An intelligent and fair GA carpooling scheduler as a social solution for greener transportation

Although many carpooling systems have been proposed, most of them lack various levels of automation, functionality, practicality, and solution quality. While Genetic Algorithms (GAs) have been successfully adopted for solving combinatorial optimization problems, their use is still rare in carpooling problems. Motivated to propose a solution for the many to many carpooling scenario, we present in this paper a GA with a customized fitness function that searches for the solution with minimal travel distance, efficient ride matching, timely arrival, and maximum fairness. The computational results and simulations based on real user data show the merits of the proposed method and motivate follow on research.