Forecasting MBTA Transit Dynamics: A Performance Benchmarking of Statistical and Machine Learning Models

TL;DR

This study benchmarks statistical and machine learning models for predicting MBTA subway usage and delays, revealing that temporal features outperform weather data in predictive accuracy and that overfitting to weather data can occur.

Contribution

It introduces a comprehensive performance benchmark of 10 statistical and machine learning models, including a novel self-exciting point process model for delay prediction.

Findings

Temporal features like day of week and season improve predictions

Weather data often worsens model performance due to overfitting

Self-exciting point process model offers a new approach for delay modeling

Abstract

The Massachusetts Bay Transportation Authority (MBTA) is the main public transit provider in Boston, operating multiple means of transport, including trains, subways, and buses. However, the system often faces delays and fluctuations in ridership volume, which negatively affect efficiency and passenger satisfaction. To further understand this phenomenon, this paper compares the performance of existing and unique methods to determine the best approach in predicting gated station entries in the subway system (a proxy for subway usage) and the number of delays in the overall MBTA system. To do so, this research considers factors that tend to affect public transportation, such as day of week, season, pressure, wind speed, average temperature, and precipitation. This paper evaluates the performance of 10 statistical and machine learning models on predicting next-day subway usage. On predicting delay count, the number of models is extended to 11 per day by introducing a self-exciting point process model, representing a unique application of a point-process framework for MBTA delay modeling. This research involves experimenting with the selective inclusion of features to determine feature importance, testing model accuracy via Root Mean Squared Error (RMSE). Remarkably, it is found that providing either day of week or season data has a more substantial benefit to predictive accuracy compared to weather data; in fact, providing weather data generally worsens performance, suggesting a tendency of models to overfit.