Xin (Bruce) Wu

Transportation researcher at Villanova University working on interpretable AI for mobility data, public transit and railway management, and supply chain optimization.

Profile poster of Xin (Bruce) Wu

I am Xin (Bruce) Wu, a transportation researcher in the Department of Civil and Environmental Engineering, Villanova University. I specialize in transportation modeling and logistics management. My work develops operations research models, interpretable and physics-informed machine learning methods, and large-scale optimization algorithms for transportation systems, mobility services, public transit and railways, supply chains, and logistics operations.

I received my doctoral degree from the School of Traffic and Transportation, Beijing Jiaotong University, where I was recognized as an outstanding doctoral graduate. Before Villanova, I conducted research at the University of Maryland and Arizona State University, contributing to projects funded by the Federal Transit Administration (FTA), Federal Highway Administration (FHWA), National Institutes of Health (NIH), Arizona Department of Transportation (ADOT), Maricopa Association of Governments (MAG), Maryland Department of Transportation (MDOT), and Northern Virginia Transportation Authority (NVTA). Earlier, I worked at the Beijing Transport Institute on large-scale transportation modeling and policy analysis.

Research impact (Google Scholar, November 2026): 1,015 citations · h-index 17 · i10-index 22 · 42 peer-reviewed publications (33 journal articles and 9 conference papers) in venues including Transportation Research Parts B, C, and E, IEEE Transactions on Intelligent Transportation Systems, and the European Journal of Operational Research. Google Scholar profile

Research areas: Mobility Data & Traffic System Modeling · Public Transit & Railway Management · Supply Chain & Logistics Optimization · Teaching & Training

Villanova University
Villanova University · 2023–present
University of Maryland
University of Maryland · 2022–2023
Arizona State University
Arizona State University · 2018–2022

Contact

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

Education

Ming Lake, Beijing Jiaotong University
Ming Lake (明湖), Beijing Jiaotong University: doctoral training
Xin Wu in Dalian, 2003
Dalian, 2003: an undergraduate at Dalian Maritime University
  • D.Eng./M.Eng., School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China, 2018
  • B.Eng., Dalian Maritime University, Liaoning, China, 2009

Employment

  • 2023 – present: Department of Civil and Environmental Engineering, Villanova University, Villanova, PA
  • 2022 – 2023: Maryland Transportation Institute, University of Maryland, College Park, MD
  • 2018 – 2022: School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ
  • 2018: Intern, Beijing Transport Institute, Beijing, China
  • 2010 – 2018: Graduate Research Assistant, School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China

Awards

  • 2022 Best Article Award, Multimodal Transportation, for “A meso-to-macro cross-resolution performance approach for connecting polynomial arrival queue model to volume-delay function with inflow demand-to-capacity ratio”
  • Best Methodological Innovation Award, Runner-up, WIDE-HTR 2026 Workshop (Temple University & University of Houston), April 2026
  • Outstanding Doctoral Graduate, Beijing Jiaotong University

Publications

Published papers (first or corresponding author)

