Abstract

Bus Priority Interventions are small-scale changes that improve bus speed and reliability. These include changes to street geometry, bus stops, and traffic signals. Automated Vehicle Location-Automated Passenger Counting (AVL-APC) data can help transit agencies by providing insight into bus location, speed, and passenger volumes. This work proposes an end-to-end methodology for using AVL-APC data to create a concept design for bus priority interventions on a bus corridor. The proposed method is illustrated by analyzing a bus route in Portland, Oregon. This mixed-methods approach paired quantitative data analysis with site visits to identify what was causing delay on the route and suggest targeted interventions. Scenario analysis of historical trip data was employed to predict the impact of different interventions. Historical trips that fell into two different scenarios were compared: a delay scenario (where a specific delay-inducing event occurred, like a red light) and a non-delay scenario (where that event did not occur). This end-to-end methodology could be used by transit agencies and transportation planners to quickly assess different corridors and interventions, diagnose problems, and determine which projects would create the greatest customer and financial benefits. Employing this approach could help planners prioritize time and resources to ensure that the highest impact projects are pursued.