Publication

New/Multimodal Mobility Services

  1. Liu, GH de Almeida Correia, Z Ma, S Li, X Ma* (2023). Integrated optimization of timetable, bus formation, and vehicle scheduling in autonomous modular public transport systems. Transportation Research Part C: Emerging Technologies, 155, 104306.
  2. K Tuncel, HN Koutsopoulos, Z Ma* (2023). An integrated ride-matching and vehicle-rebalancing model for shared mobility-on-demand services (2023). Computers and Operations Research, DOI: 10.1016/j.cor.2023.10631
  3. H. Chen, J. Kronqvist, W. Burghout, E. Jenelius, and Z. Ma* (2023). Mixed Integer Formulation with Linear Constraints for Integrated Service Operations and Traveler Choices in Multimodal Mobility Systems. 11th Symposium of the European Association for Research in Transportation (hEART 2023), Zurich, Switzerland.
  4. H. N. Koutsopoulos*, Z. Ma, and S. Zahedi (2023). Shared Mobility: Opportunities and Potential. Book Title: Re-engineering the Sharing Economy: Design, Policy, and Regulation, Cambridge University Press (In press).
  5. K Tuncel, HN Koutsopoulos, Z Ma* (2022). An Integrated Ride-Matching Model for Shared Mobility on Demand Services, MFTS conference in TU Dresden, Germany
  6. Z. Ma*, H. Koutsopoulos (2022). Near-on-Demand Mobility. The Benefits of User Flexibility for Ride-Pooling Services. Transportation Research Part C: Emerging Technologies. doi.org/10.1016/j.trc.2021.103530
  7. Moosavi, S. M. H., Ma, Z., Armaghani, D. J., Aghaabbasi, M., Ganggayah, M. D., Wah, Y. C., Ulrikh, D. V. (2022). Understanding and Predicting the Usage of Shared Electric Scooter Services on University Campuses. Applied Sciences, 12(18), 9392.
  8. X. Chen, Z. Ma* and Z. Li (2021). User Attitudes Toward Incentive Strategies for Transportation Network Company Services: Share Trips, Extra Walk and Request Rides in Advance The 20th and 21st joint COTA International Conference of Transportation Professionals, Xi’an, China
  9. S. Zahedi*, H. Koutsopoulos, Z. Ma (2020), Dynamic Interlining in Bus Operations, Transportation Research Board 99th Annual Meeting, Washington D.C., United States.
  10. G. Ye, Z. Ma*, X. Chen and Z. Li (2021). Usage Frequency and Service Type Preference of Ride-hailing
    Service in University Community: A Case Study in Suzhou, China. The 20th and 21st joint COTA
    International Conference of Transportation Professionals, Xi’an, China
  11. J. Zhou, Z. Ma*, S. Hirschmann, F. Lao (2020). Transportation Network Company Service Usage in theUniversity Community: Service Adoption, Usage Frequency and Service Type Choice. Transportation Research Board 99th Annual Meeting, Washington D.C., United States.
  12. Z. Ma*, H. N. Koutsopoulos, Y. Zheng (2019). Evaluation of the On-Demand Ridesplitting Services. Transportation Research Board 98th Annual Meeting, Washington D.C., United States

