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.

翻译：拥堵收费旨在增加财政收入并减少交通拥堵与污染。然而，由于人群具有异质性的空间出行模式和对通行费的支付意愿（或能力），定价可能产生显著的公平性问题。我们提出了一种数据驱动的方法，用于在政策制定者兼顾公平与效率目标时设计拥堵收费方案。首先，在算法层面，我们扩展了Baillon & Cominetti（2008）提出的马尔可夫交通均衡模型，以刻画异质性人群，并引入价格机制及公共交通等外部选择。在该设定下，我们证明了唯一均衡的存在性。其次，通过详细的案例研究，我们利用一家行业合作伙伴在世界上交通最拥堵的城市之一——波哥大收集的数据，对多种定价方案进行了实证评估。研究发现，针对不同经济阶层实施个性化定价在效率和公平性上均显著优于统一定价；然而，基于区域的非个性化定价也能在很大程度上弥补两者之间的差距。