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. Second, 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，人们的空间出行需求模式以及支付通行费的意愿（或能力）存在异质性，因此收费政策可能产生显著的公平性影响。我们提出了一种数据驱动的方法，在给定政策制定者公平与效率目标的前提下设计拥堵收费方案。首先，在算法层面，我们扩展了Baillon与Cominetti（2008）提出的马尔可夫交通均衡框架，以建模异质性人群，并纳入价格及公共交通等外部选择。其次，我们利用一家行业合作伙伴在波哥大市（全球拥堵最严重的城市之一）采集的数据，对不同收费方案进行了实证评估。研究发现，针对不同经济阶层进行个性化定价，其效率与公平性可显著优于统一收费；然而，非个性化但基于区域的收费方案能够弥补大部分差距。