Intelligent Transportation Systems

  1. Y. Song, D. Li, Z. Ma, D. Liu, T. Zhang (2024). A state-based inverse reinforcement learning approach to model activity travel choices behavior with reward function recovery. Transportation Research Part C: Emerging Technologies, 158, 104454.
  2. 2. Y. Lei, X. Chen, D. Wan, Z. Ma, L. Yu (2024). Choice Behavior and Diffusion Impact Analysis of Connected and Autonomous Vehicles Travelers with Managed Lanes. Transportation Research Record, (Accepted).
  3. S. Zhu, Y. Yuan, Z. Ma, Q. Lan (2024). Learning about Traffic Engineering through Rapid Prototyping –A Case Study of Car Following Microscopic Simulation, Transportation Research Board 103th Annual Meeting, Washington D.C., United States.
  4. Y. Ling, Z. Ma*, B. Zhang, Q. Zhang, X. Weng (2023). SA-BiGCN: Bi-Stream Graph Convolution Networks with Spatial Attentions for the Eye Contact Detection in the Wild. IEEE Transactions on Intelligent Transportation Systems, (accepted)
  5. P. Wu, Z. Ma, A. Gustafsson (2023). DiGital twin for Emission MonItoring aNd predIction – Kista Case. 12th Annual Swedish Transport Research Conference (STRC 2023), Stockholm, Sweden.
  6. Q. Zhang, Z. Ma (2023). Data-Driven Causality Discovery for Bus Arrival Delays in Urban Public Networks. 12th Annual Swedish Transport Research Conference (STRC 2023), Stockholm, Sweden.
  7. Q. Zhang, Z. Ma*, P. Zhang, E. Jenelius, X. Ma and Y. Wen (2023). User-station attention inference using smart card data: a knowledge graph assisted matrix decomposition model. Applied Intelligence. https://doi.org/10.1007/s10489–023–04678–2.
  8. K Tuncel, HN Koutsopoulos, Z Ma* (2023). An Unsupervised Learning Approach for Robust Denied Boarding Probability Estimation Using Smartcard and Operation Data in Urban Railways. IEEE Intelligent Transportation Systems Magazine, DOI: 10.1109/MITS.2023.3289969
  9. K Tuncel, HN Koutsopoulos, Z Ma* (2023). Data-Driven Real-Time Denied Boarding Prediction in
    Urban Railway Systems. Transportation Research Record, https://doi.org/10.1177/03611981231184237
  10. B Liu, X Ma*, E Tan and Z Ma* (2023). Passenger flow anomaly detection in urban rail transit networks with graph convolution network–informer and Gaussian Bayes models. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
  11. P. Zhang, H. N. Koutsopoulos, Z. Ma (2023). DeepTrip: A deep learning model for the individual next trip prediction with arbitrary prediction times. IEEE Transactions on Intelligent Transportation Systems (accepted).
  12. Z. Qin, Z. Ma*, P. Zhang (2022). DeepAGS: Deep Learning with Activity, Geography and Sequential Information for Individual Trip Destination Prediction, Transportation Research Procedia (in press)
  13. K. Tiong, Z. Ma*, CW. Palmqvis (2022). Prediction of Real-time Train Arrival Times Along The Swedish Southern Mainline. WIT Transactions on The Built Environment, 213: 135 – 143.
  14. L.Wang, Z. Ma*, C. Dong, H. Wang (2022). Human-centric multimodal deep (HMD)traffic signal control. IET Intelligent Transport System.1–10.https://doi.org/10.1049/itr2.12300
  15. Tiong, K., Ma, Z.* and Palmqvist, C.W., 2022, October. Real time Train Arrival Time Prediction at Multiple Stations and Arbitrary Times. In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), IEEE.
  16. Q. Zhang, Z. Ma*, P. Zhang, E. Jenelius (2023). Mobility Knowledge Graph: Review and its Application in Public Transport, Transportation Research Board 102th Annual Meeting, Washington D.C., United States.
  17.  J. Liu, Z. Ma, L. Wen, N. Zhang, Z. Qian (2023). Urban Rail Transit Fare Reconciliation Method Using Multi-source Data, Transportation Research Board 103th Annual Meeting, Washington D.C., United States.
  18. P. Zhang, Z. Ma* (2022). Individual Mobility Prediction Review: Data, Problem, Method and Application. Multimodal Transportation (Accepted).
  19. L. Wang, Z. Ma*, C. Dong, H. Wang (2022). Human-Centric Multimodal Deep (HMD) Traffic Signal Control. Transportation Research Board 101th Annual Meeting, Washington D.C., United States.
  20. C. Wu, Z. Ma* and I. Kim. (2021) Traffic Signal Control Using Multi-Agent Reinforcement Learning with Knowledge, 12rd Computational Transportation Science (CTS) Conference, Harbin, China
  21. C. Wu, Z. Ma* and I. Kim. (2020) Multi-Agent Reinforcement Learning for Traffic Signal Control: Algorithms and Robustness Analysis, 23rd IEEE Conference on Intelligent Transportation Systems 2020, Rhodes, Greece
  22. Z. Ma*, H. Koutsopoulos, L. Ferreira, M. Mesbah (2017). Estimation of Trip Travel Time Distribution Using a Generalized Markov Chain Approach, Transportation Research Part C: Emerging Technologies, 74, 1–21.

