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This paper proposes a non-monetary traffic demand management scheme, namedCARMA, as a fair solution to the morning commute congestion. We considerheterogeneous commuters traveling through a single bottleneck that differ inboth the desired arrival time and Value of Time (VOT). We consider ageneralized notion of VOT by allowing it to vary dynamically on each day (e.g.,according to trip purpose and urgency), rather than being a staticcharacteristic of each individual. In our CARMA scheme, the bottleneck isdivided into a fast lane that is kept in free flow and a slow lane that issubject to congestion. We introduce a non-tradable mobility credit, namedkarma, that is used by commuters to bid for access to the fast lane. Commuterswho get outbid or do not participate in the CARMA scheme instead use the slowlane. At the end of each day, karma collected from the bidders isredistributed, and the process repeats day by day. We model the collectivecommuter behaviors under CARMA as a Dynamic Population Game (DPG), in which aStationary Nash Equilibrium (SNE) is guaranteed to exist. Unlike existingmonetary schemes, CARMA is demonstrated, both analytically and numerically, toachieve a) an equitable traffic assignment with respect to heterogeneous incomeclasses and b) a strong Pareto improvement in the long-term average traveldisutility with respect to no policy intervention. With extensive numericalanalysis, we show that CARMA is able to retain the same congestion reduction asan optimal monetary tolling scheme under uniform karma redistribution and evenoutperform tolling under a well-designed redistribution scheme. We alsohighlight the privacy-preserving feature of CARMA, i.e., its ability to tailorto the private preferences of commuters without centrally collecting theinformation.

CARMA: Fair and efficient bottleneck congestion management via non-tradable karma credits