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Baichuan Mo
Baichuan Mo
PhD @ MIT, Research Scientist @ Lyft
Bestätigte E-Mail-Adresse bei mit.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Modeling epidemic spreading through public transit using time-varying encounter network
B Mo, K Feng, Y Shen, C Tam, D Li, Y Yin, J Zhao
Transportation Research Part C: Emerging Technologies 122, 102893, 2021
1032021
Deep neural networks for choice analysis: Architecture design with alternative-specific utility functions
S Wang, B Mo, J Zhao
Transportation Research Part C: Emerging Technologies 112, 234-251, 2020
832020
Competition between shared autonomous vehicles and public transit: A case study in Singapore
B Mo, Z Cao, H Zhang, Y Shen, J Zhao
Transportation Research Part C: Emerging Technologies 127, 103058, 2021
59*2021
Impact of built environment on first-and last-mile travel mode choice
B Mo, Y Shen, J Zhao
Transportation Research Record 2672 (6), 40-51, 2018
472018
Speed profile estimation using license plate recognition data
B Mo, R Li, X Zhan
Transportation Research Part C: Emerging Technologies 82, 358-378, 2017
452017
Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark
S Wang, B Mo, S Hess, J Zhao
arXiv preprint arXiv:2102.01130, 2021
442021
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
402020
Impact of pricing policy change on on-street parking demand and user satisfaction: A case study in Nanning, China
B Mo, H Kong, H Wang, XC Wang, R Li
Transportation Research Part A: Policy and Practice 148, 445-469, 2021
392021
Individual mobility prediction in mass transit systems using smart card data: an interpretable activity-based hidden Markov approach
B Mo, Z Zhao, HN Koutsopoulos, J Zhao
IEEE Transactions on Intelligent Transportation Systems 23 (8), 12014-12026, 2022
37*2022
Estimating dynamic origin–destination demand: A hybrid framework using license plate recognition data
B Mo, R Li, J Dai
Computer‐Aided Civil and Infrastructure Engineering 35 (7), 734-752, 2020
372020
Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks
S Wang, B Mo, J Zhao
Transportation Research Part B: Methodological 146, 333-358, 2021
332021
Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card and Train Movement Data
B Mo, Z Ma, HN Koutsopoulos, J Zhao
Journal of Advanced Transportation 2021, 5597130, 2021
31*2021
Inferring passenger responses to urban rail disruptions using smart card data: A probabilistic framework
B Mo, HN Koutsopoulos, J Zhao
Transportation Research Part E: Logistics and Transportation Review 159, 102628, 2022
212022
Ex post path choice estimation for urban rail systems using smart card data: An aggregated time-space hypernetwork approach
B Mo, Z Ma, HN Koutsopoulos, J Zhao
Transportation Science 57 (2), 313-335, 2023
18*2023
Impacts of subjective evaluations and inertia from existing travel modes on adoption of autonomous mobility-on-demand
B Mo, QY Wang, J Moody, Y Shen, J Zhao
Transportation Research Part C: Emerging Technologies 130, 103281, 2021
182021
Robust path recommendations during public transit disruptions under demand uncertainty
B Mo, HN Koutsopoulos, ZJM Shen, J Zhao
Transportation Research Part B: Methodological 169, 82-107, 2023
142023
Impact of unplanned long-term service disruptions on urban public transit systems
B Mo, MY Von Franque, HN Koutsopoulos, JP Attanucci, J Zhao
IEEE Open Journal of Intelligent Transportation Systems 3, 551-569, 2022
8*2022
Built environment and autonomous vehicle mode choice: A first-mile scenario in Singapore
Y Shen, B Mo, X Zhang, J Zhao
Transportation Research Board 98th Annual Meeting Transportation Research Board, 2019
72019
Network performance model for urban rail systems
B Mo
Massachusetts Institute of Technology, 2020
62020
Large Language Models for Travel Behavior Prediction
B Mo, H Xu, D Zhuang, R Ma, X Guo, J Zhao
arXiv preprint arXiv:2312.00819, 2023
52023
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