| Air pollution and congestion are two negative externalities due to urban transportation activities. However, there have not been many attempts to analyze these externalities simultaneously. The aim of this research is to deal with optimal transportation network design while considering the spatial allocation of pollutant emissions. In order to provide an insight into a comprehensive range of planning options, a nonlinear programming model (NLP) has been developed. It focuses on obtaining an optimal traffic assignment by taking into account total cost minimization (e.g., total travel time, capacity investment, and fuel consumption) and air quality constraints. Based on the numerical resolution of the model for different Origin-Destination demands and meteorological conditions applied to a hypothetical network, the model demonstrates the importance of air quality considerations in designing and managing urban transportation systems. When designing an urban transportation network, it can be used to analyze not only the local effects of a proposal, but also its area-wide impacts on travel patterns and air pollution throughout the city. The spatial variations of CO pollution concentrations have important implications for the design of transportation networks. To reduce the CO pollution concentration at a particular location, it is necessary to spread pollutant emissions over the urban area, by providing more travel route choices. Enforcing more stringent air quality standards at receptors can constrain traffic flows in the network, and increase costs. The advantage of the mathematical model developed in this research lies in its potential use as a strategic planning tool for both urban air pollution and transportation planning. Furthermore, the model can be extended in many ways, such as the economic and environmental impacts of mass-transit routes in an urban area, the effects of land-use changes on traffic conditions and the resulting transportation cost and air quality, and the time dimension for allocating time-varying O-D demands and air pollution. |
