A Q value-based Dynamic Programming algorithm with Boltzmann Distribution for optimizing the global traffic routing strategy

In this paper, we propose a heuristic method trying to find a good approximation to the global optimum route for origin-destination pairs through iterations until the total traveling time converges in static traffic systems. The overall idea of our method is to iteratively update the traveling time of each route section according to its corresponding traffic volume, and continuously generate a new global route by Q value-based dynamic programming combined with Boltzmann distribution. Finally, we can get the global optimum route considering the traffic volumes of the road sections. The new proposed method is compared with the conventional shortest-path method and the result demonstrates that the proposed method performs better than the conventional method in global perspective.