  In this report, we delve into two critical research inquiries. Firstly, we
explore the extent to which Reinforcement Learning (RL) agents exhibit
multimodal distributions in the context of stop-and-go traffic scenarios.
Secondly, we investigate how RL-controlled Robot Vehicles (RVs) effectively
navigate their direction and coordinate with other vehicles in complex traffic
environments. Our analysis encompasses an examination of multimodality within
queue length, outflow, and platoon size distributions for both Robot and
Human-driven Vehicles (HVs). Additionally, we assess the Pearson coefficient
correlation, shedding light on relationships between queue length and outflow,
considering both identical and differing travel directions. Furthermore, we
delve into causal inference models, shedding light on the factors influencing
queue length across scenarios involving varying travel directions. Through
these investigations, this report contributes valuable insights into the
behaviors of mixed traffic (RVs and HVs) in traffic management and
coordination.
