Bi-objective evolutionary optimization of level of service in urban transportation based on traffic density

This work investigates levels of service in urban transportation coupling a multi-objective evolutionary algorithm with the multi-agent traffic simulator MATSim. The evolutionary algorithm searches combinations of the number of private/public transportation users, capacity of buses, and time interval between bus departures minimizing traffic density and travel time simultaneously. MATSim simulates the movement of 27,000 agents according to the solutions of the evolutionary algorithm on a model of the traffic network of Quito city. We study the trade-off in objectives and analyze the solutions produced to gain knowledge about the conditions to achieve different levels of service. Also, we analyze fuel consumption and particulate matter emissions for the trade-off solutions. This work is useful for decision makers to suggest policies that can improve mobility combining private and public transportation.