Micromobility as a last-mile enhancement to public transit

Micromobility as a last-mile enhancement to public transit

Our team is developing a decision-support system for planning, designing, operating, and managing micromobility in U.S. cities. This tool is a web-based platform that 1) can crowdsource information from residents on traffic incidents such as crashes and near-misses; and 2) can help cities manage e-scooter parking and monitor equity policy compliance.

For any questions regarding this tool, please contact Jacob Yan (xiangyan@ufl.edu) and/or Xilei Zhao (xilei.zhao@essie.ufl.edu).


A conceptual framework to understand the relationship between e-scooters and public transit


[1] Xu, Y., Yan, X., Sisiopiku, V. P., Merlin, L. A., Xing, F., & Zhao, X. (2021). Micromobility trip origin and destination inference using General Bikeshare Feed Specification (GBFS) data. Proceedings of Transportation Research Board 100th Annual Meeting.

[2] Merlin, L. A., Yan, X., Xu, Y., & Zhao, X. (2021). A segment-level model of shared, electric scooter origins and destinationsTransportation Research Part D: Transport and Environment, 92, 102709.

[3] Yan, X., Yang, W., Zhang, X., Xu, Y., Bejleri, I., Zhao, X. (2021). Do e-scooters fill mobility gaps and promote equity before and during COVID-19? A spatiotemporal analysis using open big data. (Under review)


Spatial distributions of trip origins and destinations in Washington, D.C. (Density unit: trips per day per km^2.)

Morning Peak (Left: trip origin; Right: trip destination.)


Afternoon Peak (Left: trip origin; Right: trip destination.)