  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 [1]. 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%, 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% on average, and decreasing the waiting time and total delay by 5.0% and
10.7% on average, respectively.
