Unequal Trips, Unequal Places: Diagnosing and Mitigating Delay Inequity in Autonomous Vehicle Fleet Coordination
2026-07-27 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors studied how self-driving car fleets handle trip delays differently across various city areas, finding that some trips experience more delay depending on trip length and location. They noticed that areas where trips start often face more unfair delays as demand grows. To address this, they created a method called SPARE that selectively reroutes vehicles experiencing delays based on current waiting conditions, limiting how much the system changes routes. Testing SPARE on data from three cities showed it improves both overall efficiency and fairness without needing to constantly replan all routes. This means better, fairer travel times with less computational effort.
autonomous vehicle fleettrip delay distributiontrip-length inequityspatial inequityreplanningwaiting pressureroute reroutingcity-scale scalabilityefficiency-fairness tradeoffonline coordination
Authors
Nicole Hu, Mingtao Zhang, Haoyang LI, Chen Jason Zhang, Li Qing
Abstract
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.