  Connected Automated Vehicles (CAVs) bring promise of increasing traffic
capacity and energy efficiency by forming platoons with short headways on the
road. However at low CAV penetration, the capacity gain will be small because
the CAVs that randomly enter the road will be sparsely distributed, diminishing
the probability of forming long platoons. Many researchers propose to solve
this issue by platoon organization strategies, where the CAVs search for other
CAVs on the road and change lanes if necessary to form longer platoons.
However, the current literature does not analyze a potential risk of platoon
organization in disrupting the flow and reducing the capacity by inducing more
lane changes. In this research, we use driving model of Cooperative Adaptive
Cruise Control (CACC) vehicles and human-driven vehicles that are validated
with field experiments and find that platoon organization can indeed drop the
capacity with more lane changes. But when the traffic demand is well below
capacity, platoon organization forms longer CAV platoons without reducing the
flow. Based on this finding, we develop the Flow-Aware platoon organization
strategy, where the CAVs perform platoon organization conditionally on the
local traffic state, i.e., a low flow and a high speed. We simulate the
Flow-Aware platoon organization on a realistic freeway network and show that
the CAVs successfully form longer platoons, while ensuring a maximal traffic
flow.
