Understanding transit ridership in an equity context through a comparison of statistical and machine learning algorithms

TL;DR

This study compares statistical and machine learning models to predict transit use among low-income groups, highlighting ML's superior performance and interpretability tools for equitable transportation planning.

Contribution

It demonstrates the effectiveness of ML algorithms in predicting transit behavior of vulnerable populations and applies interpretability tools to address concerns about model transparency.

Findings

ML algorithms outperform traditional models in prediction accuracy

Interpretability tools help understand ML model decisions for policy insights

Transit investment impacts are evaluated using different modeling approaches

Abstract

Building an accurate model of travel behaviour based on individuals' characteristics and built environment attributes is of importance for policy-making and transportation planning. Recent experiments with big data and Machine Learning (ML) algorithms toward a better travel behaviour analysis have mainly overlooked socially disadvantaged groups. Accordingly, in this study, we explore the travel behaviour responses of low-income individuals to transit investments in the Greater Toronto and Hamilton Area, Canada, using statistical and ML models. We first investigate how the model choice affects the prediction of transit use by the low-income group. This step includes comparing the predictive performance of traditional and ML algorithms and then evaluating a transit investment policy by contrasting the predicted activities and the spatial distribution of transit trips generated by vulnerable households after improving accessibility. We also empirically investigate the proposed transit investment by each algorithm and compare it with the city of Brampton's future transportation plan. While, unsurprisingly, the ML algorithms outperform classical models, there are still doubts about using them due to interpretability concerns. Hence, we adopt recent local and global model-agnostic interpretation tools to interpret how the model arrives at its predictions. Our findings reveal the great potential of ML algorithms for enhanced travel behaviour predictions for low-income strata without considerably sacrificing interpretability.