A Methodology to Evaluate the Optimization Potential of Co-ordinated Vehicular Route Choices

A car navigation system’s job is to plan a good route from an origin to a destination. There are many different options how this can be accomplished. Path choices can be calculated based on static road map representations, or they can take into account dynamic information like, e. g., known road blocks or the current traffic situation. More recently, the idea has gained ground that navigation systems could even cooperate in order to co-ordinate route choices so as to proactively avoid the formation of traffic jams. While several heuristics for algorithms to improve the vehicles’ route choices have been proposed, little is known about the potential benefit of such optimizations. How much can we gain if dynamic information exchange and/or co-ordination between vehicles are used? Answering this question requires to obtain information on the travel times realized by “best possible”, globally co-ordinated route choices—and therefore the solution of a highly complex optimization problem. Here, we propose a method to accomplish this. We use genetic algorithm optimization to jointly evolve the route choices of all cars in a street network iteratively towards an optimal solution, where the quality of each intermediate optimization step is assessed using a road traffic simulation.