Peer-Reviewed Journal Publications

Note: Lab members (including the director and students) are in bold font.

Published/In Press

[30] Arshad, H., Emblemsvåg, J., & Zhao, X. A data-driven, scenario-based human evacuation model for passenger ships addressing hybrid uncertainty. International Journal of Disaster Risk Reduction. (Accepted)

[29] Zhang, X., Zhou, Z., Xu, Y., & Zhao, X. Analyzing spatial heterogeneity of ridesourcing demand determinants using explainable machine learning. Journal of Transport Geography. (Accepted)

[28] Forrister, A., Kuligowski, E. D., Sun, Y., Yan, X., Lovreglio, R., Cova, T. J., & Zhao, X. (2024). Analyzing risk perception, evacuation decision and delay time: A case study of the 2021 Marshall Fire in Colorado. Travel Behaviour and Society, 35, 100729.

[27] Su, L., Yan, X., & Zhao, X. (2024). Spatial Spatial equity of micromobility systems: A comparison of shared e-scooters and docked bikeshare in Washington DC. Transport Policy, 145, 25-36.

[26] Yang, W., Jafarzadehfadaki, M., Yan, X., Zhao, X., Jin, X., Frolich, D., & Sisiopiku, V. P. (2023). Shared e-scooters: User characteristics and usage patterns across four U.S. cities. Transportation Research Record. 

[25] Yan, X., Zhao, X., Broaddus, A., Johnson, J., & Srinivasan, S. (2023). Exploring the potential of shared e-scooters as a last-mile complement to public transit. Transportation Research Part D: Transport and Environment, 117, 103640.

[24] Xu, Y., Zhao, X., Zhang, X., & Paliwal, M. (2023). Real-time forecasting of dockless scooter-sharing demand: A spatio-temporal multi-graph transformer approach. IEEE Transactions on Intelligent Transportation Systems.

[23] Xu, N., Lovreglio, R., Kuligowski, E., Cova, T., Nilsson, D., & Zhao, X. (2023). Predicting and assessing wildfire evacuation decision-making using machine learning: Findings from the 2019 Kincade Fire. Fire Technology. 

[22] Wu, A., Yan, X., Kuligowski, E., Lovreglio, R., Nilsson, D., Cova, T. J., Xu, Y., & Zhao, X. (2022). Wildfire evacuation decision modeling using GPS data. International Journal of Disaster Risk Reduction, 103373.

[21] Xu, Y.^, Zhao, X.^, Lovreglio, R., Kuligowski, E., Nilsson, D., Cova, T. J., & Yan, X. (2022). A highway vehicle routing dataset during the 2019 Kincade Fire evacuation. Scientific Data, 9(1), 1-7. (^: equal contribution)

[20] Zhao, X., Xu, Y., Lovreglio, R., Kuligowski, E., Nilsson, D., Cova, T., Wu, A., & Yan, X. Estimating wildfire evacuation decision and departure timing using large-scale GPS data. Transportation Research Part D: Transport and Environment, 107, 103277.

[19] Xu, Y., Yan, X., Sisiopiku, V. P., Merlin, L. A., Xing, F., & Zhao, X. (2022).  Micromobility trip origin and destination inference using General Bikeshare Feed Specification (GBFS) data. Transportation Research Record, 03611981221092005. 

[18] Zhang, X., & Zhao, X. (2022). Machine learning approach for spatial modeling of ridesourcing demand. Journal of Transport Geography, 100, 103310.

[17] Kuligowski, E., Zhao, X., Lovreglio, R., Xu, N., Yang, K., Westbury, A., Nilsson, D., Brown, N. (2022). Modeling evacuation decisions in the 2019 Kincade fire in California. Safety Science, 146, 105541.

[16] Wang, X., Yan, X., Zhao, X., & Cao, Z. (2022). Identifying latent shared mobility preference segments in low-income communities: ride-hailing, fixed-route bus, and mobility-on-demand transit. Travel Behaviour and Society, 26, 134-142.

[15] Zhao, X., Lovreglio, R., Kuligowski, E., & Nilsson, D. (2021). Using Artificial Intelligence for safe and effective wildfire evacuations. Fire Technology, 57, 483-485.

[14] Yan, X., Yang, W., Zhang, X., Xu, Y., Bejleri, I., & Zhao, X. (2021). A spatiotemporal analysis of e-scooters’ relationships with transit and station-based bikeshare. Transportation Research Part D: Transport and Environment, 100, 103310.

