Unequal Trips, Unequal Places: Diagnosing and Mitigating Delay Inequity in Autonomous Vehicle Fleet Coordination

2026-07-27

AI Summary

This study addresses the overlooked issue of inequitable delays in city-scale autonomous vehicle fleet dispatching, which typically optimizes for minimal total travel time while neglecting disparities arising from trip length and spatial distribution. To remedy this, the authors introduce SPARE—a budget-constrained, spatially aware online rerouting framework that dynamically adjusts vehicle routes based on real-time waiting pressure and provides provable performance guarantees under evolving boundary constraints. Evaluated on real-world datasets from Manhattan, Chicago, and San Francisco, SPARE consistently outperforms six baseline methods, achieving a superior trade-off between scheduling efficiency and fairness without compromising urban-scale scalability.

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.

Problem

• delay inequity
• autonomous vehicle fleet coordination
• spatial fairness
• trip-length bias
• urban mobility

Innovation

• autonomous vehicle fleet coordination
• delay inequity
• spatial fairness
• budgeted rerouting
• congestion-responsive routing