{"id":11,"date":"2015-11-03T15:53:38","date_gmt":"2015-11-03T20:53:38","guid":{"rendered":"https:\/\/test.eng.ufl.edu\/faculty-site\/?page_id=11"},"modified":"2026-01-09T16:34:26","modified_gmt":"2026-01-09T16:34:26","slug":"publications","status":"publish","type":"page","link":"https:\/\/faculty.eng.ufl.edu\/sermos-lab\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"
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Peer-Reviewed Journal Publications<\/h3>\n

Note: Lab members (including the director and students) are in bold font.<\/strong><\/em><\/p>\n

Published\/In Press<\/h5>\n

[39] Zhang, X.<\/strong>, Zhao, X.<\/u><\/strong>, Xu, Y.<\/strong>, Nilsson, D., & Lovreglio, R. (2024). Situational-aware multi-graph convolutional recurrent network (SA-MGCRN) for travel demand forecasting during wildfires<\/a>. Transportation Research Part A: Policy and Practice<\/em>, 190<\/em>, 104242, 1-21.<\/p>\n

[38] Jiang, S.<\/strong>, Sun, Y.<\/strong>, Wong, W., Xu, Y.<\/strong>, & Zhao, X.<\/u><\/strong> (2024). Real-time urban traffic monitoring using transit buses as probes<\/a>. Transportation Research Record<\/em>.<\/p>\n

[37] Xu, Y.<\/strong>, Ke, Q., Zhang, X.<\/strong>, & Zhao, X.<\/u><\/strong> (2024).\u00a0ICN: Interactive Convolutional Network for Forecasting Travel Demand of Shared Micromobility<\/a>. GeoInformatica<\/em>.\u00a0<\/p>\n

[36] Sun, Y.<\/strong>, Forrister, A.<\/strong>, Kuligowski, E. Lovreglio, R., Cova, T. J., & Zhao, X.<\/u><\/strong> (2024). Social vulnerabilities and wildfire evacuations: A case study of the 2019 Kincade Fire<\/a>. 1-24. Safety Science<\/em>, 176<\/em>, 106557, 1-12.<\/p>\n

[35] Zhang, X.<\/strong>, Ke, Q., & Zhao, X.<\/u><\/strong> (2024). Travel Demand Forecasting: A Fair AI Approach<\/a>. IEEE Transactions on Intelligent Transportation Systems<\/em>, 1-17.<\/p>\n

[34] Cova, T. J., Sun, Y.<\/strong>, Zhao, X.<\/u><\/strong>, Liu, Y., Kuligowski, E. D., Janfeshanaraghi, N., & Lovreglio, R. (2024). Destination unknown: Examining wildfire evacuee trips using GPS data<\/a>. Journal of Transport Geography<\/em>, 117<\/em>, 103863, 1-13.<\/p>\n

[33] Wang, C.^, Liu, Y.<\/strong>^, Zhang, X.<\/strong>, Li, X., Paramygin, V., Sheng, P., Zhao, X.<\/u><\/strong>, & Xu, S. (2024). Scalable and Rapid Building Damage Detection after Hurricane Ian using Causal Bayesian Networks and InSAR Imagery<\/a>. International Journal of Disaster Risk Reduction<\/em>, 104<\/em>, 104371, 1-17. (^: Equal contribution)<\/p>\n

[32] Zhang, X.<\/strong>, Zhao, X.<\/u><\/strong>, Baldwin, D., McBride, S., Bellizzi, J., Cochran, E., Luco, N., Wood, M., & Cova, T. J. (2024). Modeling protective action decision-making in earthquakes by using explainable machine learning and video data<\/a>. Scientific Reports<\/em>, 14<\/em>(1), 5480, 1-13.\u00a0<\/p>\n

[31] Sun, Y.<\/strong>, Huang, S. K., & Zhao, X.<\/u><\/strong> (2024). Predicting hurricane evacuation decisions with interpretable machine learning methods<\/a>. International Journal of Disaster Risk Science<\/em>, 15<\/em>, 134-148.\u00a0<\/p>\n

