Agent-based Simultaneous Optimization of Congestion and Air Pollution: A Real-World Case Study

The exclusion of external costs from the behavioral decision making process of individuals yields travel demand beyond the system optimum which implies inefficiencies in the transport system. The present study investigates the effect of congestion optimization on emissions levels and vice versa while considering heterogeneity in individual attributes and choice behavior. In consequence, the resulting correction terms (tolls) are highly differentiated. Furthermore, and going beyond existing literature, the present study proposes a joint optimization of vehicular congestion and emissions. The proposed model uses a microscopic agent-based simulation framework which is applied to a real-world scenario of the Munich metropolitan area in Germany. The combined pricing scheme accounts for both external effects and in an iterative process, agents learn how to adapt their route and mode choice decisions in presence of this combined toll. The results indicate that the combined pricing strategy moves the car transport system towards the optimum, measured by a strong decrease of congestion and emission costs. Furthermore, it is found that pricing emissions only pushes users on routes with shorter distances, whereas pricing congestion only steers users on routes with shorter travel times, and potentially longer distances. That is, the two pricing strategies influence behavior by tendency into opposite directions