发表机构
The Hong Kong Polytechnic University（香港理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究城市自动驾驶车队协调中延误不平等问题，提出空间感知重路由框架SPARE，通过分配有限重新规划能力及利用等待压力重路由，实验表明其在保持可扩展性时能提升效率与公平性，改善性能和公平性。
AI 中文摘要
城市规模的自动驾驶车队协调器通常针对总出行时间进行优化，但车队平均数据掩盖了延误在行程和区域间的分布情况。我们对来自曼哈顿、芝加哥和旧金山的三个真实城市道路网络及出租车需求数据集进行了分布审计。审计揭示了普遍存在的行程长度不平等，其方向取决于城市和协调器。考虑行程长度后，随着需求增长，空间不平等变得更加明显，按起点而非终点分组行程时，这种不平等一直更强。这些发现促使我们提出了空间感知重路由（SPARE），这是一个有预算的在线协调框架，为延误车辆分配有限的重新规划能力，并利用最近观察到的等待压力对其进行重新路由。SPARE提供每次审查的决策保证，并明确限制在线路线更新。针对六个代表性基线在所有三个数据集上进行的实验表明，SPARE在保持城市规模可扩展性的同时，提供了最强的联合效率-公平性能。结果表明，有界的拥堵响应式重路由在不进行全车队重新规划的情况下提高了性能和公平性。
英文摘要
City-scale autonomous vehicle fleet coordinators are typically optimized for aggregate travel time, yet fleet averages conceal how delay is distributed across trips and regions. We conduct a distributional audit on three real-city road-network and taxi-demand datasets from Manhattan, Chicago, and San Francisco. The audit reveals pervasive trip-length inequity whose direction depends on the city and coordinator. After accounting for trip length, spatial inequity becomes more pronounced as demand grows and is consistently stronger when trips are grouped by origin rather than destination. These findings motivate SPatially Aware RErouting (SPARE), a budgeted online coordination framework that assigns limited replanning capacity to delayed vehicles and redirects them using recently observed waiting pressure. SPARE provides a per-review decision guarantee and explicitly bounds online route updates. Experiments on all three datasets against six representative baselines show that SPARE delivers the strongest joint efficiency-fairness performance while retaining city-scale scalability. The results demonstrate that bounded congestion-responsive rerouting improves performance and equity without full-fleet replanning.
