Equitable Congestion Pricing under the Markovian Traffic Model: An Application to Bogota

TL;DR

This paper develops a data-driven, equitable congestion pricing model using a Markovian traffic equilibrium framework, applied to Bogota, demonstrating personalized pricing improves efficiency and equity.

Contribution

It extends the Markovian traffic equilibrium model to include heterogeneous populations and evaluates various pricing schemes with real city data.

Findings

Personalized pricing significantly improves efficiency and equity.

Area-based pricing recovers much of the benefits of personalized schemes.

Unique equilibrium exists under the extended model.

Abstract

Congestion pricing is used to raise revenues and reduce traffic and pollution. However, people have heterogeneous spatial demand patterns and willingness (or ability) to pay tolls, and so pricing may have substantial equity implications. We develop a data-driven approach to design congestion pricing given policymakers' equity and efficiency objectives. First, algorithmically, we extend the Markovian traffic equilibrium setting introduced by Baillon & Cominetti (2008) to model heterogeneous populations and incorporate prices and outside options such as public transit. In this setting, we show that a unique equilibrium exists. Second, via a detailed case study, we empirically evaluate various pricing schemes using data collected by an industry partner in the city of Bogota, one of the most congested cities in the world. We find that pricing personalized to each economic stratum can be substantially more efficient and equitable than uniform pricing; however, non-personalized but area-based pricing can recover much of the gap.