| Predicting short-term bus passenger demand using a pattern hybrid approach Z Ma, J Xing, M Mesbah, L Ferreira Transportation Research Part C: Emerging Technologies 39, 148-163, 2014 | 149 | 2014 |
| Activity detection and transfer identification for public transit fare card data N Nassir, M Hickman, ZL Ma Transportation 42, 683-705, 2015 | 135 | 2015 |
| Estimation of trip travel time distribution using a generalized Markov chain approach Z Ma, HN Koutsopoulos, L Ferreira, M Mesbah Transportation Research Part C: Emerging Technologies 74, 1-21, 2017 | 134 | 2017 |
| Modeling distributions of travel time variability for bus operations Z Ma, L Ferreira, M Mesbah, S Zhu Journal of Advanced Transportation 50 (1), 6-24, 2016 | 80 | 2016 |
| A strategy-based recursive path choice model for public transit smart card data N Nassir, M Hickman, ZL Ma Transportation Research Part B: Methodological 126, 528-548, 2019 | 65 | 2019 |
| Modeling bus travel time reliability with supply and demand data from automatic vehicle location and smart card systems ZL Ma, L Ferreira, M Mesbah, AT Hojati Transportation Research Record 2533 (1), 17-27, 2015 | 57 | 2015 |
| Demand management of congested public transport systems: a conceptual framework and application using smart card data A Halvorsen, HN Koutsopoulos, Z Ma, J Zhao Transportation 47 (5), 2337-2365, 2020 | 55 | 2020 |
| Transit data analytics for planning, monitoring, control, and information HN Koutsopoulos, Z Ma, P Noursalehi, Y Zhu Mobility patterns, big data and transport analytics, 229-261, 2019 | 52 | 2019 |
| Improved IMM algorithm for nonlinear maneuvering target tracking L Gao, J Xing, Z Ma, J Sha, X Meng Procedia Engineering 29, 4117-4123, 2012 | 46 | 2012 |
| Individual mobility prediction review: Data, problem, method and application Z Ma, P Zhang Multimodal transportation 1 (1), 100002, 2022 | 45 | 2022 |
| Deep learning for short-term origin–destination passenger flow prediction under partial observability in urban railway systems W Jiang, Z Ma, HN Koutsopoulos Neural Computing and Applications, 1-18, 2022 | 43 | 2022 |
| A review of data-driven approaches to predict train delays KY Tiong, Z Ma, CW Palmqvist Transportation Research Part C: Emerging Technologies 148, 104027, 2023 | 42 | 2023 |
| Capacity-constrained network performance model for urban rail systems B Mo, Z Ma, HN Koutsopoulos, J Zhao Transportation Research Record 2674 (5), 59-69, 2020 | 41 | 2020 |
| Mobility patterns, big data and transport analytics: tools and applications for modeling C Antoniou, L Dimitriou, F Pereira Elsevier, 2018 | 40 | 2018 |
| Online prediction of network-level public transport demand based on principle component analysis C Zhong, P Wu, Q Zhang, Z Ma Communications in Transportation Research 3, 100093, 2023 | 35 | 2023 |
| Measuring service reliability using automatic vehicle location data Z Ma, L Ferreira, M Mesbah Mathematical Problems in Engineering 2014 (1), 468563, 2014 | 35 | 2014 |
| Behavioral response to promotion-based public transport demand management: Longitudinal analysis and implications for optimal promotion design Z Ma, HN Koutsopoulos, T Liu, AA Basu Transportation Research Part A: Policy and Practice 141, 356-372, 2020 | 33 | 2020 |
| Quantile regression analysis of transit travel time reliability with automatic vehicle location and farecard data Z Ma, S Zhu, HN Koutsopoulos, L Ferreira Transportation Research Record 2652 (1), 19-29, 2017 | 33 | 2017 |
| Near-on-demand mobility. The benefits of user flexibility for ride-pooling services Z Ma, HN Koutsopoulos Transportation Research Part C: Emerging Technologies 135, 2022 | 28 | 2022 |
| Optimal design of promotion based demand management strategies in urban rail systems Z Ma, HN Koutsopoulos Transportation Research Part C: Emerging Technologies 109, 155-173, 2019 | 28 | 2019 |
Haris N KoutsopoulosNortheastern UniversityDirección de correo verificada de neu.edu
