Xiang 'Jacob' Yan
With increased frequency and intensity due to climate change, wildfires have become a growing global concern. This study proposes a new methodology to analyze human behavior during wildfires by leveraging a large-scale GPS dataset. This methodology includes a home-location inference algorithm and an evacuation-behavior inference algorithm, to systematically identify different groups of wildfire evacuees (i.e., self-evacuee, shadow evacuee, evacuee under warning, and ordered evacuee).
Key words: wildfire, GPS data, evacuation, human behavior
Zhao, X., Xu, Y., Lovreglio, R., Kuligowski, E., Nilsson, D., Cova, T., Wu, A., Yan, X., Cao, Z. . [Download Preprint].
The increasing popularity of machine learning in transportation research raises questions regarding its advantages and disadvantages compared to conventional logit-family models used for travel behavioral analysis. We provide a comprehensive comparison between logit models and machine learning by examining the key differences in model development, evaluation, and behavioral interpretation in mode-choice modeling. We find that There appears to be a tradeoff between predictive accuracy and behavioral soundness when choosing between machine learning and logit models in mode-choice modeling.
Key words: machine learning, logit model, travel mode choice
Zhao, X., Yan, X., Yu, A., Van Hentenryck, P. (2020). . [Download Preprint]. Travel Behavior and Society, 20, 22-35. https://doi.org/10.1016/j.tbs.2020.02.003 (Won the 2020 Outstanding Paper Award)
This paper calibrates a joint model of travel mode and parking location choice to evaluate the effectiveness of parking-related policies. We found that 1) travelers are very sensitive to changes in egress time, even more so than parking price; 2) Travelers respond to parking policies primarily by shifting parking locations rather than switching travel mode.
Key words: parking policy, price elasticity, join choice mode
Yan, X. (2020). . [Download Preprint]. Transportation Research Part D: Transport and Environment, 80, 102255. https://doi.org/10.1016/j.trd.2020.102255
In recently years, transit agencies have started to consider integrating ridesourcing services (i.e. on-demand, app-driven ridesharing services) with public transit. In this project, we evaluated traveler preferences for this type of integrated transit system. We find that when used to provide convenient last-mile connections, ridesourcing could provide a significant boost to transit ridership.
Key words: Ridesourcing, public transit, travel behavior, mode choice
Yan, X., Levine, J., Zhao, X. (2019). . [Download Preprint]. Transportation Research Part C: Emerging Technologies, 105, 683-696. https://doi.org/10.1016/j.trc.2018.07.029