Analysis of mode choice affects from the introduction of Doha Metro using machine learning and statistical analysis

Abstract

The aim of this study was to investigate the possible influences of the operation of the new Doha Metro on the travel mode choice behavior in Doha City, Qatar. Revealed preference (RP) and stated preference (SP) survey questionnaires were designed to collect the necessary data. The questions considered different trip conditions and socioeconomic factors of travelers. Three different mode choices were considered in this study: private cars, taxi services, and metro. Two statistical models and one machine learning model were used to analyze the current and future mode choices: discrete choice binary logit (BL) and multinomial logit (MNL) models as well as extreme gradient boosting (XGBoost). Furthermore, the SHapley Additive exPlanations (SHAP) method was used to rank the input features based on their importance according to the mean SHAP value. The results showed that the XGBoost model outperforms the other two models in terms of predicting the travel mode choice as well as in terms of its accuracy. The results showed that various trip characteristics are significant in determining the mode choice, including the number of travelers and bags, journey time, and reimbursement of parking fees. Furthermore, different socioeconomic characteristics proved to be significant for the current and future mode choices, including nationality, income, age, employment status, and vehicle ownership.