Shortest Driving Time Computation Based on Cloud Technologies and Genetic Algorithm

By gathering information of communications between people, vehicles, and roads, the Intelligent Transportation System (ITS) improves traffic service quality, safety, efficiency, and convenience of the conventional transportation system. The paper proposes a method based on cloud computing and the genetic algorithm to provide improvements of performance boost, accuracy, and efficiency of the current transportation system. Maps are partitioned into layers in order to improve reusability of data. For maps with large number of nodes, the required execution time of the genetic algorithm will increase; moreover, different chromosome initialization settings and/or different feedback mechanisms are required to avoid failure or premature convergence of the genetic algorithm. For applications with tremendous number of traffic nodes, the proposed approach is a feasible solution since it can offer approximate solutions without consuming too much of resources; besides, the results also show that the approach can obtain approximately optimal solutions much faster and better.