A Genetic-Algorithm-Based Approach to Solve Carpool Service Problems in Cloud Computing

Traffic congestion has been a serious problem in many urban areas around the world. Carpooling is one of the most effective solutions to traffic congestion. It consists of increasing the occupancy rate of cars by reducing the empty seats in these vehicles effectively. In this paper, an advanced carpool system is described in detail and called the intelligent carpool system (ICS), which provides carpoolers the use of the carpool services via a smart handheld device anywhere and at any time. The carpool service agency in the ICS is integrated with the abundant geographical, traffic, and societal information and used to manage requests. For help in coordinating the ride matches via the carpool service agency, we apply the genetic algorithm to propose the genetic-based carpool route and matching algorithm (GCRMA) for this multiobjective optimization problem called the carpool service problem (CSP). The experimental section shows that the proposed GCRMA is compared with two single-point methods: the random-assignment hill climbing algorithm and the greedy-assignment hill climbing algorithm on real-world scenarios. Use of the GCRMA was proved to result in superior results involving the optimization objectives of CSP than other algorithms. Furthermore, our GCRMA operates with significantly a small amount of computational complexity to response the match results in the reasonable time, and the processing time is further reduced by the termination criteria of early stop.