Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models X Zhao, X Yan, A Yu, P Van Hentenryck Travel Behaviour and Society 20, 22-35, 2020 | 310* | 2020 |
Integrating ridesourcing services with public transit: An evaluation of traveler responses combining revealed and stated preference data X Yan, J Levine, X Zhao Transportation Research Part C: Emerging Technologies 105, 683-696, 2019 | 233 | 2019 |
COPEWELL: A Conceptual Framework and System Dynamics Model for Predicting Community Functioning and Resilience After Disasters JM Links, BS Schwartz, S Lin, N Kanarek, J Mitrani-Reiser, TK Sell, ... Disaster Medicine and Public Health Preparedness, 2017 | 114 | 2017 |
Modelling and interpreting pre-evacuation decision-making using machine learning X Zhao, R Lovreglio, D Nilsson Automation in Construction 113, 103140, 2020 | 105 | 2020 |
Identifying key factors associated with ridesplitting adoption rate and modeling their nonlinear relationships Y Xu, X Yan, X Liu, X Zhao Transportation Research Part A: Policy and Practice 144, 170-188, 2021 | 97 | 2021 |
Using machine learning for direct demand modeling of ridesourcing services in Chicago X Yan, X Liu, X Zhao Journal of Transport Geography 83, 102661, 2020 | 96 | 2020 |
A spatiotemporal analysis of e-scooters’ relationships with transit and station-based bikeshare X Yan, W Yang, X Zhang, Y Xu, I Bejleri, X Zhao Transportation research part D: transport and environment 101, 103088, 2021 | 66 | 2021 |
A segment-level model of shared, electric scooter origins and destinations LA Merlin, X Yan, Y Xu, X Zhao Transportation Research Part D: Transport and Environment 92, 102709, 2021 | 59 | 2021 |
Mobility-on-demand versus fixed-route transit systems: An evaluation of traveler preferences in low-income communities X Yan, X Zhao, Y Han, P Van Hentenryck, T Dillahunt Transportation Research Part A: Policy and Practice 148, 481-495, 2021 | 55 | 2021 |
Modeling evacuation decisions in the 2019 Kincade fire in California ED Kuligowski, X Zhao, R Lovreglio, N Xu, K Yang, A Westbury, D Nilsson, ... Safety science 146, 105541, 2022 | 53 | 2022 |
Assessing food system vulnerabilities: a fault tree modeling approach GM Chodur, X Zhao, E Biehl, J Mitrani-Reiser, R Neff BMC public health 18, 1-11, 2018 | 47 | 2018 |
Identifying latent shared mobility preference segments in low-income communities: Ride-hailing, fixed-route bus, and mobility-on-demand transit X Wang, X Yan, X Zhao, Z Cao Travel Behaviour and Society 26, 134-142, 2022 | 38 | 2022 |
Estimating wildfire evacuation decision and departure timing using large-scale GPS data X Zhao, Y Xu, R Lovreglio, E Kuligowski, D Nilsson, TJ Cova, A Wu, X Yan Transportation research part D: transport and environment 107, 103277, 2022 | 34 | 2022 |
Using Artificial Intelligence for Safe and Effective Wildfire Evacuations X Zhao, R Lovreglio, E Kuligowski, D Nilsson Fire Technology, 2020 | 29 | 2020 |
Modeling heterogeneity in mode-switching behavior under a mobility-on-demand transit system: An interpretable machine learning approach X Zhao, X Yan, P Van Hentenryck arXiv preprint arXiv:1902.02904, 2019 | 26* | 2019 |
Machine learning approach for spatial modeling of ridesourcing demand X Zhang, X Zhao Journal of Transport Geography 100, 103310, 2022 | 24 | 2022 |
Form-finding analysis for a new type of cable–strut tensile structures generated by semi-regular tensegrity J Lu, X Dong, X Zhao, X Wu, G Shu Advances in Structural Engineering 20 (5), 772-783, 2017 | 22 | 2017 |
Micromobility trip origin and destination inference using general bikeshare feed specification data Y Xu, X Yan, VP Sisiopiku, LA Merlin, F Xing, X Zhao Transportation Research Record 2676 (11), 223-238, 2022 | 20* | 2022 |
Predicting and assessing wildfire evacuation decision-making using machine learning: findings from the 2019 Kincade Fire N Xu, R Lovreglio, ED Kuligowski, TJ Cova, D Nilsson, X Zhao Fire Technology 59 (2), 793-825, 2023 | 18 | 2023 |
Form finding analysis of cable-strut tensile dome based on tensegrity torus J Lu, X Wu, X Zhao, G Shu Engineering Mechanics 32 (6), 66-71, 2015 | 16* | 2015 |