Equitable Congestion Pricing under the Markovian Traffic Model: An Application to Bogota
arXiv:2407.05035v2 Announce Type: replace 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 indus
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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.
Subjects:
Computer Science and Game Theory (cs.GT); Optimization and Control (math.OC)
Cite as: arXiv:2407.05035 [cs.GT]
(or arXiv:2407.05035v2 [cs.GT] for this version)
https://doi.org/10.48550/arXiv.2407.05035
arXiv-issued DOI via DataCite
Submission history
From: Alfredo Torrico [view email] [v1] Sat, 6 Jul 2024 10:01:00 UTC (11,110 KB) [v2] Mon, 30 Mar 2026 20:03:38 UTC (10,238 KB)
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