PT&R - Data Management and Simulation Tools

  1. P. Zhang, Z. Ma*, X. Weng (2021). Detecting Invalid Associations between Fare Machines and Metro Stations Using Smart Card Data. Journal of Advanced Transportation. https://doi.org/10.1155/2021/5283283
  2. P. Zhang, Z. Ma*, X. Weng, H. Koutsopoulos (2021). Recovering the Association Between Unlinked Fare Machines and Stations Using Automated Fare Collection Data in Metro Systems. Transportation Research Record, doi.org/10.1177/03611981211045370
  3. B. Mo, Z. Ma*, H. N. Koutsopoulos, and J. Zhao (2021). Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card Data. Journal of Advanced Transportation, doi.org/10.1155/2021/5597130.
  4. B. Mo, Z. Ma*, H. N. Koutsopoulos, and J. Zhao (2020). Capacity-Constrained Network Performance Model for Urban Rail Systems. Transportation Research Record, 2674(5), 59–69.
  5. B. Mo, Z. Ma*, H. Koutsopoulos, J. Zhao (2020). Calibrating Route Choice for Urban Rail System: A Comparative Analysis Using Simulation-based Optimization Methods. Transportation Research Board 99th Annual Meeting, Washington D.C., United States.

PT&R- Capacity Management

  1. J. Högdahl, Z. Ma, L. Wang (2024). Reinforcement Learning Based Robust Railway Timetabling to Resolve Robustness Vulnerabilities. Transportforum 2024, Linköping, Sweden.
  2. J. Högdahl, Z. Ma, L. Wang (2023). Reinforcement Learning Based Robust Railway Timetabling to Resolve Robustness Vulnerabilities. The 4th International Workshop on “Artificial Intelligence for RAILwayS”, Ischia, Italy.
  3. A. Crespo, S. Chai, F. Weidinger, J. Högdahl, Z. Ma, C. Cervelló-Pastor, C. Steinbach, S. Sallent, A. Oetting (2023). Use of Hybrid Methods for the Enhancement of Real-Time Railway Traffic Control (Dispatching). The 4th International Workshop on “Artificial Intelligence for RAILwayS”, Ischia, Italy.
  4. L. Wang, Z. Ma*, P. Zhang, X. Chen, B. Mo, P. Duan (2023). Individual Longitudinal Adoption Pattern Analysis under Fare Incentives Using Smart Card Data, Transportation Research Board 102th Annual Meeting, Washington D.C., United States.
  5. Z. Ma*, H. N. Koutsopoulos, A. Halvorsen, J. Zhao (2021). Demand Management in Urban Railway Systems: Strategy, Design, Evaluation, Monitoring, and Technology. Book Title: Handbook on Public Transport Research, Edward Elgar Handbooks in Transport series. ISBN: 9781788978651, Edward Elgar publishing.
  6. H. Koutsopoulos*, Z. Ma, P. Noursalehi, Y. Zhu (2019). Transit Data Analytics for Planning, Monitoring, Control and Information. Book Title: Mobility Patterns, Big Data and Transportation Analytics, ISBN: 9780128129708, Elsevier.
  7. Z. Ma*, H. Koutsopoulos, T. Liu, A. Basu (2020). Behavioral Response to Promotion-based Public Transport Demand Management: Longitudinal Analysis and Implications for Optimal Promotion Design. Transportation Research Part A: Policy and Practice, 141, 356–372.
  8. A. Halvorsen, H. N. Koutsopoulos, Z. Ma*, J. Zhao (2020). Demand Management in Congested Public Transportation Systems: A Framework and Application, Transportation, 47, 2337–2365 (translated into Chinese by CITIC publishing group, http://bijiao.caixin.com/2020/cs_110/).
  9. K. Tuncel*, H. Koutsopoulos, Z. Ma (2020), Data Driven Real-Time Platform Crowding PredictionUsing Automated Fare Collection and Vehicle Location Data in Urban Railway Systems, Transportation Research Board 99th Annual Meeting, Washington D.C., United States.
  10. Z. Ma*, H. N. Koutsopoulos. (2019). Optimal Design of Promotion Based Demand Management Strategies in Urban Rail Systems. Transportation Research Part C: Emerging Technologies 109, 155–173.
  11. Z. Ma*, H. N. Koutsopoulos, Y. Chen, N. H. M. Wilson (2019). Estimation of Denied Boarding in Urban Rail Systems: Alternative Formulations and Comparative Analysis, Transportation Research Record, 2673(11), 771–778.
  12. Z. Ma*, H. Koutsopoulos, Y. Chen (2018). Optimal Design of Transit Demand Management Strategies: Case Study in Hong Kong, Transportation Research Board 97th Annual Meeting, Washington D.C., United States

