Equitable Congestion Pricing under the Markovian Traffic Model: An Application to Bogota
For policymakers in congested cities, this provides a practical method to design congestion pricing that addresses equity concerns, with empirical validation in a real-world setting.
This paper develops a data-driven approach for congestion pricing that balances equity and efficiency, extending the Markovian traffic equilibrium model to heterogeneous populations. Applied to Bogota, personalized pricing per economic stratum is more efficient and equitable than uniform pricing, but area-based pricing recovers much of the gap.
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.