  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.
