Vehicle Routing Algorithm Reduces Air Pollutant Exposure by 30 Percent for 40 Percent of Truck Trips with Only Modest Increases on Travel Time.

Southern California Case Study Assesses the Impacts of Eco-Routing.

Date Posted

Connected Emission Control Technologies for Freight Vehicles

Summary Information

Medium- and heavy-duty diesel trucks, the majority of which are used for freight movement, are significant contributors of nitrogen oxides (NOx) and particulate matter (PM) emissions. As a result, areas close to freight hubs such as ports, railyards, and distribution centers often experience elevated levels of diesel-related air pollution. There is great potential in applying connected vehicle (CV) technology to reduce the environmental and health impacts of freight vehicles on the designation of disadvantaged communities (DACs).

One such application is eco-routing that determines a travel route between an origin and a destination that would make the vehicle consume the least amount of fuel. This project aimed to develop new vehicle routing algorithms for heavy-duty diesel trucks that would reduce the exposure of local residents to air pollutant emissions from these trucks, and to evaluate the benefits of the new vehicle routing algorithms in terms of reductions in air pollutant exposure. Simulation-based experiments were conducted using the Reseda-Northridge area of Southern California as a case study to evaluate the potential benefits of this air pollution mitigation strategy. The studied area features a road network with a variety of road types (freeways, arterials, collectors, etc.) and has densely populated communities with a large fraction of children and seniors who are more sensitive to air pollution. The year 2010 was selected based on consideration of the best availability of the full range of data (e.g., 2010 U.S. Census) at the time of conducting this study.


Link-based traffic activities were fed into an emission model to estimate the total amount of human exposure to pollutant emissions generated by trucks. The estimated exposure value was used in a least-cost-path algorithm to find a travel route that would minimize the total exposure value for the trip, with the total exposure value representing the amount of pollutant generated by the truck that is inhaled by local residents. The estimation of this value involves a modeling chain that goes from traffic activity to emissions production, to air pollutant dispersion, and finally to human exposure.

The study evaluated 400 different trips for the Reseda-Northridge area of Southern California. For each trip, both the fastest route and the lowest exposure route identified by the developed algorithm were determined. The differences in travel time, total exposure to fine particulate matter (PM2.5), and total exposure to reactive organic gases (ROG) between the two routes were compared.


  • The total pollutant exposure by target population groups can be greatly reduced with small adjustments to route choice.
  • Compared to the fastest route possible, the lowest exposure route (emissions impact on surrounding area) was found to result in a more than 30 percent reduction in total air pollutant exposure for about 40 percent of the 400 simulated trips.
  • The lowest exposure route kept the increase in trip travel time to no more than 10 percent.
  • When coupled with clean vehicle technology, a larger pollutant exposure reduction can be achieved.
  • The low exposure routing concept is particularly valuable for mitigating the air quality impact of high-emitting vehicles (e.g., HDDTs) in disadvantaged communities as well as near sensitive facilities such as schools and hospitals.
Results Type