Dynamic simulation modeling and policy analysis of an area-based congestion pricing scheme for a transportation socioeconomic system

This paper evaluates the impact of an area-based congestion pricing scheme in terms of its effectiveness on mitigating traffic congestion by using a system dynamics model. Unknown parameter values are calibrated using data available from the area-based pricing scheme implemented in the London metropolitan area. The key features of our model are that individual behavior is affected by the level of congestion, the cost of driving, and the supply/capacity and demand associated with metro transit. Perceptions of users are captured by three separate linguistic variables and fuzzy set theory is used to evaluate the combined effects of individual perceptions on the travel mode selection and the switching behavior between travel modes. As part of our analysis we explore three premises, i.e., that revenues generated from a congestion pricing scheme can substantially improve alternative transportation modes, that the improvement of these modes can have a positive effect on the mitigation of traffic congestion, and that a congestion pricing scheme cannot effectively resolve congestion problems in short term due to the existence of material and information delays. We assess various policies and determine appropriate values for critical parameters to find the best results in terms of implementing the area-based pricing scheme.