Trust-Based Recommendation for Shared Mobility Systems Based on a Discrete Self-Adaptive Neighborhood Search Differential Evolution Algorithm

Safety is one concern that hinders the acceptance of ridesharing in general public. Several studies have been conducted on trust issue recent years to relieve this concern. The introduction systems provides a pragmatic approach solving problem. In study, we will develop trust-aware recommender system decision model generate recommendations for drivers and passengers. requirements both sides, passengers, are taken into consideration proposed paper. considers factors typical systems, including vehicle capacities, timing, location requirements, etc. aims determine shared rides minimize cost while respecting relevant constraints. As problem nonlinear integer programming problem, combine self-adaptive neighborhood search with Differential Evolution an algorithm solve it. To assess effectiveness algorithm, several other evolutionary computation approaches also applied same assessment done based performance applying different algorithms find solutions test cases, provide guideline selecting proper solution approach.