A Fair and Efficient Bottleneck Congestion Management with CARMA

This talk demonstrates the use of CARMA (=karma for cars) as a fair solution to the morning commute congestion. We consider heterogeneous commuters traveling through a single bottleneck that differ in the value of time (VOT), generalizing the notion of VOT to vary dynamically on each day (e.g., according to trip purpose and urgency) rather than being a static characteristic of each individual. In our CARMA scheme, the bottleneck is divided into a fast lane that is kept in free flow and a slow lane that is subject to congestion. Commuters use karma to bid for access to the fast lane, and those who get outbid or do not participate in the scheme instead use the slow lane. At the end of each day, karma collected from the bidders is redistributed, and the process repeats day by day. We specialize the karma economy mean-field game model to this setting and analyze pthe roperties of its mean-field equilibrium. Unlike existing monetary schemes, CARMA is demonstrated to achieve (a) an equitable traffic assignment with respect to heterogeneous income classes and (b) a strong Pareto improvement in the long-term average travel disutility with respect to no policy intervention. Moreover, CARMA can retain the same congestion reduction as an optimal monetary tolling scheme under uniform karma redistribution and even outperforms tolling under a well-designed redistribution scheme.