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We present a probabilistic proactive rebalancing method and speed-up techniques for improving the performance of a state-of-the-art real-time high-capacity fleet management framework. We improve on both computational efficiency and system performance. The speed-up techniques include search-space pruning and I/O cost reduction for parallelization, reducing the computation time by up to 97.67&#x0025;, in experiments on taxi trips in New York City. The proactive rebalancing routes idle vehicles to future demands based on probabilistic estimates from historical demand, increasing the service rate by 4.8&#x0025; on average, and decreasing the waiting time and total delay by 5.0&#x0025; and 10.7&#x0025; on average, respectively.

Proactive Rebalancing and Speed-Up Techniques for On-Demand High Capacity Ridesourcing Services