[30] Arshad, H.<\/strong>, Emblemsv\u00e5g, J., & Zhao, X.<\/strong><\/span> (2024). A data-driven, scenario-based human evacuation model for passenger ships addressing hybrid uncertainty<\/a>. International Journal of Disaster Risk Reduction<\/em>.<\/p>\n

[29] Zhang, X.<\/strong>, Zhou, Z., Xu, Y.<\/strong>, & Zhao, X.<\/strong><\/span> (2024).\u00a0Analyzing spatial heterogeneity of ridesourcing demand determinants using explainable machine learning<\/a>. Journal of Transport Geography<\/em>.\u00a0<\/p>\n

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

[27] Su, L.<\/strong>, Yan, X., & Zhao, X.<\/strong><\/span> (2024). Spatial equity of micromobility systems: A comparison of shared e-scooters and docked bikeshare in Washington DC<\/a>. Transport Policy<\/em>, 145<\/em>, 25-36.<\/p>\n

[26] Yang, W., Jafarzadehfadaki, M., Yan, X., Zhao, X.<\/strong><\/span>, Jin, X., Frolich, D., & Sisiopiku, V. P. (2023). Shared e-scooters: User characteristics and usage patterns across four U.S. cities<\/a>. Transportation Research Record<\/em>.\u00a0<\/p>\n

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

[24] Xu, Y.<\/strong>, Zhao, X.<\/strong><\/span>, Zhang, X.<\/strong>, & Paliwal, M.<\/strong> (2023).\u00a0Real-time forecasting of dockless scooter-sharing demand: A spatio-temporal multi-graph transformer approach<\/a>. IEEE Transactions on Intelligent Transportation Systems<\/em>.<\/p>\n

[23] Xu, N.<\/strong>, Lovreglio, R., Kuligowski, E., Cova, T., Nilsson, D., & Zhao, X.<\/strong><\/span> (2023). Predicting and assessing wildfire evacuation decision-making using machine learning: Findings from the 2019 Kincade Fire<\/a>. Fire Technology<\/em>.\u00a0<\/p>\n

[22] Wu, A.<\/strong>, Yan, X., Kuligowski, E., Lovreglio, R., Nilsson, D., Cova, T. J., Xu, Y<\/strong>., & Zhao, X.<\/strong> <\/u>(2022). Wildfire evacuation decision modeling using GPS data<\/a>. International Journal of Disaster Risk Reduction<\/em>, 103373.<\/p>\n

[21] Xu, Y.<\/strong>^, Zhao, X.<\/strong>^<\/u>, Lovreglio, R., Kuligowski, E., Nilsson, D., Cova, T. J., & Yan, X. (2022). A highway vehicle routing dataset during the 2019 Kincade Fire evacuation<\/a>. Scientific Data<\/em>,\u00a09<\/em>(1), 1-7. (^: equal contribution)<\/p>\n

[20] Zhao, X.<\/strong><\/u>, Xu, Y.<\/strong>, Lovreglio, R., Kuligowski, E., Nilsson, D., Cova, T., Wu, A.<\/strong>, & Yan, X.\u00a0Estimating wildfire evacuation decision and departure timing using large-scale GPS data<\/a>. Transportation Research Part D: Transport and Environment<\/em>, 107<\/em>, 103277.<\/p>\n

[19] Xu, Y.<\/strong>, Yan, X., Sisiopiku, V. P., Merlin, L. A., Xing, F., & Zhao, X.<\/strong><\/u> (2022).\u00a0 Micromobility trip origin and destination inference using General Bikeshare Feed Specification (GBFS) data<\/a>. Transportation Research Record, <\/em>03611981221092005.\u00a0<\/p>\n

[18] Zhang, X.<\/strong>, & Zhao, X.<\/strong><\/u>\u00a0(2022). Machine learning approach for spatial modeling of ridesourcing demand<\/a>.\u00a0Journal of Transport Geography,\u00a0100<\/em>, 103310.<\/p>\n