  1. TR-CFirst author Wu, X., Guo, J., Xian, K., 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, 321–346. DOI
  2. TR-EFirst author Wu, X., Liu, K., Zhen, D., Ji, Y.E., Cui, Q., Xiong, C., 2026. Tradable Equity Credits (TECs) for public transit: a dynamic pricing approach for heterogeneous travelers. Transportation Research Part E: Logistics and Transportation Review, 211, 104858. DOI
  3. TR-EFirst author Wu, X., Wang, X., Kim, T., Saleh, K., Fu, H., 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, 104283. DOI
  4. TR-BFirst author Wu, X., Lu, J., Wu, S., Zhou, X.S., 2021. Synchronizing time-dependent transportation services: Reformulation and solution algorithm using quadratic assignment problem. Transportation Research Part B: Methodological, 152, 140–179. DOI
  5. EJORFirst author Wu, X., Nie, L., 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), 974–995. DOI
  6. TR-EFirst author Wu, X., Nie, L., Xu, M., 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, 186–209. DOI
  7. TR-EFirst author Wu, X., Nie, L., Xu, M., 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, 61–89. DOI
  8. Transportmetrica AFirst author Wu, X., Liu, K., Zhen, D., Ji, Y., Shang, X., Cui, Q., 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, 1–29. DOI
  9. TR-CFirst author Wu, X., Nie, L., 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, 212–244. DOI
  10. TRBFirst author Wu, X., Dutta, A., Zhang, W., Zhu, H., Livshits, V., Zhou, X.S., 2021. Characterization and calibration of volume-to-capacity ratio in volume-delay functions on freeways based on a queue analysis approach. Proceedings of the 100th Annual Meeting of the Transportation Research Board, Washington, D.C. (cited by an FHWA handbook)
  11. URTFirst author Wu, X., Huo, Y., Wen, Y., Xing, J., 2026. Multimodal hub-and-spoke network design problems to integrate urban air mobility and high-speed rail services: A semi-quadratic assignment formulation and its linearization. Urban Rail Transit, 1–17. DOI
  12. MPEFirst author Wu, X., Nie, L., 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, 404926. DOI
  13. T-ITSCo-first author Li, R., Wu, X., Luo, W., Tavakoli, A., Nataraj, C., Xiong, C., 2026. Taming volatile trajectories: Sup-HMM, a Bayesian-optimized hidden Markov model for map-matching of location-based service data. IEEE Transactions on Intelligent Transportation Systems. DOI
  14. TR-CCorresponding Shao, F., Shao, H., Wu, X., Cheng, Q., Lam, W.H.K., 2025. A physics-informed machine learning framework for speed-flow prediction: Integrating an S-shaped traffic stream model with deep learning models. Transportation Research Part C: Emerging Technologies, 180, 105362 (corresponding author). DOI
  15. SensorsCorresponding Sun, J., Guo, J., Wu, X., Zhu, Q., Wu, D., Xian, K., Zhou, X., 2019. Analyzing the impact of traffic congestion mitigation: from an explainable neural network learning framework to marginal effect analyses. Sensors, 19(10), 2254 (corresponding author). DOI
  16. MULTRACorresponding Zhou, X.S., Cheng, Q., Wu, X., Li, P., Belezamo, B., Lu, J., 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), 100017 (corresponding author; 2022 Best Article Award). DOI
  17. URTCorresponding Wang, X., Fu, H., Wu, X., Liu, Y., Kim, T., 2025. Multilayered railway passenger demand estimation considering nested choices: A computational graph-based learning framework. Urban Rail Transit, 11(3), 250–266 (corresponding author). DOI
  18. IEEECorresponding Belezamo, B., Wu, X., Avcı, C., Zhou, X., 2019. Kinematic wave-oriented Markov chain model to capture the spatiotemporal correlations of coupled traffic states. 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2343–2348 (corresponding author).

