Activity-Based Approach for the Design of Sustainable Area and Cordon Pricing Schemes

Abstract

Background Vehicle-generated emissions remain a serious threat to public health, and a major contributing factor to climate change. In response to this situation, several cities around the world have successfully reduced vehicle emissions by implementing area and cordon-based pricing (ACP). Despite these successes, designing ACP schemes continues to be a challenging task given the complexities of estimating the multidimensional effects of this type of strategy. Existing transportation network design methodologies focus primarily on congestion-related goals, and employ an aggregate representation of travel demand. However, the assessment of the public health impacts of vehicle emissions requires information on disaggregate travel and activity participation behavior, as well as spatially and temporally varying pollutant concentrations; existing methodologies do not meet the planning needs of agencies interested in determining the optimal design of ACP schemes that explicitly account for both congestion and public health. Methods We propose an optimization model that can be used by transportation planning agencies to determine optimal charging boundary locations and toll levels with the objective of reducing the health impacts of vehicular traffic and improve mobility. The proposed ACP design problem is formulated as a bi-level, simulation-based optimization model. The problem's upper-level is composed of the policy makers’ goals (i.e., health and mobility objectives). The travelers’ responses to the policy maker's decisions, as well as the resulting system-wide impacts, are analyzed in the lower-level. The lower-level model system is composed of five sub-models: (a) a disaggregate activity-based travel behavior model to simulate the agents’ behavioral response to ACP specifications, (b) a traffic assignment model to estimate the distribution of traffic in the network, (c) a traffic emissions model, (d) a pollutant dispersion model, and (e) a health impact assessment model to estimate the changes in public health risks resulting from ACP schemes. The proposed methodology is computationally expensive given the type of models being combined. Therefore, we present a multi-objective, surrogate-based optimization approach that is intended to accelerate the discovery of good solutions to the design problem. Results An illustrative application of the proposed methodology is presented using Chicago as a case study. As expected, preliminary results suggest that there is a tradeoff between improving travel mobility objectives and reducing traffic's public health impacts. Conclusions The proposed methodology can be an important tool for transportation planners. Further research is necessary to refine the different parameters and strategies used in the solution algorithm.