[13] Liu, X., Van Hentenryck, P., Zhao, X. (2021). Optimization models for estimating transit network origin-destination flows with big transit data. Journal of Big Data Analytics in Transportation, 3(3), 247-262.

[12] Yan, X., Zhao, X., Han, Y., Van Hentenryck, P., & Dillahunt, T. (2021). Mobility-on-demand versus fixed-route transit systems: An evaluation of traveler preferences in low-income communities. Transportation Research Part A: Policy and Practice, 148, 481-495.

[11] Merlin, L. A.*, Yan, X., Xu, Y., & Zhao, X. (2021). A segment-level model of shared, electric scooter origins and destinations. Transportation Research Part D: Transport and Environment, 92, 102709.

[10] Xu, Y., Yan, X., Liu, X., & Zhao, X. (2021). Identifying key factors associated with ride-splitting adoption rate and modeling their nonlinear relationships. Transportation Research Part A: Policy and Practice, 144, 170-188.

[9] Zhao, X., Yan, X., Yu, A., & Van Hentenryck, P. (2020). Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models. Travel Behaviour and Society, 20, 22-35. (Outstanding Paper Award 2020)

[8] Yan, X., Liu, X., & Zhao, X. (2020). Using machine learning for direct demand modeling of ridesourcing services in Chicago. Journal of Transport Geography, 83, 102661.

[7] Zhao, X., Lovreglio, R., & Nilsson, D. (2020). Modelling and interpreting pre-evacuation decision-making using machine learning. Automation in Construction, 113, 103140.

[6] Yan, X., Levine, J., & Zhao, X. (2019). Integrating ridesourcing services with public transit: An evaluation of traveler responses combining revealed and stated preference data. Transportation Research Part C: Emerging Technologies, 105, 683-696.

[5] Zhao, X., Miers, I., Green, M., & Mitrani-Reiser, J. (2019). Modeling the cybersecurity of hospitals in natural and man-made hazards. Sustainable and Resilient Infrastructure, 4(1), 36-49.

[4] Chodur, G., Zhao, X., Biehl, E., Mitrani-Reiser, J., & Neff, R.* (2018). Assessing food system vulnerabilities: A fault tree modeling approach. BMC Public Health, 18(1), 817.

[3] Links, J. M., Schwartz, B. S., Lin, S., Kanarek, N., Mitrani-Reiser, J., Sell, T. K., Boddie, C. R., Ward, D., Slemp, C., Burhans, R., Gill, K., Igusa, T., Zhao, X., Aguirre, B., Trainor, J., Nigg, J., Inglesby, T., Carbone, E., & Kendra, J. M. (2017). COPEWELL: A conceptual framework and system dynamics model for predicting community functioning and resilience after disasters. Disaster Medicine and Public Health Preparedness, 12(1), 127-137.

[2] Lu, J., Dong, X., Zhao, X., Wu, X., & Shu, G. (2017). Form-finding analysis of a new type of cable-strut tensile structures generated by semi-regular tensegrity. Advances in Structural Engineering, 20(5), 772–783.

[1] Lu, J., Wu, X., Zhao, X., & Shu, G. (2015). Form finding analysis of cable-strut tensile dome based on tensegrity torus. Engineering Mechanics, 32(6), 66–71. (in Chinese)

Theses and Dissertations

[6] Xu, Y. (2023). AI-enabled travel demand forecasting for shared mobility. Ph.D. Dissertation, University of Florida.

[5] Xu, N. (2021). Assessing household evacuation decision-making in Hurricane Irma. Master’s Thesis, University of Florida.

[4] Cao, Z. (2021). Exploring Mobility-On-Demand service preference heterogeneity in low-income communities. Master’s Thesis, University of Florida.

[3] Noei, S. (2020). Longitudinal control in traffic microsimulation. Ph.D. Dissertation, University of Florida.

[2] Zhao, X. (2017). Multi-scale community resilience modeling for natural and manmade hazards. Ph.D. Dissertation, Johns Hopkins University.

[1] Zhao, X. (2017). Modeling transportation networks and urban traffic dynamics: A Markovian framework. Master’s Thesis, Johns Hopkins University.

Patents

[2] Lu, J., Zhao, X., Shu, G., Qiang, H., Cao, X., & Wang, Z. (2015). Full-tension cable-strut roof structural system and construction method thereof. China Patent No. CN103334529B.

[1] Lu, J., Zhao, X., Shu, G., Qiang, H., Cao, X., & Wang, Z. (2013). Full-tension cable bar roof structure system. China Patent No. CN203307981U.