Public Transit for Special Events: Ridership Prediction and Train Optimization

TL;DR

This paper introduces data-driven methods using AFC data to predict and manage transit ridership during special events, improving train scheduling and reducing congestion.

Contribution

It develops novel machine learning and regression models for ridership prediction and train optimization during special events, enhancing transit system performance.

Findings

Ridership at major stadiums is predictable at the aggregate level.

Unsupervised clustering identifies passenger boarding patterns.

Simulation results show improved wait times and demand matching.

Abstract

Many special events, including sport games and concerts, often cause surges in demand and congestion for transit systems. Therefore, it is important for transit providers to understand their impact on disruptions, delays, and fare revenues. This paper proposes a suite of data-driven techniques that exploit Automated Fare Collection (AFC) data for evaluating, anticipating, and managing the performance of transit systems during recurring congestion peaks due to special events. This includes an extensive analysis of ridership of the two major stadiums in downtown Atlanta using rail data from the Metropolitan Atlanta Rapid Transit Authority (MARTA). The paper first highlights the ridership predictability at the aggregate level for each station on both event and non-event days. It then presents an unsupervised machine-learning model to cluster passengers and identify which train they are boarding. The model makes it possible to evaluate system performance in terms of fundamental metrics such as the passenger load per train and the wait times of riders. The paper also presents linear regression and random forest models for predicting ridership that are used in combination with historical throughput analysis to forecast demand. Finally, simulations are performed that showcase the potential improvements to wait times and demand matching by leveraging proposed techniques to optimize train frequencies based on forecasted demand.