Designing, simulating, and performing the 100-AV field test for the CIRCLES consortium: Methodology and Implementation of the Largest mobile traffic control experiment to date

TL;DR

This work addresses scaling autonomous-vehicle (AV) traffic control from ring-road experiments to high-density highway conditions by presenting MegaVanderTest (MVT), a large-field deployment of 100 AVs near Nashville. It introduces a bi-level calibration framework that integrates a SUMO-based agent-based microsimulation with data from TDOT and INRIX, minimizing $MSE$ between simulated and observed flows and speeds, and uses SPSA-assisted optimization to align departure times and routes. Validation on a six-mile segment demonstrates that the framework, coupled with实时 feedback between the optimizer and simulator, can reproduce stop-and-go dynamics and support safe, effective AV routing (orange and yellow) for congestion mitigation. The study provides a practical blueprint for planning, simulating, and executing large-scale live traffic-control experiments, with implications for traffic efficiency, energy use, and future field deployments, while outlining data-quality limitations and avenues for refinement using lane-specific MOTION data.

Abstract

Previous controlled experiments on single-lane ring roads have shown that a single partially autonomous vehicle (AV) can effectively mitigate traffic waves. This naturally prompts the question of how these findings can be generalized to field operational, high-density traffic conditions. To address this question, the Congestion Impacts Reduction via CAV-in-the-loop Lagrangian Energy Smoothing (CIRCLES) Consortium conducted MegaVanderTest (MVT), a live traffic control experiment involving 100 vehicles near Nashville, TN, USA. This article is a tutorial for developing analytical and simulation-based tools essential for designing and executing a live traffic control experiment like the MVT. It presents an overview of the proposed roadmap and various procedures used in designing, monitoring, and conducting the MVT, which is the largest mobile traffic control experiment at the time. The design process is aimed at evaluating the impact of the CIRCLES AVs on surrounding traffic. The article discusses the agent-based traffic simulation framework created for this evaluation. A novel methodological framework is introduced to calibrate this microsimulation, aiming to accurately capture traffic dynamics and assess the impact of adding 100 vehicles to existing traffic. The calibration model's effectiveness is verified using data from a six-mile section of Nashville's I-24 highway. The results indicate that the proposed model establishes an effective feedback loop between the optimizer and the simulator, thereby calibrating flow and speed with different spatiotemporal characteristics to minimize the error between simulated and real-world data. Finally, We simulate AVs in multiple scenarios to assess their effect on traffic congestion. This evaluation validates the AV routes, thereby contributing to the execution of a safe and successful live traffic control experiment via AVs.

Figures (13)

• Figure 1: The CIRCLES Consortium at the experiment headquarters with partners listed.
• Figure 2: I-24 road network: The MVT experiment testbed shown with © Google maps and SUMO .
• Figure 3: The I-24 MOTION system gloudemans202324gloudemans2020interstate comprises 276 cameras mounted on 40 poles ranging from 110 ft to 135 ft above the freeway along a 4.2 mile stretch of Interstate 24, southeast of Nashville, Tennessee.
• Figure 4: Road map showing the planning and execution sequence of project milestones.
• Figure 5: Throughput data for July 2021. The pairs of numbers indicate how many vehicles drove through the section of road indicated by the green line during peak congestion. The number on the left shows the count of vehicles between 6:30am to 7:30am, and the number on the right shows the count between 7:30am and 8:30am.