Stated preference survey for predicting eco-friendly transportation choices among Mansoura University students

Abstract This paper explores the attitudes and travel mode preferences of Mansoura University students towards eco-friendly transportation options, specifically shared electric scooters (SESs) and shared bikes (SBs). An online stated preference survey was administered to 790 students via Google Forms, yielding approximately 9,590 choice scenarios for analysis. The dataset was split into training (70%) and testing (30%) sets, with 6,717 cases in the training set and 2,880 cases in the testing set. A multinomial logit model and ten machine learning classifiers were used to predict mode choice behavior. The variables considered in the models include total travel time, travel cost, gender, health issues, bike ownership, ability to ride a bike and scooter, residency, prior knowledge of shared modes, smartphone ownership, frequency of public transport use, trip cost, trip time, and monthly personal income. Model performance was evaluated at two levels: individual and aggregate. At the individual level, accuracy reflects the overall percentage of correct predictions. At the aggregate level, accuracy is assessed based on the predicted market shares for each mode. The Extreme Gradient Boosting (XGB) model was the most accurate, achieving an accuracy slightly above 60%, followed by the Gradient Boosting (GB) and Random Forest (RF) models, both with accuracies slightly above 50%. Ensemble models like RF, GB, and XGB provided relatively accurate aggregate predictions with minimal deviations from actual mode shares. These models slightly overestimated the bus mode share but offered accurate predictions for other modes, making them robust choices for this classification task.