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City of Austin DIVE Data Learning Cohorts Portal
Austin Transportation Data Science Essentials

On-Time Transit Analytics

Uses APC/AVL to analyze late arrivals on Bus Line 7 and recommend interventions.

The On-Time Transit Analytics project examined Automatic Passenger Counter (APC) and Automatic Vehicle Location (AVL) data to identify trends in late arrivals for a high-volume bus route. The team blended multiple years of historical datasets, standardized timestamps, and joined vehicle telemetry to understand delay hotspots.

Key Findings

  • Late arrivals clustered during evening peak hours, particularly in the downtown corridor.
  • Weather events and special downtown activities correlated with service disruptions.
  • Stop-level dwell times provided early warning signals for cascading delays.

Impact

  • Recommendations informed dynamic dispatch scheduling and additional operator support for peak times.
  • Austin Transportation is piloting real-time alerts for route supervisors informed by the team’s dashboard.
  • The methodology serves as a blueprint for future reliability analyses across Capital Metro routes.

Next Steps

  1. Integrate predictive analytics to anticipate late arrivals based on live telemetry.
  2. Extend the dashboard to cover accessibility metrics, including ramp deployment frequency.
  3. Formalize data-sharing agreements to streamline future cohort projects involving APC and AVL datasets.