Publikationer av Zhenliang Ma
Refereegranskade
Artiklar
[1]
Y. Song et al., "A state-based inverse reinforcement learning approach to model activity-travel choices behavior with reward function recovery," Transportation Research Part C : Emerging Technologies, vol. 158, 2024.
[2]
K. Y. Tiong, Z. Ma och C.-W. Palmqvist, "A review of data-driven approaches to predict train delays," Transportation Research Part C : Emerging Technologies, vol. 148, s. 104027, 2023.
[3]
K. Tuncel, H. N. Koutsopoulos och Z. Ma, "An integrated ride-matching and vehicle-rebalancing model for shared mobility on-demand services," Computers & Operations Research, vol. 159, 2023.
[4]
K. Y. Tiong, Z. Ma och C.-W. Palmqvist, "Analyzing factors contributing to real-time train arrival delays using seemingly unrelated regression models," Transportation Research Part A : Policy and Practice, vol. 174, 2023.
[5]
P. Zhang, H. N. Koutsopoulos och Z. Ma, "DeepTrip : A Deep Learning Model for the Individual Next Trip Prediction With Arbitrary Prediction Times," IEEE transactions on intelligent transportation systems (Print), vol. 24, no. 6, s. 5842-5855, 2023.
[6]
B. Mo et al., "Ex Post Path Choice Estimation for Urban Rail Systems Using Smart Card Data : An Aggregated Time-Space Hypernetwork Approach," Transportation Science, vol. 57, no. 2, s. 313-335, 2023.
[7]
L. Wang et al., "Human-centric multimodal deep (HMD) traffic signal control," IET Intelligent Transport Systems, vol. 17, no. 4, s. 744-753, 2023.
[8]
Z. Liu et al., "Integrated optimization of timetable, bus formation, and vehicle scheduling in autonomous modular public transport systems," Transportation Research Part C : Emerging Technologies, vol. 155, 2023.
[9]
S. Cui et al., "Joint optimal vehicle and recharging scheduling for mixed bus fleets under limited chargers," Transportation Research Part E : Logistics and Transportation Review, vol. 180, 2023.
[10]
Q. Zhang et al., "Mobility knowledge graph : review and its application in public transport," Transportation, 2023.
[11]
T. Liu, H. N. Koutsopoulos och Z. Ma, "Modeling the Duration of the Impact of Unplanned Disruptions on Passenger Trips Using Smartcard Data in Urban Rail Systems," URBAN RAIL TRANSIT, vol. 9, no. 3, s. 266-279, 2023.
[12]
C. Zhong et al., "Online prediction of network-level public transport demand based on principle component analysis," Communications in Transportation Research, vol. 3, 2023.
[13]
B. Liu et al., "Passenger flow anomaly detection in urban rail transit networks with graph convolution network-informer and Gaussian Bayes models," Philosophical Transactions. Series A : Mathematical, physical, and engineering science, vol. 381, no. 2254, 2023.
[14]
Q. Zhang et al., "User-station attention inference using smart card data : a knowledge graph assisted matrix decomposition model," Applied intelligence (Boston), vol. 53, no. 19, s. 21944-21960, 2023.
[15]
Z. Ma och P. Zhang, "Individual Mobility Prediction Review : Data, Problem, Method and Application," Multimodal Transportation, vol. 1, no. 1, s. 100002, 2022.
[16]
Y. Zhao och Z. Ma, "Naïve Bayes-Based Transition Model for Short-Term Metro Passenger Flow Prediction under Planned Events," Transportation Research Record, vol. 2676, no. 9, s. 309-324, 2022.
[17]
Z. Ma och H. N. Koutsopoulos, "Near-on-demand mobility. The benefits of user flexibility for ride-pooling services," Transportation Research Part C : Emerging Technologies, vol. 135, 2022.
[18]
S. M. H. Moosavi et al., "Understanding and Predicting the Usage of Shared Electric Scooter Services on University Campuses," Applied Sciences, vol. 12, no. 18, 2022.
[19]
X. Chen, Z. Ma och Z. Li, "Unplanned Disruption Analysis and Impact Modeling in Urban Railway Systems," Transportation Research Record, vol. 2676, no. 10, s. 16-27, 2022.
Konferensbidrag
[20]
Y. Ling et al., "Analyzing factors contributing to bus driver deceleration behavior at intersections using multi-source naturalistic driving data," i Transportation Research Board 103th Annual Meeting, Washington D.C., United States, 2024.
