authored more than 35 peer-reviewed articles in leading journals, including the Transportation Research Part B (TRB), Transportation Research Part C (TRC), Transportation Research Part E (TRE), and the European Journal of Operational Research (EJOR). He has also received several best paper awards, including recognition from Multimodal Transportation.

Published papers (First author or corresponding author)

  1. Wu, X., Guo, J., Xian, K., and Zhou, X., 2018. Hierarchical travel demand estimation using multiple data sources: A forward and backward propagation algorithmic framework on a layered computational graph. Transportation Research Part C: Emerging Technologies, 96, pp.321-346.
  2. Wu, X., Wang, X., Kim, T., Saleh, K., Fu, H. and Xiong, C., 2025. Simultaneous estimation of induced, diverted, and ex-post demand for railway passengers: an interpretable machine learning framework based on constrained computational graphs. Transportation Research Part E: Logistics and Transportation Review, 202, p.104283.
  3. Wu, X., Liu, K., Zhen, D., Ji, Y., Shang, X., Cui, Q. and Xiong, C., 2025. Tradable Equity Credits (TECs) and internalised revenue maximisation: a nonlinear programming model for promoting equitable public transit services. Transportmetrica A: Transport Science, pp.1-29.
  4. Wu, X., Lu, J., Wu, S. and Zhou, X.S., 2021. Synchronizing time-dependent transportation services: Reformulation and solution algorithm using quadratic assignment problem. Transportation Research Part B: Methodological, 152, pp.140-179.
  5. Wu, X., Nie, L., Xu, M., and Zhao, L., 2019. Distribution planning problem for a high-speed rail catering service considering time-varying demands and pedestrian congestion: A lot-sizing-based model and decomposition algorithm. Transportation research part E: logistics and transportation review, 123, pp.61-89.
  6. Wu, X., Nie, L., Xu, M. and Yan, F., 2018. A perishable food supply chain problem considering demand uncertainty and time deadline constraints: Modeling and application to a high-speed railway catering service. Transportation Research Part E: Logistics and Transportation Review, 111, pp.186-209.
  7. Wu, X., Nie, L. and Xu, M., 2017. Designing an integrated distribution system for catering services for high-speed railways: A three-echelon location routing model with tight time windows and time deadlines. Transportation Research Part C: Emerging Technologies, 74, pp.212-244.
  8. Wu, X., Nie, L. and Xu, M., 2017. Robust fuzzy quality function deployment based on the mean-end-chain concept: Service station evaluation problem for rail catering services. European Journal of Operational Research, 263(3), pp.974-995.
  9. Wu, X., Nie, L., Xu, M. and Fu, H., 2016. Fuzzy QFD for Distribution Center Location Evaluation in Catering Service of High-Speed Railway. Management & Engineering, (22), p.83.
  10. Wu, X., Nie, L. and Xu, M., 2015. Service station evaluation problem in catering service of the high-speed railway: a fuzzy QFD approach based on evidence theory. Mathematical Problems in Engineering, 2015
  11. Wang, X., Fu, H., Wu, X., Liu, Y. and Kim, T., 2025. Multilayered Railway Passenger Demand Estimation Considering Nested Choices: A Computational Graph-based Learning Framework. Urban Rail Transit, pp.1-17(corresponding author).
  12. Zhou, X.S., Cheng, Q., Wu, X., Li, P., Belezamo, B., Lu, J. and Abbasi, M., 2022. A meso-to-macro cross-resolution performance approach for connecting polynomial arrival queue model to volume-delay function with inflow demand-to-capacity ratio. Multimodal Transportation, 1(2), p.100017. (corresponding author and 2022 Best Article Award).
  13. Sun, J., Guo, J., Wu, X., Zhu, Q., Wu, D., Xian, K., and Zhou, X., 2019. Analyzing the impact of traffic congestion mitigation: from an explainable neural network learning framework to marginal effect analyses. Sensors, 19(10), p.2254. (corresponding author).

Highlighted studies as a collaborator

  1. Hu, S., Xiong, C., Ji, Y., Wu, X., Liu, K. and Schonfeld, P., 2024. Understanding factors influencing user engagement in incentive-based travel demand management program. Transportation Research Part A: Policy and Practice, 186, p.104145
  2. Lu, J., Li, C., Wu, X. and Zhou, X.S., 2023. Physics-informed neural networks for integrated traffic state and queue profile estimation: A differentiable programming approach on layered computational graphs. Transportation Research Part C: Emerging Technologies, 153, p.104224.
  3. Cheng, Q., Liu, Z., Guo, J., Wu, X., Pendyala, R., Belezamo, B. and Zhou, X.S., 2022. Estimating key traffic state parameters through parsimonious spatial queue models. Transportation Research Part C: Emerging Technologies, 137, p.103596.
  4. Wang, W., Cai, K., Du, W., Wu, X., Tong, L.C., Zhu, X., and Cao, X., 2020. Analysis of the Chinese railway system as a complex network. Chaos, Solitons & Fractals, 130, p.109408.
  5. Ke, Y., Nie, L., Liebchen, C., Yuan, W. and Wu, X., 2020. Improving synchronization in air and high-speed rail integration service via adjusting a rail timetable: A real-world case study in China. Journal of Advanced Transportation, 2020.
  6. Ke, Y., Wu, X., Nie, L., Yao, Z. and Chen, Y., 2024. Synchronizing train, aircraft, shuttle, and passenger flows in intermodal timetabling: A time–space network-based formulation and a decomposition algorithm using Alternating Direction method of multipliers. Transportation Research Part C: Emerging Technologies, 159, p.104464.
  7. Yuan, W., Nie, L., Wu, X. and Fu, H., 2018. A dynamic bid price approach for the seat inventory control problem in railway networks with consideration of passenger transfer. PloS one, 13(8), p.e0201718.
  8. Zhang, X., Nie, L., Wu, X. and Ke, Y., 2020. How to optimize train stops under diverse passenger demand: a new line planning method for large-scale high-speed rail networks. Networks and Spatial Economics, 20(4), pp.963-988.
  9. Nie, B., Nie, L., Fu, H. and Wu, X., 2023. Weekly line planning to improve the match between rail supply and passenger demand. Computer‐Aided Civil and Infrastructure Engineering, 38(13), pp.1810-1833.

