Understanding and Predicting the Usage of Shared Electric Scooter Services on University Campuses

Electric vehicles (EVs) have been progressing rapidly in urban transport systems given their potential reducing emissions and energy consumptions. The Shared Free-Floating Scooter (SFFES) is an emerging EV publicized to address the first-/last-mile problem travel. It also offers alternatives for short-distance journeys using cars or ride-hailing services. However, very few SFFES studies carried out developing countries university populations. Currently, many universities are facing increased number of private car travels on campus. study designed explore attitudes perceptions students staff towards usage campus corresponding influencing factors. Three machine learning models were used predict usage. Eleven important factors SFFESs identified via supervised unsupervised feature selection techniques, with top three being daily travel mode, road features (e.g., green spaces) age. random forest model showed highest accuracy predicting frequency (93.5%) selected 11 variables. A simulation-based optimization analysis was further conducted discover characterization users, barriers/benefits safety concerns.