Through a data use agreement, we got access to novel dataset (i.e., the 11 Spin post-ride survey dataset) that records thousands of transit-connecting shared e-scooter trips in Washington DC. This unique dataset allows us to address some critical research gaps in prior research on transit and micromobility integration. Specifically, we used the dataset to reveal the spatiotemporal patterns of transit-connecting shared e-scooter trips in Washington DC and to validate the buffer-zone approach commonly used in prior research to infer transit-connecting micromobility trips.

Key words: Extreme heat, transit, microclimate, heat exposure

• Related publication:

Yin, Z., Rybarczyk, G., Zheng, A., Su, L., Sun, B., & Yan, X. (2024). Shared micromobility as a first-and last-mile transit solution? Spatiotemporal insights from a novel dataset. Download Preprint Journal of Transport Geography, 114, 103778.

This study uses survey data collected from Washington DC and Los Angeles to evaluate if and to what extent shared e-scooters can enhance public transit and reduce driving. Mode choice models further suggest that males, non-Whites, and people without a college degree are more inclined to use shared e-scooters. While survey respondents intend to use shared escooters for short trips only, they are willing to use scoot-N-ride for medium-to-long trips.

Key words: micromobility, transit, new mobility, mode choice

• Related publication:

Yan, X., Zhao, X., Broaddus, A., Johnson, J., & Srinivasan, S. (2023). Evaluating shared e-scooters’ potential to enhance public transit and reduce driving. Download Preprint. Transportation research part D: transport and environment, 117, 103640.

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

• Related publication:

Zhao, X., Xu, Y., Lovreglio, R., Kuligowski, E., Nilsson, D., Cova, T., Wu, A., Yan, X., Cao, Z. Estimating wildfire evacuation decision and departure timing using large-scale GPS data. [Download Preprint]. Transportation research part D: transport and environment, 107, 103277.

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

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Zhao, X., Yan, X., Yu, A., Van Hentenryck, P. (2020). Prediction and behavioral analysis of travel mode choice: A comparison of logit models and machine learning. [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

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Yan, X. (2020). Evaluating household residential preferences for walkability and accessibility across three U.S. regions. [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

• Related publication:

Yan, X., Levine, J., Zhao, X. (2019). Integrating ridesourcing services with public transit: An evaluation of traveler responses combining revealed and stated preference data. [Download Preprint]. Transportation Research Part C: Emerging Technologies, 105, 683-696. https://doi.org/10.1016/j.trc.2018.07.029