  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.
