UrbanMix: LLM-Guided Simulation of Mixed Autonomy Traffic with Heterogeneous Behavioral Profiles

摘要

Cities deploying autonomous vehicles face an urgent policy question: would the adoption of autonomous vehicles (AVs) improve the congestion rate or worsen it? What would be the optimal adoption rate to minimize the congestion rate? How would cautious AVs (Waymo-style) and aggressive AVs (Tesla "Mad Max'"-style) interact with human drivers and delivery robots on shared roads? We present UrbanMix, an interactive simulation platform that embeds cognitively diverse agents (human drivers, cautious AVs, aggressive AVs, and delivery robots) with distinct behavioral profiles inside a Simulation of Urban MObility (SUMO) framework of real urban road networks. Our LLM planner operates as an urban policy coordinator, setting traffic rules through a bounded action interface, while a regulation shield enforces infrastructure constraints. Our experiments reveal three key phenomena: (i) roads throughput may substantially drop with the increase in AVs adoption; (ii) an aggressive cascade, where runtime behavior switching modeling Tesla user-selectable "Mad Max" mode triggers up to 60 times increase in emergency braking events on a real Austin Downtown network; and (iii) a delivery bottleneck, showing 24-36% throughput reduction from slow robots. Results validated on synthetic and real data from Austin, TX, demonstrate that real network topology amplifies cascade effects by more than four times.