Minimizing the carbon emissions on road networks

The models and algorithms developed for transportation planning, vehicle routing, path finding and the software that utilize them are usually based on distance and constant travel times between the relevant locations and aim at minimizing total distance or travel time . However, constant travel time assumption is not realistic on road networks as the traffic conditions may vary from morning/evening rush hours to off-peak noon/night hours, from the weekends to business days, even from one season to another. Thus, distance/time based optimization does not exactly reflect the real fuel consumptions, hence the actual costs; neither can they be used to accurately account for the greenhouse gas (GHG) emissions. A distance/constant time based optimization model may even yield an infeasible solution when time-windows exist or the route length is time limited. In this study, we first analyze the peculiar characteristics of the Greenest Path Problem (GPP) where the objective is to find the least GHG generating path from an origin to a destination on the road network. We then propose a fast heuristic method for determining the greenest path, by incorporating fuel consumption and GHG emission objectives. Finally, we integrate the proposed algorithm into the Green Vehicle Routing Problem that minimizes the GHG emissions rather than the total distance or travel time. The developed heuristic is benchmarked against the existing algorithms by using synthetic traffic data on a real road network to illustrate potential savings and sustainability benefits.