How to identify factors influencing mode choice behaviors within a heterogeneous metropolitan area? A novel approach midway between data science and econometrics

Estimating factors influencing mode choice behaviors has posed a significant challenge for decision makers. In this paper, we propose a three-step method to estimate mode choice between public transportation and private vehicles within a heterogeneous metropolitan area. First, we implement a deep learning model, Altaïr, capable of inferring travel times and travel flows by leveraging multi-source input data (Step 1). To identify homogeneous sub-regions regarding mode choice behaviors, two data clustering models are performed: k-means and a Gaussian Mixture Model (GMM, Step 2). The GMM reveals three spatial clusters based on the relationships between relative travel times and relative travel flows in public transportation and private vehicles. Moreover, an econometric model (robust ordinary least squares) is employed to identify additional explanatory variables, including sociodemographic features and location variables (Step 3). This hybrid method is currently employed in the Paris metropolitan area (France). At the metropolitan level, we find that competitive travel times in public transportation lead to higher ridership. Conversely, when the time ratio exceeds approximately 3.5–4, public transportation use becomes negligible in comparison to private vehicles. This hybrid method allows for precise, interpretable prediction of mode choice between public transportation and private vehicles. The method's results are close to those of the regional Household Travel Surveys (HTS), as shown in a validation analysis. The design ensures the transferability of the method to any metropolitan area, including areas with incomplete data.