Highlighted research as a collaborator

  1. TR-E Shao, F., Dong, D., Shao, H., Wu, X., Lam, W.H.K., 2026. A hierarchical generative adversarial network for traffic flow uncertainty modeling and anomalous state identification. Transportation Research Part E: Logistics and Transportation Review, in press, 105353. DOI
  2. TR-C Lu, J., Li, C., Wu, X., 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, 104224. DOI
  3. TR-C Cheng, Q., Liu, Z., Guo, J., Wu, X., Pendyala, R., Belezamo, B., Zhou, X.S., 2022. Estimating key traffic state parameters through parsimonious spatial queue models. Transportation Research Part C: Emerging Technologies, 137, 103596. DOI
  4. TR-D Zhen, D., Wu, X., Liu, K., Ji, Y., Xiong, C., Cui, Q., 2026. Carbon credit in travel demand management: an incentive-based framework for emission reduction. Transportation Research Part D: Transport and Environment, 150, 105041. DOI
  5. IJDRR Zhang, L., Wu, X., Liu, K., Al Mehedi, M.A., Zhou, J., Smith, V., Xiong, C., 2026. Diagnostic framework for causal inference in seasonal urban flooding: Precipitation-based control selection and synthetic difference-in-differences in Lagos, Nigeria. International Journal of Disaster Risk Reduction, 105993. DOI
  6. JTG Liu, K., Wu, X., Molnar, L., Hu, S., Zhang, L., Xiong, C., 2025. Characterizing human mobility and its impact on the spread of COVID-19 during extreme weather events: A case study of hurricane Laura. Journal of Transport Geography, 129, 104423. DOI
  7. PLOS ONE Luo, W., Wu, X., Li, R., Fitzpatrick, M., Charurat, M., Blanco, N., et al., Xiong, C., 2025. Variant patterns and influence of inter-regional travel during the SARS-CoV-2 expansion in South Africa. PLOS ONE, 20(11), e0329621. DOI
  8. TR-A Hu, S., Xiong, C., Ji, Y., Wu, X., Liu, K., Schonfeld, P., 2024. Understanding factors influencing user engagement in incentive-based travel demand management program. Transportation Research Part A: Policy and Practice, 186, 104145. DOI
  9. CSF Wang, W., Cai, K., Du, W., Wu, X., Tong, L.C., Zhu, X., Cao, X., 2020. Analysis of the Chinese railway system as a complex network. Chaos, Solitons & Fractals, 130, 109408. DOI
  10. JAT Ke, Y., Nie, L., Liebchen, C., Yuan, W., 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, 5081315. DOI
  11. TR-C Ke, Y., Wu, X., Nie, L., Yao, Z., 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, 104464. DOI
  12. PLOS ONE Yuan, W., Nie, L., Wu, X., 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), e0201718. DOI
  13. NETS Zhang, X., Nie, L., Wu, X., 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), 963–988. DOI
  14. CACIE Nie, B., Nie, L., Fu, H., Wu, X., 2023. Weekly line planning to improve the match between rail supply and passenger demand. Computer-Aided Civil and Infrastructure Engineering, 38(13), 1810–1833. DOI

Other journal articles and conference papers

  1. IEEE Jiang, Y., Wu, X., Chen, Y., 2020. Designing a flexible catering system for high-speed railway considering departure time selection and time deadline constraints. IEEE Access, 8, 44300–44317. DOI
  2. Complexity Jiang, Y., He, T., Xiong, J., Wu, X., Chen, Y., 2020. Parallel machine production and transportation operations’ scheduling with tight time windows. Complexity, 2020, 1381340. DOI
  3. M&EFirst author Wu, X., Nie, L., Xu, M., Fu, H., 2016. Fuzzy QFD for distribution center location evaluation in catering service of high-speed railway. Management & Engineering, 22, 83.
  4. IEEE Liu, K., Wu, X., Zhang, L., Xiong, C., 2024. Characterize human mobility in Nigeria during flooding season and its impact in shaping the spread of COVID-19. 2024 IEEE Global Humanitarian Technology Conference (GHTC), 56–63. DOI
  5. IEEE Zhang, L., Wu, X., Liu, K., Xiong, C., 2024. Evaluating the spatial-temporal impact of urban flooding on mobility patterns and point of interest. 2024 IEEE Global Humanitarian Technology Conference (GHTC), 424–430. DOI
  6. CICTP Yuan, W., Nie, L., Fu, H., Wu, X., Ke, Y., 2018. Simulation-based method for seat inventory control optimization under heterogeneous customer behavior: A case study of the China Railway. CICTP 2018, 788–798.
  7. CICTP Zhuge, L., Li, W., Guo, J., Xian, K., Wu, X., Zhou, X., 2018. A tree-based reoptimization framework for solving traffic assignment problem in rapid decision making applications. CICTP 2018, 205–214.
  8. TRB Yuan, W., Nie, L., Wu, X., 2018. A dynamic bid price approach for the seat inventory control problem in high-speed railway networks considering passenger transfer service. TRB 97th Annual Meeting. TRID
  9. CICTP Leng, N., Nie, L., Wu, X., 2015. A practical method of passenger flow forecasting for Nanning–Guangzhou and Guiyang–Guangzhou high-speed rail. CICTP 2015, 3528–3538.
  10. MEIC Leng, N., Nie, L., Guo, G., Wu, X., 2015. Passenger flow forecasting for Chinese high speed rail network. 2015 International Conference on Mechatronics, Electronic, Industrial and Control Engineering (MEIC).