PT&R - Incident Management

  1. Y. Zhao, J. Li, Z. Cheng, H. Peng, Z. Ma* (2024). Irregular Demand Pattern Analysis Under Unplanned Disruptions in Urban Rail Systems, Transportation Research Board 103th Annual Meeting, Washington D.C., United States.
  2. T. Liu, Z. Ma*, H. Koutsopoulos (2023). Impact Duration Model of Unplanned Disruptions in Urban Rail Systems. Urban Rail Transit. 10.1007/s40864-023-00197-y
  3. W. Sun, X. Chen, Z. Ma (2023). Statistical and Machine Learning Models for Incident Delay Prediction in Urban Railway Systems: A Methodology Review, Transportation Research Board 102th Annual Meeting, Washington D.C., United States.
  4. Y. Zhao, Z. Ma*, H. Peng (2023). Irregular Passenger Demand Identification under Disruptions: A Robust Principal Component Analysis Approach, Transportation Research Board 102th Annual Meeting, Washington D.C., United State
  5. Y. Zhao, Z. Ma* (2022). Naïve Bayes Combination Model for Short-term Metro Passenger Flow Prediction under Planned Events. Transportation Research Record (Accepted).
  6. X. Chen, Z. Ma*, Z. Li (2022). Unplanned Disruption Analysis in Urban Railway Systems: Evidence from Hong Kong. Transportation Research Record (Accepted).
  7. T. Liu, Z. Ma*, H. Koutsopoulos (2022). Impact Duration Model of Unplanned Disruptions in Urban Rail Systems. Transportation Research Record (Accepted).
  8. Y. Zhao, Z. Ma*, X. Jiang, H. Koutsopoulos (2021). Short-term Metro Passenger Flow Prediction Capturing the Impact of Unplanned Events. Transportation Research Record, doi.org/10.1177/03611981211037553.
  9. T. Liu, Z. Ma*, H. N. Koutsopoulos (2021). Unplanned Disruption Analysis in Urban Railway Systems Using Smart Card Data. Urban Rail Transit, 7, 177–190.

PT&R - Demand Prediction

  1. tion. Multimodal Transportation (Accepted).C. Zhong, P. Wu, Q. Zhang and Z. Ma (2023). Online prediction of network-level public transport demand based on principle component analysis. Communications in Transportation Research 2023. Vol. 3 Pages 100093.
  2. W. Jiang, Z. Ma*, H. Koutsopoulos (2022). Deep Learning for Short-Term Origin-Destination Passenger Flow Prediction under Partial Observability in Urban Railway Systems. Neural Computing and Applications (Accepted).
  3. Y. Zhao, Z. Ma*, Y. Yang, W. Jiang and X. Jiang (2020). Short-Term Passenger Flow Prediction with Decomposition in Urban Railway Systems. IEEE Access, doi:10.1109/ACCESS.2020.3000242.
  4. Y. Zhao, L. Ren, Z. Ma*, and X. Jiang (2020). A Novel Three-Stage Framework for Prioritizing and Selecting Feature Variables for Short-Term Metro Passenger Flow Prediction. Transportation Research Record, 2674 (8), 192-205
  5. Z. Ma*, J. Xing, M. Mesbah, L. Ferreira (2014). Predicting Short-Term Bus Passenger Demand Using a Pattern Hybrid Approach, Transportation Research Part C: Emerging Technologies, 39, 148–163.

