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

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 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

Can transit investments in low-income neighbourhoods increase transit use? Exploring the nexus of income, car-ownership, and transit accessibility in Toronto

Transportation equity advocates recommend improving public transit in low-income neighbourhoods to alleviate socio-spatial inequalities and increase quality of life. However, transportation planners often overlook transit investments in neighbourhoods with “transit-captive” populations because they are assumed to result in less mode-shifting, congestion relief, and environmental benefits, compared to investments that aim to attract choice riders in wealthier communities. In North American cities, while many low-income households are already transit users, some also own and use private vehicles. It suggests that transit improvements in low-income communities could indeed result in more transit use and less car use. Accordingly, the main objective of this article is to explore the statistical relationship between transit use and transit accessibility as well as how this varies by household income and vehicle ownership in the Greater Toronto and Hamilton Area (GTHA). Using stratified regression models, we find that low-income households with one or more cars per adult have the most elastic relationship between transit accessibility and transit use; they are more likely to be transit riders if transit improves. However, we confirm that in auto-centric areas with poor transit, the transit use of low-income households drops off sharply as car ownership increases. On the other hand, a sensitivity analysis suggests more opportunities for increasing transit ridership among car-deficit households when transit is improved. These findings indicate that improving transit in low-income inner suburbs, where most low-income car-owning households are living, would align social with environmental planning goals

Exploring the joint impacts of income, car ownership, and built environment on daily activity patterns: A cluster analysis of trip chains

Clustering activity patterns and identifying homogeneous travel behaviour through trip chain sequences offer valuable insight for transportation planners and policymakers in addressing transport equity problems and travel demand management. This study explores how income and car-ownership levels determine mobility patterns and travellers' decisions. Unlike previous studies that investigated the travel mode and destinations separately, we designed a novel, aggregated form considering the trip purpose and associated transport mode use as a unit of our analysis. To mitigate the subjectivity of rule-based approaches for trip chain analysis, we employ a novel sequence clustering framework to extract homogeneous clusters of activity patterns. Our results reveal that income and car-ownership levels influence travellers' travel decisions and mobility patterns. Among low-income carless households, 37% of their daily trips include care activities where women more frequently than men play this traditional role in a household by either public transit or a car as a passenger. In the low-income car-owner subsample, females still use public transit for their work trips, whereas males more often use the available car to commute to work. Males of wealthy carless households integrate public transit and active transportation for their daily trips when they live in high-density and more accessible neighbourhoods. While our findings demonstrate the impact of car ownership, income, and built environment on trip-chaining behaviour, we recognise that achieving transport equity will require tailored transportation and land use policies and investments that address the specific needs and barriers faced by different household types, particularly the most vulnerable ones in terms of sociodemographic characteristics, accessibility levels, and affordability issues. Hence, we recommend that policymakers and planners take a more holistic approach to transportation planning that considers the interplay of these factors to ensure that transportation systems and services are accessible, affordable, and equitable for all