Professional Service

  • Peer reviewer: 100 completed reviews (2018–2026) for 16 journals and conferences, including Transportation Science; Transportation Research Part B; Transportation Research Part C; Transportation Research Part E; IEEE Transactions on Intelligent Transportation Systems; IEEE Intelligent Transportation Systems Magazine; Transportmetrica A and B; Transportation Research Record; Journal of Transport Geography; and the TRB Annual Meeting.
  • Guest Editor, Special Issue “Applied Mathematics and Optimization Methods in Transport Planning and Management,” Mathematics (MDPI), 2025–present
  • Guest Editor, Special Issue “Next-Generation Intelligent Transportation Systems,” Future Internet (MDPI), 2025–present
  • Judge, INFORMS Railway Applications Section (RAS) Student Paper Competition, 2026

Research Projects

Villanova University

  • Enhancing Mobility Innovation with Software-Based Solutions for Smart and Equitable Travel Demand Management (FTA), 2023–2025
  • Determining the Impact of Human Mobility Patterns on Transmissions of SARS-CoV-2 (NIH), 2022–2026
  • An Exploratory Research to Quantify SARS-CoV-2–Related Health Effects Under Extreme Climate Events (NIH), 2023–2024

University of Maryland

  • Advanced Transportation and Congestion Mitigation Technologies Deployment (ATCMTD) (FHWA), June 2022–2023

Arizona State University

  • Development of Multi-Resolution Modeling Tools for Personalized and Dynamic Travel Demand Management Applications (FHWA), 2020–2021; 2021–present
  • Development of Open-Source Dynamic Traffic Assignment for Northern Virginia (NVTA), 2021–June 2022
  • Dynamic Traffic Assignment: Assessing Its Value as a Planning Support Tool in Arizona (ADOT), 2020–2022
  • Loop 101 Mobility Project Phase 1 Modeling Effort (ADOT), 2021
  • FY2018 MAG Models Integration: Calibration of volume–delay functions in the travel demand model (MAG), 2020
  • TOMNeT Project: Teaching travel demand flow estimation models, a new deep-learning approach using multi-source data (UTC), 2021
  • Improving regional activity-based models in Beijing (BTI), 2019

Beijing Jiaotong University

  • Planning and management of the high-speed rail economy in Yunnan Province, Social Science Research, 2016–2017
  • Circulation planning of rolling stock on the Beijing–Guangzhou and Beijing–Shanghai corridors (CRC), 2015–2017
  • A multi-stage approach to designing skip-stop patterns for line planning (Beijing Jiaotong University), 2013–2014
  • Designing a distribution system for catering services of high-speed rail based on rail timetables (Beijing Jiaotong University), 2013–2014 (Supervisor)
  • Rail timetabling and management: locating transfer hubs and designing transfer schedules (CRC), 2012–2013
  • Supporting technologies for passenger service product design and optimization (CRC), 2011–2012
  • Passenger management and service technologies for high-speed railway: extended service design and quality assurance (CRC), 2014–2015
  • Management of catering services on EMU trains (CRC), 2010–2011
  • Key techniques and systems for optimizing high-speed railway transport organization plans (CRC), 2010–2011