Local Rules, Global Efficiency: Emergent Coordination on Large-Scale Road Networks

Abstract

Urban transportation systems face a fundamental efficiency paradox: individually optimal routing decisions can create system-wide congestion that increases travel time for all participants. Traffic engineers address this through rigid infrastructure solutions—for example, roundabouts that enforce circular flow, or turn restrictions that prevent conflicting maneuvers. However, these manually designed interventions lack flexibility: optimized for peak demand, they unnecessarily constrain traffic during off-peak hours. Autonomous vehicles (AVs) offer a transformative opportunity for dynamic coordination that adapts to traffic conditions. However, achieving such adaptive coordination at city scale with thousands of decentralized vehicles remains an open challenge. We propose Guided Local Coordination (GLC), a decentralized framework combining global shortest-path guidance with priority-based local conflict resolution. GLC precomputes optimal distance labels for the entire network, then uses priority inheritance and backtracking to resolve real-time collisions without replanning. We demonstrate that GLC scales to city-scale networks with 4K nodes and 10K agents, achieving high performance. As a key qualitative result, GLC produces emergent roundabout-like behavior: under high density, left-turn traffic spontaneously forms circulating flows while straight-through traffic proceeds unimpeded—adaptive coordination arising purely from local rules without explicit programming.