As transportation network companies and automobile manufacturers continue to invest in the development of self-driving vehicles, it can be expected that autonomous taxi (a-taxi) fleets will become a major component of on-demand transport services in the foreseeable future. The majority of existing automated fleet management systems focus on central dispatch strategies that rely on real-time information and communication. This paper proposes a novel decentralised cooperative cruising method for offline operation of a-taxi fleets, which serves as a contingency plan during a full communication shutdown. The proposed method acts as an emergency plan for the system to continue serving passengers with the objective of maximising the total number of served passengers by the fleet. The method uses historical trip data to estimate PageRank centralities of roads as a proxy of long-term likelihood of finding waiting passengers over a series of trips. The proposed method uses this metric to (i) compute weighted shortest paths for vacant a-taxi cruising route planning, and (ii) partition the network into homogeneous regions for effective cruising destination choice (mission planning). The movements of vacant a-taxis between regions are modelled as a Markov chain such that a transition probability matrix is computed to achieve the optimal spatial distribution of vacant a-taxis to maximise the total expected pick-ups by the fleet, estimated based on bilateral meeting functions. Compared to benchmark strategies which select destinations randomly and cruise along the shortest travel time path, the proposed method shows significant improvements in service performances for different fleet sizes.

随着交通网络公司和汽车制造商不断投资开发自动驾驶汽车，可以预期，在可预见的未来，自动驾驶出租车（a-taxi）车队将成为按需运输服务的主要组成部分。大多数现有的自动化车队管理系统都专注于依赖实时信息和通信的中央调度策略。本文提出了一种新的去中心化合作巡航a-taxi 车队离线运行的方法，作为完全通信关闭期间的应急计划。所提出的方法作为系统继续为乘客提供服务的应急计划，目的是使车队服务的乘客总数最大化。该方法使用历史旅行数据来估计道路的 PageRank 中心性，作为在一系列旅行中找到等待乘客的长期可能性的代理。所提出的方法使用该度量来 (i) 计算用于空置出租车巡航路线规划的加权最短路径，以及 (ii) 将网络划分为同质区域以进行有效的巡航目的地选择（任务规划）。区域之间空出租车的运动被建模为马尔可夫链，以便计算转移概率矩阵以实现空出租车的最佳空间分布，以最大化车队的总预期接客量，基于双边估计会议功能。与随机选择目的地并沿最短旅行时间路径巡航的基准策略相比，所提出的方法显示出不同车队规模的服务性能显着提高。