基于马尔可夫交通模型的公平拥堵定价：波哥大应用
Equitable Congestion Pricing under the Markovian Traffic Model: An Application to Bogota
AI总结：
本文提出基于马尔可夫模型的数据驱动方法，研究公平与效率的拥堵定价设计，通过波哥大案例验证个性化定价在效率和公平性上的优势，同时发现基于区域的非个性化定价可弥补部分差距。
摘要：
拥堵定价用于提高收入并减少交通和污染，但人们在空间需求和支付意愿上存在异质性，因此定价可能有重大公平影响。本文开发了一种数据驱动方法，扩展了Baillon & Cominetti (2008)提出的马尔可夫交通均衡模型，以建模异质人口并纳入价格和公共交通等外部选项。在此设定下，证明了唯一均衡的存在。通过详细案例研究，利用行业合作伙伴在波哥大收集的数据，评估了各种定价方案。研究发现，针对不同经济阶层的个性化定价在效率和公平性上显著优于统一定价；然而，非个性化但基于区域的定价可以恢复大部分差距。
英文摘要：
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.
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