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We present a probabilistic proactive rebalancing method and speed-uptechniques for improving the performance of a state-of-the-art real-timehigh-capacity fleet management framework [1]. We improve on both computationalefficiency and system performance. The speed-up techniques include search-spacepruning and I/O cost reduction for parallelization, reducing the computationtime by up to 97.67%, in experiments on taxi trips in New York City. Theproactive rebalancing routes idle vehicles to future demands based onprobabilistic estimates from historical demand, increasing the service rate by4.8% on average, and decreasing the waiting time and total delay by 5.0% and10.7% on average, respectively.

Proactive rebalancing and speed-up techniques for on-demand high capacity ridesourcing services