Traffic-Aware Adaptive Routing for Minimizing Fuel Consumption

Fuel consumption in the vehicles are dependent on two variables of the route-deterministic and stochastic variables. The deterministic variable comprises length, elevation, curvature of road and stop signs along the route. The stochastic variable comprises variability in the velocity due to traffic and traffic lights. In this paper, we formulate the problem of traffic aware adaptive routing as a Markov decision problem (MDP) with total cost criterion featuring a continuum of state and finite actions. We show that the value iteration algorithm associated with our model would converge. Since it is impractical to implement value iteration on continuous spaces, we use Q learning with function approximation to perform numerical simulations to learn routing policies.