Paper Details

Traffic congestion has been a serious problem in many urban areas around the world. Carpooling is one ofthe most effective solutions to traffic congestion. It consists of increasing the occupancy rate of cars by reducing theempty seats in these vehicles effectively. In this paper, an advanced carpool system is described in detail and called theintelligent carpool system (ICS), which provides carpoolers the use of the carpool services via a smart handheld deviceanywhere 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 serviceagency, we apply the genetic algorithm to propose the genetic-based carpool route and matching algorithm (GCRMA)for this multi objective optimization problem called the carpool service problem (CSP).The experimental section shows that the proposed GCRMA is compared with two single-point methods: the randomassignment hill climbing algorithm and the greedy assignment hill climbing algorithm on real-world scenarios. Use ofthe 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 matchresults in the reasonable time, and the processing time is further reduced by the termination criteria of early stop.

Ragini Wagh, Komal Karande, Tasneem Sayyed, L.J.Sankpal, “A Genetic Algorithm Based Approach to Solve Carpool Service Problems in Cloud Computing”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 3, Issue 13, pp. -, January 2016.