Rapid urbanisation and economic growth have resulted in high transportation demand in cities. The widespread use of private automobiles has been linked with emissions, crashes and the misuse of public space, prompting policymakers to seek car-use reduction policies. However, policymakers are often confronted with uncertainty about the effectiveness and suitability of those policies and their impacts on citizens. This study presents a simulation-based framework to evaluate car-use reduction measures based on a case study in Munich, Germany. The framework employs a discrete choice model, developed using stated preference data from a representative sample, to model and simulate mode choices under combinations of car-reducing measures. The model is then integrated into an agent-based simulation, calibrated and validated against the latest available household travel survey. The simulation results indicate that promoting active mobility and reducing speed limits for motorised traffic may be the prevailing strategy, with positive environmental effects. The agent-based framework allows for impact analysis across different population segments and can be extended to consider accessibility and equity metrics for diverse groups.
Adamidis et al. (Thu,) studied this question.