Services-Oriented Computing Using the Compact Genetic Algorithm for Solving the Carpool Services Problem

Carpooling is an effective solution to traffic congestion. It increases the usage rate of vehicles by employing empty seats as a transportation resource. In order to provide carpooling services to users, we developed an intelligent carpool system called BlueNet-Ride. After prospective carpoolers submit their requests through their smart handheld devices, this system provides appropriate matches by using the proposed Low-Complexity and Low-Memory Carpool Matching method. The compact genetic algorithm is applied to our Low-Complexity and Low-Memory Carpool Matching method, which involves three proposed modules: an Evolutionary Model Initialization module, an Evolutionary Process Operation module, and an Evolutionary Model Modification module. The Evolutionary Model Initialization module takes advantage of the manipulation of the evolving population on a probability distribution to achieve low-memory requirements during the evolution process of the carpool match solution. The Evolutionary Process Operation and Evolutionary Model Modification modules simulate genetic operations to accomplish superior matching within a short amount of time. The experimental results demonstrate that our Low-Complexity and Low-Memory Carpool Matching method achieves the highest degree of performance with regard to solution quality, processing time, and memory requirements of all evaluated methods.