Generation of realistic mobility for VANETs using genetic algorithms

The first step in the evaluation of vehicular ad hoc networks (VANETs) applications is based on simulations. The quality of those simulations not only depends on the accuracy of the network model but also on the degree of reality of the underlying mobility model. VehILux-a recently proposed vehicular mobility model, allows generating realistic mobility traces using traffic volume count data. It is based on the concept of probabilistic attraction points. However, this model does not address the question of how to select the best values of the probabilities associated with the points. Moreover, these values depend on the problem instance (i.e. geographical region). In this article we demonstrate how genetic algorithms (GAs) can be used to discover these probabilities. Our approach combined together with VehILux and a traffic simulator allows to generate realistic vehicular mobility traces for any region, for which traffic volume counts are available. The process of the discovery of the probabilities is represented as an optimisation problem. Three GAs-generational GA, steady-state GA, and cellular GA-are compared. Computational experiments demonstrate that using basic evolutionary heuristics for optimising VehILux parameters on a given problem instance permits to improve the model realism. However, in some cases, the results significantly deviate from real traffic count data. This is due to the route generation method of the VehILux model, which does not take into account specific behaviour of drivers in rush hours.