The Economics of Carpooling, Road Pricing, and Autonomous Transportation
Project Outcomes Statement
The project involves the creation of a theoretical and computational framework for improving traffic congestion and analyzing the interplay among three technologies that will have a major impact on transportation in the future: road pricing, carpooling, and autonomous driving.
The papers developed as part of this project include:
- "Carpooling and the Economics of Self-Driving Cars" (with Michael Schwarz)
- "Congestion Pricing, Carpooling, and Commuter Welfare" (with Michael Schwarz)
- "A Practical Approach to Congestion Pricing: The Case of New York City" (with Frank Yang)
- "On the Interplay between Self-Driving Cars and Public Transportation" (with Nicolas Lanzetti, Maximiliian Schiffer, and Marco Pavone)
We have also developed a database of congestion pricing schemes around the world (both existing ones and ones currently in development) to make it easier for researchers and policymakers to see the state of the art in the area of congestion pricing.
Several features distinguish our approach from the earlier ones. First, we argue that our flexible approach is superior to the inflexible "cordon pricing" that has been adopted in several cities around the world, because it allows to use time-sensitive tolls to regulate the pattern of inflows to the Central Business District in the morning, the outflows in the evening, and the circulation of fleet vehicles (taxies, ridehailing companies, delivery companies, etc.) throughout the day. Second, by building on Vickrey's seminal "bottleneck model", we simplify the estimation of tolls vs. what would be required to compute Pigouvian taxes based on the externalities imposed by the drivers on others. Finally, we emphasize the importance of considering carpooling jointly with congestion pricing, as the two technologies are highly completentary and jointly have the potential to lead to a much greater improvement in commuter welfare than either one on its own.
Investigator
Supported by the National Science Foundation grant #1824317
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