Xilei Zhao
| Xilei Zhao | |
|---|---|
| File:Xilei Zhao.tifXilei Zhao.tif Xilei Zhao | |
| Born | 1990 China |
| 🎓 Alma mater | Johns Hopkins University |
| 💼 Occupation | |
| 🏅 Awards | NASEM Research Fellowship (Early Career) |
Xilei Zhao (赵曦蕾, born 1990) is an assistant professor in Transportation Engineering at the University of Florida where she is the director of the Smart, Equitable, Resilient Mobility Systems (SERMOS) Lab.[1] Her research focuses on machine learning applications for transportation and resilience.
Education[edit]
She holds a Master's degree in Applied Mathematics and Statistics and a Ph.D. degree in Civil Engineering from Johns Hopkins University.[1]
Research[edit]
Zhao has been developing and applying data and computational science methods to solve challenging problems in transportation and community preparedness and response, focusing on many disasters, such as building fires, hurricanes and wildfires.[2][3][4][5] She has made contributions to the investigation of several disasters, such as the investigation of the Kinkade Fire using questionnaire studies, GPS data and machine learning algorithms.[6][7] Further, her research investigated and proposed strategies to promote e-scooters to enhance public transit in Washington, DC and to make self-driving cars accessible to people with disabilities.[8]
Awards[edit]
- Travel Behaviour and Society Outstanding Paper Award 2020[9]
- Early-Career Research Fellowship of the National Academies of Sciences, Engineering, and Medicine 's Gulf Research Program[10]
Selected works[edit]
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.
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.
Zhao, X., Lovreglio, R., & Nilsson, D. (2020). Modelling and interpreting pre-evacuation decision-making using machine learning. Automation in Construction, 113, 103140.
Zhao, X., Xu, Y., Lovreglio, R., Kuligowski, E., Nilsson, D., Cova, T. J., ... & Yan, X. (2022). Estimating wildfire evacuation decision and departure timing using large-scale GPS data. Transportation research part D: transport and environment, 107, 103277.
References[edit]
- ↑ 1.0 1.1 "Xilei Zhao » People – Engineering School of Sustainable Infrastructure & Environment". www.essie.ufl.edu. Retrieved 2024-01-19.
- ↑ "UF experts available to discuss approaching tropical storm". news.ufl.edu. Retrieved 2024-01-19.
- ↑ Nyce, Caroline Mimbs (2022-09-30). "Escaping Hurricane Ian". The Atlantic. Retrieved 2024-01-19.
- ↑ "How Location Intelligence Can Help Protect Lives During Disasters". EHS Daily Advisor. 2022-02-09. Retrieved 2024-01-19.
- ↑ Zhao, Xilei; Lovreglio, Ruggiero; Nilsson, Daniel (2020-05-01). "Modelling and interpreting pre-evacuation decision-making using machine learning". Automation in Construction. 113: 103140. doi:10.1016/j.autcon.2020.103140. hdl:10179/17315. ISSN 0926-5805. Unknown parameter
|s2cid=ignored (help) - ↑ "Researchers delve into Kincade fire evacuation trends". www.pressdemocrat.com. Retrieved 2024-01-19.
- ↑ Zhao, Xilei; Xu, Yiming; Lovreglio, Ruggiero; Kuligowski, Erica; Nilsson, Daniel; Cova, Thomas J.; Wu, Alex; Yan, Xiang (2022-06-01). "Estimating wildfire evacuation decision and departure timing using large-scale GPS data". Transportation Research Part D: Transport and Environment. 107: 103277. arXiv:2109.07745. doi:10.1016/j.trd.2022.103277. ISSN 1361-9209.
- ↑ "Team builds self-driving tech for people with cognitive impairments". The University of Kansas. 2021-01-28. Retrieved 2024-01-19.
- ↑ "TBS Outstanding Paper Award 2020 - Awards - Travel Behaviour and Society - Journal - Elsevier". www.journals.elsevier.com. Retrieved 2024-01-19.
- ↑ "Fellows: Early-Career Research Fellowship". www.nationalacademies.org. Retrieved 2024-01-19.
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