The process of solving carpool problem with genetic and SMA* algorithm

One of the most critical issues in urban areas is the traffic jam which has harmful effects such as stress and environmental pollutions. One solution for decreasing this dilemma is standing against single-seating. Carpool is the newest way to reduce of car numbers by increasing number of present passengers in each car. This significantly decreases the traffic in urban routes. Based on recent research studies, carpool is a complex and non-linear problem. Applying the genetic optimization algorithm is a good solution to resolve this problem lead to optimally matching passengers. By compare this algorithm with other optimization algorithms Result in to show the advantage of current algorithm in carpool problem. SMA* algorithm is heuristic algorithm that make a solution with use of limited memory, search in solution space and fitness function. In this paper, we apply the SMA* algorithm to propose a new solution to carpool problem and compare with genetic algorithm. The SMA* algorithm is compared against genetic algorithm in simulated environment and the results show that genetic algorithm could get better results in terms of computational time.

Keywords:

carpool , genetic algorithm , intelligent transportation systems , SMA* algorithm