Minimizing vehicle emissions through transportation road network design incorporating demand uncertainty

Traditionally, transportation road networks have been designed for minimal congestion. Unfortunately, such approaches do not guarantee minimal vehicle emissions. Given the negative impacts of vehicle pollutants as well as tighter national air quality standards, it is critical for regions to be able to identify capacity modifications to road networks such that vehicle emissions are minimal. This ability combined with land use changes and opportunities for non-auto travel are paramount in helping regions improve air quality. However, network design research has yet to directly address this topic.
To fill this apparent gap in network design research, an emissions network design problem and solution method are proposed in this thesis. Three air pollutants are considered: hydrocarbons, nitrogen oxides, and carbon monoxide. The proposed model is applied to two road networks: Sioux Falls, ND and Anaheim, CA. The model is a bi-level optimization problem solved using a genetic algorithm and incorporates the influence of demand uncertainty. Findings indicate designing for minimal congestion tends to increase emissions of criteria air pollutants. However, not adding capacity to a road network also increases emissions of pollutants. Therefore, an optimization problem and solution method, such as the model presented here, is useful for identifying capacity additions that reduce vehicle emissions. It is also useful for understanding the tradeoffs between designing a network for minimal congestion versus minimal vehicle emissions.