Simulation-based impact assessment framework for driving restriction zone policies

Abstract

Conventional methods for predicting emissions predominantly rely on site data-driven regression models, thus failing to evaluate mobility impacts of transport policies. This research demonstrates how agent-based simulation has been used to assess ex-ante impacts of one of these policies, driving restriction zone, enabling policymakers to evaluate and refine such interventions during the design phase. The proposed framework examines impacts on mobility and the environment at various aggregation levels. The case study assesses two policy scenarios with varying tolerance levels for road network access by unauthorized non-residents in residential areas; each one-fifth of non-local drivers is restricted every fifth weekday, based on license-plate digits. The findings reveal that both policies reduce car usage, albeit with a slight cost to traffic efficiency. Additionally, the policies contribute to a notable decrease in CO₂ emissions and local air pollutants across all agent groups, citywide, and more locally.