A Driver and Riders Matching Approach

Dependence on personal automobiles is becoming increasingly costly due to accelerating climate change and rising gasoline prices. It is particularly wasteful when one realizes that most car seats are typically empty. With the advancement of mobile social networking technologies, it is necessary to reconsider the principles and desired characteristics of ride-sharing systems. Ride-sharing systems can be popular among people if we can provide more flexible and adaptive solution according to preferences of the participants and solve the social challenges. In this paper, we present the genetic algorithm for solving the riders and drivers matching problem with different conflicting objectives. The experiment results of the proposed algorithm indicates the superior performance over the generalized label correcting algorithm in terms of quality and runtime.