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Large-scale ride-hailing systems often combine real-time routing at theindividual request level with a macroscopic Model Predictive Control (MPC)optimization for dynamic pricing and vehicle relocation. The MPC relies on ademand forecast and optimizes over a longer time horizon to compensate for themyopic nature of the routing optimization. However, the longer horizonincreases computational complexity and forces the MPC to operate at coarserspatial-temporal granularity, degrading the quality of its decisions. Thispaper addresses these computational challenges by learning the MPCoptimization. The resulting machine-learning model then serves as theoptimization proxy and predicts its optimal solutions. This makes it possibleto use the MPC at higher spatial-temporal fidelity, since the optimizations canbe solved and learned offline. Experimental results show that the proposedapproach improves quality of service on challenging instances from the New YorkCity dataset.

Learning Model Predictive Controllers for Real-Time Ride-Hailing Vehicle Relocation and Pricing Decisions