Optimizing long-term carpooling with fairness: A collaborative Jaya algorithm

Abstract

Inspired by Japan’s unique regulatory framework, this study addresses the Long-Term Carpooling Problem with Fairness (LTCPF), with a focus on enhancing sustainable urban transport. We investigate this issue by optimizing carpooling arrangements to balance travel time, ensure inclusive rider participation, and reduce detour time discrepancies. At the core of our approach is the Collaborative Jaya Algorithm (CJA), a modification of the existing Jaya algorithm with improved computational efficiency and reduced hyperparameter dependency. Our model assigns explicitly fixed roles to participants as drivers or riders, promoting efficient and equitable carpooling. The practical efficacy of the CJA is validated through rigorous simulation experiments across various scenarios. The simulation results demonstrate that the proposed algorithm is superior to existing counterparts.