PT&R- Operations and Reliability

  1. Q. Zhang, Z. Ma*, Y. Ling, Z. Qin, P. Zhang, Z. Zhang (2024). Causal Graph Discovery for Urban Bus Operation Delays: A case in Stockholm, Transportation Research Board 103th Annual Meeting, Washington D.C., United States.
  2. S. Zahedi, H.N. Koutsopoulos, Z. Ma (2023). Dynamic interlining in bus operations. Transportation, https://link.springer.com/article/10.1007/s11116-023-10440-x
  3. S. Cui, K. Gao, B. Yu, Z. Ma, A. Najafi (2023). Joint optimal vehicle and recharging scheduling for mixed bus fleets under limited chargers. Transportation Research Part E: Logistics and Transportation Review, 180, 103335
  4. K.Y. Tiong*, Z. Ma and C.W. Palmqvist (2023). Evaluation Framework for Train Delays Prediction Models. 7th International Conference on Transportation Information and Safety (ICTIS 2023), China
  5. K.Y. Tiong*, Z. Ma and C.W. Palmqvist (2023). Real-time High-Speed Train Delay Prediction using Seemingly Unrelated Regression Models. 16th World Conference on Transport Research, Montreal WCTRS
    2023, Canada
  6. K.Y. Tiong*, Z. Ma and C.W. Palmqvist (2023). Analyzing factors contributing to real-time train arrival delays using seemingly unrelated regression models. Transportation Research Part A: Policy and Practice, 174, p.103751. 
  7. K.Y. Tiong, Z. Ma* and C.W. Palmqvist. (2023). A review of data-driven approaches to predict train delays. Transportation Research Part C: Emerging Technologies, 148, p.104027.
  8. Z. Ma*, H. Koutsopoulos, L. Ferreira (2017). Quantile Regression Analysis of Transit Travel Time Reliability Using Automatic Vehicle Location and Fare Card Data, Transportation Research Record, 2652, 19–29.
  9. Z. Ma*, H. Koutsopoulos, L. Ferreira, M. Mesbah (2016). Estimation of Traffic State Transition Probabilities and its Application to Travel Time Prediction, Transportation Research Board 95th Annual Meeting 2016, Washington D.C., United States.
  10. Z. Ma*, L. Ferreira, M. Mesbah, S. Zhu (2016). Modelling Distributions of Travel Time Reliability For Bus Operations, Journal of Advanced Transportation, 50(1), 6–24.
  11. Z. Ma*, L. Ferreira, M. Mesbah, A. Hojati (2015). Modelling Bus Travel Time Reliability Using Supply and Demand Data From AVL and Smart Card Systems, Transportation Research Record, 2533, 17–27.
  12. Z. Ma*, L. Ferreira, M. Mesbah (2014). Measuring Service Reliability Using Automatic Vehicle Location Data, Mathematical Problems in Engineering, http://dx.doi.org/10.1155/2014/468563.
  13. Z. Ma*, L. Ferreira, M. Mesbah (2013) A Framework for the Development of Bus Service Reliability Measures, 34th Australian Transportation Research Forum, Brisbane, Australia.

PT&R - Travel Pattern and Behavior

  1. Q. Zhang, Z. Ma*, P. Zhang, E. Jeneilius (2023). Mobility Knowledge Graph: Review and its Application in Public Transport. Transportation https://doi.org/10.1007/s11116-023-10451-8
  2. L. Wang, X. Chen,  Z Ma*, P. Zhang, B. Mo, P. Duan (2023). Data-Driven Analysis and Modeling of Individual Longitudinal Behavior Response to Fare Incentives in Public Transport. Transportation, (accepted)
  3. B. Mo, Z. Ma*, H. N. Koutsopoulos, and J. Zhao (2022). Ex-Post Path Choice Estimation for Urban Rail Systems Using Smart Card Data: An Aggregated Time-Space Hypernetwork Approach. Transportation Science, https://doi.org/10.1287/trsc.2022.1177
  4. Z. Ma*, B. Mo, A. Adolfsson (2022). Data-driven Route Choice Estimation In Urban Rails. Informs Annual Meeting, Indianapolis, United States.
  5. Q. Zhang, P. Zhang, E. Jenelius, Z. Ma* (2022). Understand Travel Activities: Mobility KnowledgeGraph Construction from Smart Card Data. Swedish Transport Forum 2022, Linköping, Sweden.
  6. M. Eltved*, H. Koutsopoulos, N. H. M. Wilson, K. Tuncel, Z. Ma (2020), Understanding Reverse Routing Path Choice Behavior in Congested Metro Systems, Transportation Research Board 99th Annual Meeting.
  7. W Jiang, Z Ma*, I Kim, S Lee. Revealing Mobility Regularities in Urban Rail Systems (2020). Procedia Computer Science, 170, 219-226
  8. J. Zhou*, Neil Sipe, Z. Ma, D. Babiano, S. Darchen. (2019). Monitoring Transit-Served Areas With Smartcard Data: A Brisbane Case Study, Journal of Transport Geography, 76, 265–275
  9. N. Nassir*, M. Hickman, Z. Ma (2019). A Strategy-Based Recursive Path Choice Model For Public Transit Smart Card Data, Transportation Research Part B: Methodological, 126, 528–548.
  10. J. Zhou*, Neil Sipe, Z. Ma, D. Babiano, S. Darchen. (2019). Monitoring Transit-Served Areas With Smartcard Data: A Brisbane Case Study, Journal of Transport Geography, 76, 265–275.
  11. N. Nassir*, M. Hickman, Z. Ma (2017). Statistical Inference of Transit Passenger Boarding Strategies from Farecard Data, Transportation Research Record, 2652, 8–18.
  12. N. Nassir*, M. Hickman, Z. Ma (2015). Activity Detection and Transfer Identification for Public Transit Fare Card Data, Transportation, 42, 683–705.
  13. N. Nassir*, M. Hickman, Z. Ma (2015). Mining Transit Passenger Boarding Strategies from Fare Card Data, 13th Conferences on Advanced Systems in Public Transport, Rotterdam, Netherlands