Projects by Topic

Florida Mobility Hubs

Mobility hubs are physical locations that allow travelers to seamlessly switch between various modes of transportation such as public transit, ridehailing, and micromobility. These hubs, by enhancing connectivity and accessibility, contribute to an improved quality of travel and offer various socioeconomic benefits. Also, as integral components of transport networks, mobility hubs play a crucial role in integrating new mobility technologies.

E-scooter, public transit, and bikesharing

To address the policy question of how e-scooters interact with existing public mobility options, we conducted a spatiotemporal analysis of e-scooters’ relationships with public transit and station-based bikeshare in Washington DC. Results suggested that the service areas of the three modes largely overlap, and most e-scooter trips could have been made by transit or bikeshare. In addition, we found that e-scooters enhance mobility services for some underserved neighborhoods. Before COVID-19, about 10% of all e-scooter trips were taken to connect with the Metrorail system

Key words: E-scooter, public transit, bikeshare

Yan, X., Yang, W., Zhang, X., Xu, Y., Bejleri, I., Zhao, X. (2021). A spatiotemporal analysis of e-scooters’ relationships with transit and station-based bikesharing. [Download Preprint]. Transportation Research Part D: Transport and Environment. 12, 103088. https://doi.org/10.1016/j.trd.2021.103088

Mobility-on-demand vs fixed-route transit systems in low-income communities

Some transit observers envision future public transit to be integrated systems with fixed-route services running along major corridors and ridesourcing servicing lower-density areas. This paper evaluates traveler preferences for a proposed integrated transit system versus the existing fixed-route system, with a particular focus on disadvantaged travelers. Results from low-income communties in Detroit and Ypsilanti suggest that a majority of survey respondents preferred a MOD transit system over a fixed-route one. However, some women may have safety concerns, and low technology self-efficacy can be a more serious barrier for many people to adopt MOD transit.

Key words: mobility on demand, public transit, transport equity

Yan, X., Zhao, X., 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. [Download Paper]. Transportation Research Part A: Policy and Practice, 148, 481-495. https://doi.org/10.1016/j.tra.2021.03.019

Modeling e-scooter use at the street segment level

Electric scooters (or e-scooters) have quickly proliferated in cities worldwide, presenting a host of regulatory challenges. We analyze trip origins and destinations for shared e-scooter use at the street-segment level. Results show that street segments near tourist sites, hotels, and transit stops attract the most scooter-trip destinations. In constrast, the supply of available e-scooters is the dominant force shaping scooter-trip origins

Key words: E-scooter, street segment, public transit, travel demand

Merlin, L., Yan, X., Zhao, X. (2021). A segment-level model of shared scooter origins and destinations. [Download Preprint]. Transportation Research Part D: Transport and Environment, 92, 102709. https://doi.org/10.1016/j.trd.2021.102709

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

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.

Traveler preferences for an integrated transit system that combines fixed-route and ridesourcing services

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). 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