Projects by Topic

Analyzing wildfire evacuation behavior with GPS data

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. Estimating wildfire evacuation decision and departure timing using large-scale GPS data. [Download Preprint].

Understanding micromobility equity with open big data

We develop an analytical framework to examine how dockless e-scooter and station-based bikesharing differ regarding a set of equity-related outcomes (i.e., availability, accessibility, usage, and idle time) across neighborhoods in different socioeconomic categories. An analysis of idle time is made possible by the availability of the GBFS data, a new source of open big data. The analysis of idle time can shed light on if improving spatial access to shared micromobility vechicles in low-income communities can effectively promote micromobility use in these areas.

Key words: E-scooter, bikeshare, micromobility equity

Su, L., Yan, X., Zhao, X. Micromobility equity: A comparison of shared e-scooters and station-based bikeshare in Washington DC. [Download Preprint].

Micromobility Analytics and Management Platform (Technology Transfer)

I worked with my colleage, Dr. Xilei Zhao, and a few Master of Computer Science students at UF (Ziying Wang, Manzhu Wang, Yepeng Liu) to develop a crowdsourcing platform [watch a 3-min demo that have two main functions]:

1) It allows residents to report micromobility-related safety incidents (i.e., crashes and near-misses).

2) It allows city transportation staff to conduct analytics regarding e-scooter parking and usage patterns.

Processing the GBFS data to facilitate transportation planning and decision-making

In Feburary 2019, I developed a Python program that automatically download the General Bikeshare Feed Specification (GBFS) data for multiple U.S. cities. In this study, we developed algorithms to infer micromobility trip origins and destinations based on the GBFS data.

Key words: micromobility, GBFS data, e-scooter

Xu, Y., Yan, X., Sisiopiku, V., Merlin, L., Xing, F., Zhao, X. Micromobility trip origin and destination inference using General Bikeshare Feed Specification (GBFS) data. [Download Preprint].

Modeling ridesplitting adoption rate with machine learning

In this paper, we developed a random forest model to examine which factors are key predictors of ridesplitting adoption rate (i.e., the proportion of ridesourcing trips with ridesharing authorization) and to explore their nonlinear relationships. The random forest model resulted in additional insights that were not obtainable from the commonly applied linear regression model.

Key words: random forest, machine learning, travel demand prediction

Xu, Y., Yan, X., Liu, X., Zhao, X. (2021). Identifying key factors associated with ride-splitting rate and modeling their nonlinear relationships. [Download Preprint]. Transportation Research Part A: Policy and Practice, 144, 170-188. https://doi.org/10.10416/j.tra.2020.12.005

Comparing machine learning and logit models in travel mode choice modeling

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). 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)

Predicting ridesourcing demand with machine learning

As ridesourcing continues to grow in popularity, being able to accurately predict the demand for it is essential for effective land-use and transportation planning and policymaking. In this research project, we show that using random forest, a machine learning model that can automatically capture nonlinear relationships and interactive effections, can significantly improve predictive accuracy compared to the traditional statistical model.

Key words: random forest, machine learning, travel demand prediction

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