Evaluating congestion pricing schemes using agent-based passenger and freight microsimulation

TL;DR

This study uses a large-scale agent-based microsimulation to evaluate the distributional and welfare impacts of various congestion pricing schemes on both passenger and freight transport in a prototypical North American city.

Contribution

It introduces an integrated microsimulation approach to assess the effects of congestion pricing on passengers and freight, highlighting welfare gains and regressivity issues.

Findings

Distance-based pricing yields the highest welfare gains.

Cordon and distance schemes are regressive without revenue redistribution.

Lower-income individuals and small shippers tend to lose under these schemes.

Abstract

The distributional impacts of congestion pricing have been widely studied in the literature and the evidence on this is mixed. Some studies find that pricing is regressive whereas others suggest that it can be progressive or neutral depending on the specific spatial characteristics of the urban region, existing activity and travel patterns, and the design of the pricing scheme. Moreover, the welfare and distributional impacts of pricing have largely been studied in the context of passenger travel whereas freight has received relatively less attention. In this paper, we examine the impacts of several third-best congestion pricing schemes on both passenger transport and freight in an integrated manner using a large-scale microsimulator (SimMobility) that explicitly simulates the behavioral decisions of the entire population of individuals and business establishments, dynamic multimodal network performance, and their interactions. Through simulations of a prototypical North American city, we find that a distance-based pricing scheme yields the largest welfare gains, although the gains are a modest fraction of toll revenues (around 30\%). In the absence of revenue recycling or redistribution, distance-based and cordon-based schemes are found to be particularly regressive. On average, lower income individuals lose as a result of the scheme, whereas higher income individuals gain. A similar trend is observed in the context of shippers -- small establishments having lower shipment values lose on average whereas larger establishments with higher shipment values gain. We perform a detailed spatial analysis of distributional outcomes, and examine the impacts on network performance, activity generation, mode and departure time choices, and logistics operations.