[17] Kuligowski, E., Zhao, X.<\/strong><\/u>, Lovreglio, R., Xu, N.<\/strong>, Yang, K.<\/strong>, Westbury, A., Nilsson, D., Brown, N. (2022). Modeling evacuation decisions in the 2019 Kincade fire in California<\/a>. Safety Science, 146, <\/em>105541.<\/em><\/p>\n

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

[15] Zhao, X.<\/strong><\/u>, Lovreglio, R., Kuligowski, E., & Nilsson, D. (2021). Using Artificial Intelligence for safe and effective wildfire evacuations<\/a>. Fire Technology<\/em>, 57, 483-485.<\/p>\n

[14] Yan, X., Yang, W., Zhang, X.<\/strong>, Xu, Y.<\/strong>, Bejleri, I., & Zhao, X.<\/strong><\/u>\u00a0(2021). A spatiotemporal analysis of e-scooters’ relationships with transit and station-based bikeshare<\/a>. Transportation Research Part D: Transport and Environment, 100, <\/em>103310.<\/em><\/p>\n

[13] Liu, X.<\/strong>, Van Hentenryck, P., Zhao, X.<\/strong><\/u> (2021). Optimization models for estimating transit network origin-destination flows with big transit data<\/a>. Journal of Big Data Analytics in Transportation, 3(3), <\/em>247-262.<\/em><\/p>\n

[12] Yan, X., Zhao, X.<\/strong><\/u>, 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<\/a>. Transportation Research Part A: Policy and Practice<\/em>, 148<\/em>, 481-495.<\/em><\/p>\n

[11] Merlin, L. A.*, Yan, X., Xu, Y.<\/strong>, & Zhao, X.<\/strong><\/span> (2021). A segment-level model of shared, electric scooter origins and destinations<\/a>. Transportation Research Part D: Transport and Environment<\/em>, 92<\/em>, 102709.<\/p>\n

[10] Xu, Y.<\/strong>, Yan, X., Liu, X.<\/strong>, & Zhao, X.<\/strong><\/span>\u00a0(2021). Identifying key factors associated with ride-splitting adoption rate and modeling their nonlinear relationships<\/a>. Transportation Research Part A: Policy and Practice<\/em>, 144<\/em>, 170-188.<\/p>\n

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

[8] Yan, X., Liu, X.<\/strong>, &\u00a0Zhao, X.<\/strong><\/span>\u00a0(2020). Using machine learning for direct demand modeling of ridesourcing services in Chicago<\/a>. Journal of Transport Geography<\/em>, 83<\/em>, 102661.<\/em><\/p>\n

[7] Zhao, X.<\/strong><\/span>, Lovreglio, R., & Nilsson, D. (2020). Modelling and interpreting pre-evacuation decision-making using machine learning<\/a>. Automation in Construction<\/em>, 113<\/em>, 103140.<\/em><\/p>\n

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

[5] Zhao, X.<\/strong><\/u>, Miers, I., Green, M., & Mitrani-Reiser, J. (2019). Modeling the cybersecurity of hospitals in natural and man-made hazards<\/a>. Sustainable and Resilient Infrastructure, 4(1)<\/em>, 36-49.<\/em><\/p>\n

[4] Chodur, G., Zhao, X.<\/u><\/strong>, Biehl, E., Mitrani-Reiser, J., & Neff, R.* (2018). Assessing food system vulnerabilities: A fault tree modeling approach<\/a>. BMC Public Health<\/em>, 18(1)<\/em>, 817.<\/p>\n

[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.<\/u><\/strong>, 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<\/a>. Disaster Medicine and Public Health Preparedness<\/em>, 12(1)<\/em>, 127-137.<\/p>\n

[2] Lu, J., Dong, X., Zhao, X.<\/u><\/strong>, Wu, X., & Shu, G. (2017). Form-finding analysis of a new type of cable-strut tensile structures generated by semi-regular tensegrity<\/a>. Advances in Structural Engineering<\/em>, 20(5)<\/em>, 772\u2013783.<\/p>\n