Papers in Conference Proceedings:

  1. Wu, X., Dutta, A., Zhang, W., Zhu, H., Livshits, V. and Zhou, X.S., 2021. Characterization and Calibration of Volume-to-Capacity Ratio in Volume-Delay Functions on 2 Freeways Based on a Queue Analysis Approach. Transportation Research Record: Journal of the Transportation Research Board, No. TRBAM-21-04304 (Cited by FHWA handbook).
  2. Belezamo, B., Wu, X., Avcı, C., & Zhou, X. (2019, October). Kinematic wave-oriented Markov Chain model to capture the spatiotemporal correlations of coupled traffic states. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC)(pp. 2343-2348). IEEE (Corresponding Author).
  3. Wu, X., Guo, J., Lin, R., Qu, Y., Xian, K., and Zhou, X., 2018. Enabling Transportation System Intelligence: Hierarchical Estimation of Traffic Network State and Behavior Coefficients Using a Computational Graph Approach Modeling Framework (No. 18-06749).
  4. Zhang, L., Wu, X., Liu, K. and Xiong, C., 2024, October. Evaluating the Spatial-Temporal Impact of Urban Flooding on Mobility Patterns and Point of Interest. In 2024 IEEE Global Humanitarian Technology Conference (GHTC) (pp. 424-430). IEEE.
  5. Liu, K., Wu, X., Zhang, L. and Xiong, C., 2024, October. Characterize Human Mobility in Nigeria During Flooding Season and Its Impact in Shaping the Spread of Covid-19. In 2024 IEEE Global Humanitarian Technology Conference (GHTC) (pp. 56-63). IEEE.

Participated projects

Villanova University:

  1. Enhancing Mobility Innovation with Software-Based Solutions for Smart and Equitable Travel Demand Management (USDOT Federal Transit Administration), 2023-2025
  2. Determining the Impact of Human Mobility Patterns on Transmissions of SARS-CoV-2 (National Institutes of Health) 2022-2026
  3. An Exploratory Research to Quantify SARS-CoV-2 Related Health Effects under Extreme Climate Events (National Institutes of Health) 2023-2024

University of Maryland

  1. Advanced Transportation and Congestion Mitigation Technologies Deployment (ATCMTD) incenTrip app development, 6-2022 – 2023

Arizona State University:

  1. Development of Multi-resolution Modeling Tools for Personalized and Dynamic Travel Demand Management Applications
  2. Development of open source dynamic traffic assignment for Northern Virginia, 2021-6-2022.
  3. Development of Multi-resolution Modeling Tools for Personalized and Dynamic Travel Demand Management Applications (FHWA), 2021-Now.
  4. Multi-Resolution Modeling for Traffic Analysis, Federal Highway Administration (FHWA), 2020-2021
  5. Dynamic Traffic Assignment: Assessing its Value as a Planning Support Tool in Arizona, Arizona Department of Transportation (ADOT), 2020- Now
  6. Loop101 Mobility Project Phase 1 Modeling Effort (ADOT), 2021
  7. FY2018 MAG MODELS INTEGRATION: Calibration of volume-delay function in the area of MAG travel demand model, 2020
  8. TOMNeT Project: Teaching the travel demand flow estimation models: a new deep-learning approach using multi-source data, 2021
  9. Improving regional activity-based models in Beijing, Beijing Transport Institute (BTI), 2019

Beijing Jiaotong University:

  1. Planning and management of High-speed rail economy in Yunnan province, Social scientific research, 2016-2017 (Collaborator).
  2. Circulation planning of rolling stocks on Beijing-Guangzhou and Beijing-Shanghai corridors, China Railways Corporation (China’s Ministry of Railways), 2015-2017. (Collaborator).
  3. A multi-stage approach to design skip-stop pattern for line planning problem Science and technology department of Beijing Jiaotong University, 2013-2014. (Collaborator).
  4. Design a distribution system for catering service of high-speed railway based on rail timetable, Science and technology department of Beijing Jiaotong University, 2013-2014. (Supervisor).
  5. Rail timetabling and management: locate transfer hub and design transfer schedule, China Railways Corporation (China’s Ministry of Railways), 2012-2013 (Collaborator).
  6. Technologies for passenger services: Supporting technologies for passenger service product design and optimization, China Railways Corporation (China’s Ministry of Railways), 2011-2012 (Collaborator).
  7. Passenger management and service technologies for high-speed railway: Extended service design and quality assurance, China Railways Corporation (China’s Ministry of Railways), 2014-2015 (Collaborator).
  8. Management of the catering business on EMU trains, China Railways Corporation (China’s Ministry of Railways), 2010-2011. (Collaborator)
  9. Key Technique and System in Optimizing High-Speed Railway Transport Organization Plan, China Railways Corporation (China’s Ministry of Railways), 2010-2011 (Collaborator).