Pricing vehicle emissions and congestion externalities using a dynamic traffic network simulator

Road traffic is a major contributor to air pollution which is a serious problem in many large cities. Experience in London, Milan, and Stockholm indicates that road pricing can be useful in reducing vehicle emissions as well as congestion. This study uses a dynamic traffic network simulator that models choices of mode, departure time, and route to investigate the effectiveness of tolls to target emissions and congestion externalities on a stylized urban road network during a morning commuting period. The spatial distribution of four pollutants is calculated using a Gaussian dispersion model that accounts for wind speed and direction. Single and double cordon tolls are evaluated, as well as flat tolls that do not change during the simulation period and step tolls that change at half-hourly intervals. The presence of emissions externalities raises optimal toll levels, and substantially increases the welfare gains from tolling, although the proportional advantage of step tolls over flat tolls is lower than if congestion is the only externality. The individual welfare distributional effects of tolling vary strongly with residential and workplace locations relative to the cordon, and also differ for the upwind and downwind sides of the city.

Automated summary

Road traffic is a major cause of air pollution in large cities. Road pricing has been proven effective in reducing vehicle emissions and congestion based on experiences in London, Milan, and Stockholm. This study investigates the impact of tolls on emissions and congestion externalities using a dynamic traffic network simulator. The results show that the presence of emissions externalities increases the optimal toll levels and the welfare gains from tolling.