[1] Lu, J., Wu, X., Zhao, X.<\/u><\/strong>, & Shu, G. (2015). Form finding analysis of cable-strut tensile dome based on tensegrity torus<\/a>. Engineering Mechanics<\/em>, 32(6)<\/em>, 66\u201371. (in Chinese)<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n

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Theses and Dissertations<\/h3>\n

[6] Xu, Y.<\/strong> (2023). AI-enabled travel demand forecasting for shared mobility. Ph.D. Dissertation, University of Florida.<\/p>\n

[5] Xu, N.<\/strong> (2021). Assessing household evacuation decision-making in Hurricane Irma. Master’s Thesis, University of Florida.<\/p>\n

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

[3] Noei, S.<\/strong> (2020). Longitudinal control in traffic microsimulation. Ph.D. Dissertation, University of Florida.<\/p>\n

[2] Zhao, X.<\/u><\/strong>\u00a0(2017). Multi-scale community resilience modeling for natural and manmade hazards<\/a>. Ph.D. Dissertation, Johns Hopkins University.<\/p>\n

[1] Zhao, X.<\/u><\/strong>\u00a0(2017). Modeling transportation networks and urban traffic dynamics: A Markovian framework<\/a>. Master’s Thesis, Johns Hopkins University.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n

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\u00a0<\/h3>\n

Books<\/h3>\n

[2] Sun, Y.<\/strong>, Zhao, X.<\/u><\/strong>, Lovreglio, R., & Kuligowski, E. D. (2024). AI for Large-Scale Evacuation Modeling: Promises and Challenges<\/a>. In M. Z. Naser (Eds), Interpretable Machine Learning for the Analysis, Design, Assessment, and Informed Decision Making for Civil Infrastructure<\/em>, 185-204. Elsevier.<\/p>\n

[1] Lovreglio, R., Paes, D., Feng, Z., & Zhao, X.<\/u><\/strong> (2024). Digital Technologies for Fire Evacuations<\/a>. In X. Huang, & W. C. Tam (Eds), Intelligent Building Fire Safety and Smart Firefighting<\/em>, 439-454. Cham: Springer Nature Switzerland.<\/p>\n

 <\/p>\n<\/div>\n<\/div>\n<\/div>\n

Patents<\/h3>\n

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

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

 <\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n","protected":false},"excerpt":{"rendered":"

Peer-Reviewed Journal Publications Note: Lab members (including the director and students) are in bold font. Published\/In Press [39] Zhang, X., Zhao, X., Xu, Y., Nilsson, D., & Lovreglio, R. (2024). Situational-aware multi-graph convolutional recurrent network (SA-MGCRN) for travel demand forecasting during wildfires. Transportation Research Part A: Policy and Practice, 190, 104242, 1-21. [38] Jiang, S., […]<\/p>\n","protected":false},"author":380,"featured_media":0,"parent":0,"menu_order":5,"comment_status":"closed","ping_status":"closed","template":"page-templates\/page-sidebar-none.php","meta":{"_acf_changed":false,"inline_featured_image":false,"featured_post":"","footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-11","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/sermos-lab\/wp-json\/wp\/v2\/pages\/11","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/faculty.eng.ufl.edu\/sermos-lab\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/faculty.eng.ufl.edu\/sermos-lab\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/sermos-lab\/wp-json\/wp\/v2\/users\/380"}],"replies":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/sermos-lab\/wp-json\/wp\/v2\/comments?post=11"}],"version-history":[{"count":20,"href":"https:\/\/faculty.eng.ufl.edu\/sermos-lab\/wp-json\/wp\/v2\/pages\/11\/revisions"}],"predecessor-version":[{"id":1675,"href":"https:\/\/faculty.eng.ufl.edu\/sermos-lab\/wp-json\/wp\/v2\/pages\/11\/revisions\/1675"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/sermos-lab\/wp-json\/wp\/v2\/media?parent=11"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}