主动再平衡与加速技术用于按需高容量出行服务
Proactive rebalancing and speed-up techniques for on-demand high capacity ridesourcing services
AI总结：
本文提出了一种概率主动再平衡方法和加速技术，用于提升实时高容量车队管理框架的性能，通过提高计算效率和系统性能，减少计算时间并提高服务效率。
摘要：
我们提出了一种概率主动再平衡方法和加速技术，用于改进最先进的实时高容量车队管理框架[1]。我们提高了计算效率和系统性能。加速技术包括搜索空间修剪和I/O成本降低以实现并行化，实验表明在纽约市出租车行程上将计算时间减少高达97.67%。主动再平衡根据历史需求的概率估计将空闲车辆路由至未来的请求，平均提高了服务速率4.8%，并分别将等待时间和总延迟减少了5.0%和10.7%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
