Q Value-Based Dynamic Programming with Boltzmann Distribution for Global Optimal Traffic Routing Strategy

In this paper, we propose a heuristic method -- Boltzmann Optimal Route Method trying to find a good approximation to the global optimum route for Origin-Destination pairs through iterations until the total traveling time converges. The overall idea of our method is to update the traveling time of each route section iteratively 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- Greedy strategy both in the static traffic system where the volumes of all the given Origin-Destination pairs of road networks are constant and in the dynamic traffic system in which changing traffic volumes are constantly provided. The results demonstrate that the proposed method performs better than the conventional method in global perspective.