Multi-objective optimization of level of service in urban transportation.

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 number of private/public transportation users, capacity of buses, and time interval between bus departures minimizing traffic density, travel time and fuel consumption 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 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.