提出公平性优化的自动驾驶车队协调方法，缓解行程延迟不均问题。
Unequal Trips, Unequal Places: Diagnosing and Mitigating Delay Inequity in Autonomous Vehicle Fleet Coordination
- 基于实时等待压力，动态重规划延迟车辆路径。
- 在三座城市数据集上，显著降低行程与区域延迟不公。
- 适合关注交通公平与可扩展调度的研究者与工程师。
城市级自动驾驶车队协调器通常以总出行时间最优为目标，但平均值掩盖了行程与区域间的延迟分布差异。我们在曼哈顿、芝加哥和旧金山三个真实城市的道路网络与出租车需求数据集上开展分布审计，发现普遍存在的行程长度不公，其方向随城市和协调策略而异。在考虑行程长度后，随着需求增长，空间不公更加显著，且按出发地分组时比按目的地分组时更严重。为此提出SPARE（SPatially Aware RErouting）框架，一种预算受限的在线协调机制，将有限重规划能力分配给延迟车辆，并利用近期观测到的等待压力进行路径调整。SPARE提供每轮决策保证并显式限制在线路径更新。在三个数据集上对六种基准方法的实验表明，SPARE在效率与公平性的联合表现上最优，同时保持城市规模可扩展性。结果证明，有界拥堵响应重规划可在无需全车队重规划的前提下提升性能与公平性。
原文摘要 · Abstract (English)
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.
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