Personalized Pareto-improving Congestion Pricing and Tradable Credit Schemes

Abstract

Equity is a key issue that hampers public acceptability of congestion pricing. Although revenue refunding and tradable mobility credit (TMC) schemes offer a means to redress issues of equity, they cannot guarantee Pareto-improvement (i.e., no user is worse off) when toll revenues are uniformly redistributed (or credits in the case of TMCs). In this paper, we develop a bi-level optimization framework for both pricing with personalized revenue refunding and TMCs with personalized credit distribution that are efficient, equitable, and Pareto-improving. The system optimization level determines the tolling policy while the user optimization level determines an individual-specific refunding of revenue (or distribution of credits for TMCs). Simulation experiments for the morning commute problem in a combined mode and departure time context (with heterogeneity and nonlinear income effects) demonstrate that the proposed approach can make congestion tolling Pareto improving and more equitable while attaining desired improvements in network congestion and welfare.