Searching for better mobility policy scenarios: Combining agent-based simulation and numerical search approaches

Abstract

Cities are increasingly adopting restrictive measures on personal car use, such as congestion charges and Low Emission Zones (LEZ), to improve air quality in large urban areas. This work proposes a reproducible study methodology to help search for better mobility policy trajectories through simulations guided by a design of experiments and the use of numerical search techniques. Using the LEZ policy of the Métropole du Grand Paris as a case study, we show how to ii) build ban scenarios and simulate them using MATSim and ii) identify effective ban strategies that minimize pollutant emissions and limit the impact on individuals. Thus, this methodology can be generalized to similar mobility policy types or other study areas.

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