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

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Yousefzadeh Barri E., Farber S., Jahanshahi H., Beyazıt İnce E.

Journal of Transport Geography, vol.105, 2022 (SSCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 105
  • Publication Date: 2022
  • Doi Number: 10.1016/j.jtrangeo.2022.103482
  • Journal Name: Journal of Transport Geography
  • Journal Indexes: Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, Aquatic Science & Fisheries Abstracts (ASFA), Business Source Elite, Business Source Premier, Hospitality & Tourism Complete, Hospitality & Tourism Index
  • Keywords: Machine learning, Statistical models, Transit equity, Travel behaviour, Travel mode choice
  • Istanbul Technical University Affiliated: Yes


© 2022 Elsevier LtdBuilding 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 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.