# Improving Freight Traffic Efficiency at Urban Intersections Using Heavy Vehicle Platooning
## 3 Methodology  
The methodology employed in this study aims to assess the performance of the HCV platoon-based adaptive signal-control algorithm in a real-world setting using actual logistics traffic data collected from a complex intersection in Tallahassee City. The algorithm's primary objective is to provide dual levels of signal-optimization control by integrating departure sequence optimization and advisory speed coordination specifically for HCV platoons. Unlike conventional signal-control strategies that manage individual vehicles separately, this approach leverages real-time connectivity and vehicle-to-infrastructure (V2I) communication among heavy connected vehicles to minimize intersection delays and stops. The HCV platoon-based adaptive signal-control algorithm presents several advantages over traditional vehicle-based optimization methods. First, the algorithm uses HCV-specific connectivity data to identify platoons of arriving or stopped heavy vehicles and optimizes the signal timing plan based on platoon size, location, and approach speed. This allows for efficient green phase allocation, reducing computational complexity and enabling deployment in realistic, high-volume intersections with complex logistics traffic flows. Second, the algorithm incorporates an advisory speed-coordination strategy, minimizing the number of stops for HCV platoons while considering upstream traffic flow. By synchronizing platoon arrivals with green intervals, fuel consumption, idling time, and emissions are significantly reduced. Third, the developed algorithm provides advisory speed guidance exclusively to the lead HCV in each platoon, which then communicates with following vehicles using Cooperative Adaptive Cruise Control (CACC). This reduces computational overhead, as only the platoon leader processes speed guidance while ensuring that all platoon members follow a coordinated trajectory through the intersection. Lastly, the algorithm integrates a modified Intelligent Driving Model (IDM), specifically adapted to HCV dynamics, considering slower acceleration rates, longer braking distances, and increased intersection clearance times. These adjustments ensure a realistic representation of HCV movement and enhance the accuracy of advisory speed recommendations. Figure 3 illustrates the logic of the HCV platoon-based adaptive signal-control algorithm, highlighting the various components involved in optimizing signal timings for mixed traffic environments, where HCVs interact with non-CVs and smaller vehicles. The control algorithm operates as a discrete loop, continuously updating itself at each time step based on real-time traffic conditions. The first step in the algorithm involves HCV platoon identification, where both stopped and moving platoons are detected. Stopped platoons are identified based on queue lengths and headway analysis, ensuring accurate detection of waiting HCV groups. Moving platoons are recognized using real-time vehicle trajectory data, analyzing speed differentials and inter-vehicle distances. Additionally, the algorithm estimates the presence of non-CVs (unequipped heavy vehicles) and incorporates them into signal-control decisions to maintain operational realism. The second step selects the optimal signal phase based on HCV platoon characteristics, including location, speed, number of vehicles, and lane assignments. Given that the intersection features shared through-right lanes and exclusive left-turn lanes, the algorithm dynamically adjusts green times to prioritize HCV platoon movements while maintaining balanced intersection operations for all users. The objective of this step is to minimize the overall average delay for HCVs and improve their throughput at the intersection. Figure 3: The developed HCV platoon-based algorithm flowchart. The third step determines the advisory speed for each departure sequence, minimizing the number of stops and ensuring smoother traffic flow through the intersection.