This project provided programming and analysis support for the creation of WMATA’s Bus Ridership Datamart and Bus Priority Datamart. WMATA has had unsupported access to fine-grained automatic vehicle location (AVL) data for years and needed help loading, cleaning, and aggregating this data so their planners could use it in bus priority decision-making. They also needed help cleaning raw automatic passenger count (APC) data.

Together with our partners, Foursquare ITP wrote a number of algorithms to match the fine-grained AVL data to the road network, parse it into travel time decomposition buckets, make vehicle heading data useful, and create fields for easy aggregation. We also helped create useful dashboards for staff to explore and download APC data. As a result, WMATA planners now have access to a rich new data source to conduct before/after analyses for transit priority treatments, select new locations for bus priority treatments, and gain insight on ridership from their APC data.

As a result, WMATA planners now have access to a rich new data source to conduct before/after analyses for transit priority treatments, select new locations for bus priority treatments, and gain insight on ridership from their APC data.

Solutions and Outcomes
  • Developed usable, fine-grained AVL data aggregated spatially to the road network and temporally into travel time decomposition buckets.
  • Created sample SQL queries for staff to run to pull, aggregate and analyze AVL data.
  • Established dashboards for staff to visualize fine-grained AVL and APC data at various levels of aggregation.