Project Summary
This project builds a new data-driven, passenger-focused tool for BART scheduling and performance analysis. It has two components. The first develops statistical models that use BART hourly trip data and real-time train arrival estimates to estimate how long passengers at each station and time of day are willing to wait for the next train, and to estimate the hidden demand lost when waits are long. Building on these estimates, the second component develops an efficient optimization framework based on the lead researcher’s new on-off queueing model for systems where riders are sensitive to delays. The framework optimizes train schedules, including the time between trains and how long trains stay at stations (dwell times), to maximize ridership while accounting for varying willingness to wait across stations and times. The proposed 12-month project will produce validated estimates of demand and passenger patience, a prototype optimization tool, scenario analysis results for BART operations, detailed schedule-change recommendations for BART, a practitioner-focused UC ITS report, and an academic journal article for a leading operations research journal.
