Importance

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Abstract

Described herein are techniques for optimizing real-time traffic flow of a multi-class vehicle network. In some embodiments, a trained machine learning model may be used to determine one or more segments of a street network where traffic congestion is predicted to occur at a predetermined future time. The vehicles may include a first type of vehicle and a second type of vehicle, each occupying different portions of a given segment. A subset of vehicles occupying the segments at the predetermined future time may be identified and, for each, a set of alternative routes may be determined. Quantum optimization data, including the pre-selected routes, the alternative routes, and an indication of whether the vehicle is of a first or second type, may be provided to a quantum computing system, which may return an updated route for at least some of the vehicles to minimize traffic congestion at the predetermined future time.

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First Claim