Proactive rebalancing and speed-up techniques for on-demand high capacity ridesourcing services
For operators of high-capacity ridesourcing services, this work offers incremental improvements in computational efficiency and system performance.
This paper improves a real-time high-capacity fleet management framework by introducing a probabilistic proactive rebalancing method and speed-up techniques, reducing computation time by up to 97.67% and increasing service rate by 4.8% while decreasing waiting time and total delay by 5.0% and 10.7% respectively on New York City taxi data.
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.