Context-aware hybrid reinforcement and recurrent learning for student home-campus trip prediction

Predicting individual mobility is essential for transport planning and sustainability strategies, as it helps anticipate travel demand. However, most studies of student mobility remain descriptive and cannot be applied to predict trip attributes, despite such prediction being essential for proposing personalised mobility recommendations that account for students' contextual constraints, such as academic schedules. This paper introduces CAH2RL-Move, a two-stage method for predicting student trip features, including origin, destination, travel mode, and departure time. The approach uses recurrent encoders with attention mechanisms to capture spatial routines and variations in travel mode, then applies deep reinforcement learning to model the influence of academic schedules on departure times. The model is validated using a dataset constructed from real student mobility preferences and actual academic schedules. Compared with baseline approaches, CAH2RL-Move achieves 80.74%–88.61% departure time prediction accuracy within 30-minute windows across four student mobility cases, enabling informed mobility recommendations.