Integrating data mining and transformer models for socially beneficial bicycle scheduling in urban bicycle-sharing

The rapid growth of bicycle-sharing systems in urban settings necessitates the implementation of effective scheduling strategies to optimise resource allocation and tackle challenges such as uneven distribution of bicycles and operational inefficiencies. This study introduces an innovative integration of data mining techniques and transformer models aimed at enhancing bicycle scheduling. By leveraging the MARO resource scheduling platform, we simulate bicycle mobility and scrutinise demand patterns through electronic fence clustering. We evaluate two distinct scheduling strategies: a dynamic programming-based approach and a transformer-based methodology. Experimental findings reveal that the proposed transformer model markedly decreases average time overhead and path distances in comparison to conventional methods, thereby fostering more efficient and socially advantageous bicycle-sharing systems. This research significantly contributes to the optimisation of urban transportation and the advancement of sustainable mobility solutions.