[21]
Q. Zhang et al., "Causal Graph Discovery for Urban Bus Operation Delays : A case in Stockholm," i The 103rd Transportation Research Board (TRB) Annual Meeting, January 7–11, 2024, Washington, DC, USA, 2024.
[22]
Y. Lei et al., "Choice Behavior and Diffusion Impact Analysis of Connected and Autonomous Vehicles Travelers with Managed Lanes," i Transportation Research Board 103th Annual Meeting, Washington D.C., United States, 2024.
[23]
Z. Yangyang et al., "Irregular Demand Pattern Analysis Under Unplanned Disruptions in Urban Rail Systems," i Transportation Research Board 103th Annual Meeting, Jan 7-11, 2024, Washington DC, United States, 2024.
[24]
S. Zhu et al., "Learning about Traffic Engineering through Rapid Prototyping–A Case Study of Car Following Microscopic Simulation," i Transportation Research Board 103th Annual Meeting, Washington D.C., United States, 2024.
[25]
Q. Zhang, Z. Ma och E. Jenelius, "Data-Driven Causality Discovery for Bus Arrival Delays in Urban Public Networks," i The 12th Annual Swedish Transport Research Conference (STRC 2023), 16 - 17 October 2023, Stockholm, Sweden, 2023.
[26]
C. Wu, I. Kim och Z. Ma, "Deep Reinforcement Learning Based Traffic Signal Control : A Comparative Analysis," i 14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023, 2023, s. 275-282.
[27]
Z. Qin, P. Zhang och Z. Ma, "DeepAGS: Deep Learning with Activity, Geography and Sequential Information for Individual Trip Destination Prediction," i 2022 Conference Proceedings Transport Research Arena, TRA Lisbon 2022, 2023, s. 4255-4262.
[28]
H. Chen et al., "Mixed Integer Formulation with Linear Constraints forIntegrated Service Operations and Traveler Choices inMultimodal Mobility Systems," i hEART 2023: 11th Symposium of the European Association for Research in Transportation, September 6-8, 2023, 2023.
[29]
Q. Zhang et al., "Mobility Knowledge Graph : Review and its application in public transport," i Transportation Research Board (TRB) 102nd Annual Meeting, Washington DC, United States, January 8-12, 2023, 2023.
[30]
J. Högdahl, Z. Ma och L. Wang, "Reinforcement Learning Based Robust Railway Timetabling to Resolve Robustness Vulnerabilities," i The 4th International Workshop on Artificial Intelligence for Railways (AI4RAILS 2023), co-located with the International Conference on Optimization and Decision Science (ODS 2023), 2023.
[31]
Y. Ling et al., "STMA-GCN_PedCross: Skeleton Based Spatial-Temporal Graph Convolution Networks with Multiple Attentions for Fast Pedestrian Crossing Intention Prediction," i 2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023, 2023, s. 500-506.
[32]
A. Crespo Materna et al., "Use of Hybrid Methods for the Enhancement of Real-Time Railway Traffic Control (Dispatching)," i The 4th International Workshop on Artificial Intelligence for Railways (AI4RAILS 2023), September 4th, 2023, Ischia, Italy, 2023.
[33]
K. Y. Tiong, Z. Ma och C. W. Palmqvist, "Prediction of real-time train arrival times along the Swedish southern mainline," i Computers in Railways XVIII : Railway Engineering Design and Operation, 2022, s. 135-143.
[34]
K. Tiong, Z. Ma och C.-W. Palmqvist, "Real-time Train Arrival Time Prediction at Multiple Stations and Arbitrary Times," i 2022 IEEE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2022, s. 793-798.
Icke refereegranskade
Konferensbidrag
[35]
Q. Zhang et al., "Mobility knowledge graph and assisted application," i 11th Swedish Transport Research Conference, 18-19 October 2022, Lund, Sweden, 2022.
[36]
Q. Zhang et al., "Understand Travel Activities : Mobility Knowledge Graph Construction from Smart Card Data," i Transportforum, Linköping, 16–17 juni 2022, 2022.
Rapporter
[37]
E. Jenelius et al., "Prestudy on Establishing a Research Project on Forecasting Methodology," Stockholm : KTH Royal Institute of Technology, TRITA-ABE-RPT, 2328, 2023.
Senaste synkning med DiVA:
2024-04-19 00:03:21