# Improving Freight Traffic Efficiency at Urban Intersections Using Heavy Vehicle Platooning
###### Abstract  
The increasing presence of heavy connected vehicles (HCVs) in urban traffic necessitates optimized signal-control strategies to improve efficiency. This study develops a platoon-based signal-optimization algorithm to reduce delays, minimize stops, and enhance traffic flow at intersections. The algorithm collects real-time CV data (speed, position, and inter-vehicle distances) to identify platoons, then dynamically adjusts signal timings using platoon-prioritized signal control and advisory speed coordination to synchronize HCV arrivals with green intervals. The algorithm was tested using a VISSIM microscopic traffic-simulation model, calibrated with real-world traffic data from Tallahassee, Florida, under varying traffic-demand scenarios and connected vehicle penetration levels. Performance was evaluated based on average HCV delay and the total number of stops, comparing the platoon-based approach to actuated and vehicle-based signal-control methods. Results show a significant reduction in both delay and stops, with the greatest improvements observed under higher CV penetration and over-saturated conditions. These findings confirm the effectiveness of platoon-based optimization in improving intersection performance and overall traffic progression. Future research will focus on multi-intersection applications and V2I integration to further optimize signal-control strategies. os 2025 25 February 2025 26 February 2025 2  
control, can significantly improve traffic flow, reduce stops, and enhance fuel efficiency [7; 8]. The concept of HCV platooning is enabled by Cooperative Adaptive Cruise Control (CACC), which allows vehicles within a platoon to communicate and synchronize speed, acceleration, and braking [9]. Figure 1 illustrates the structure of an HCV platoon, where a leader vehicle determines movement dynamics while following vehicles respond accordingly. However, while platoon-based optimization has been explored for passenger vehicles, existing traffic signal-control frameworks rarely consider the unique operational constraints of heavy vehicles, such as increased braking distances, cargo priority, and intersection clearance requirements [10]. This study proposes a novel platoon-based signal-optimization algorithm tailored specifically for HCV operating in a CV-enabled environment. The algorithm integrates two synergistic control mechanisms:  
1. **Platoon-prioritized signal control**: This dynamically adjusts green phase durations based on real-time HCV platoon data, including size, approach speed, and location. 2. **Advisory speed coordination**: This guides platoon leaders to synchronize arrival times with green intervals, reducing stop frequency and smoothing acceleration and deceleration profiles. By leveraging these mechanisms, the algorithm aims to minimize HCV delays, reduce the number of stops, and enhance overall intersection efficiency while maintaining coordination with surrounding traffic. To validate the proposed approach, a case study was conducted at a major high-traffic intersection in Tallahassee, Florida (Orange Ave and Adams Street), a key corridor for logistics operations. Figure 2 presents an overview of the study site, highlighting its relevance for evaluating platoon-based signal optimization in real-world freight-movement scenarios. The VISSIM microscopic traffic-simulation model, calibrated to Florida Department of Transportation (FDOT) guidelines, was used to replicate real-world traffic conditions, incorporating distinct behavioral models for non-CVs, platoon leaders, and platoon followers [12]. The performance of the developed algorithm was compared against actuated signal control and individual vehicle-based optimization strategies across varying CV penetration levels. The key contributions of this study include:  
* A **platoon-centric signal-optimization model** specifically designed for HCVs, addressing a critical gap in passenger vehicle-dominated traffic-management approaches.