Shared Autonomous Vehicles Mobility Data Initiative 

The University of Alabama and the Alabama Transportation Institute have developed an open data initiative to allow planners, engineers, and other researchers to explore potential shared autonomous vehicle (SAV) deployments. The data set is concurrent with the CR2C2 focus as it facilitates SAV deployment studies in small and mid-sized metropolitan areas (SMMAs)—those with populations under 500,000.  These SMMAs are often under-resourced and underrepresented in transportation technology deployment. Unlike larger metropolitan regions, SMMAs typically lack the capacity to develop detailed agent-based models (ABMs) needed to plan for future mobility technologies and services. The ATI Mobility Data Initiative (MDI) addresses that gap by developing synthetic, high-resolution population and travel demand datasets for more than 400 small and mid-sized urban areas across the United States. These datasets are designed to align with aggregate demographic and travel characteristics of local populations, enabling transportation practitioners and researchers to more easily adopt agent-based modeling frameworks. A key component of the project is a case study conducted in Tuscaloosa, Alabama, which demonstrates the application of the framework in a real-world planning context. Results from additional SMMAs are currently in progress and will be released in open-access formats, complete with documentation and metadata to support broad public use.

The primary objectives of the initiative are to:

○   Enable data-driven mobility planning in resource-constrained communities;

○   Promote the adoption of agent-based modeling (ABM) in small-area contexts;

○   Support policy analysis and scenario planning at disaggregated levels;

○   Facilitate integration of future mobility technologies (e.g., SAVs, urban air mobility).

By providing accessible, well-documented synthetic data, this effort advances equitable, scalable, and future-ready planning practices—empowering SMMAs to participate fully in the transformation of transportation systems.

 Link to Dataset: TBD

Related publications:

o   Zhang, Z., Liu, J., Pena-Bastidas, J., Jones, S., 2024. Charging Infrastructure Assessment for Shared Autonomous Electric Vehicles in 374 Small and Medium-Sized Urban Areas: An Agent-Based Simulation Approach. Transport Policy, 155, 58-78. https://doi.org/10.1016/j.tranpol.2024.06.017