# Improving Freight Traffic Efficiency at Urban Intersections Using Heavy Vehicle Platooning
## 5 Conclusions  
This study introduced a platoon-based signal-optimization algorithm designed for heavy connected vehicles (HCVs) in connected vehicle (CV) environments. The algorithm prioritizes platoon movements by dynamically adjusting signal timings and providing advisory speed guidance, aiming to minimize HCV delays and the number of stops. A comprehensive evaluation was conducted using a microscopic traffic-simulation model, comparing the performance of the proposed approach against conventional actuated signal control and vehicle-based optimization strategies. The results demonstrated that the platoon-based algorithm significantly enhances intersection performance. Under under-saturated demand conditions, HCV delay was reduced by 50% compared to actuated signal control and by 30% compared to the vehicle-based method. Similarly, the reduction in the number of stops reached 55% relative to actuated signal control and 40% compared to the vehicle-based method. Under over-saturated demand conditions, delay reductions of 30% and 18% were observed compared to actuated and vehicle-based control, respectively, while the number of stops was reduced by 35% and 17%. These improvements indicate that the platoon-based approach effectively reduces traffic disruptions, smoothens vehicle flow, and enhances intersection efficiency. Additionally, the algorithm demonstrated superior computational efficiency, as it maintained stable processing times even with increasing traffic volumes, making it a scalable solution for real-time traffic management. Beyond traffic efficiency, the proposed algorithm also presents potential environmental and economic benefits. By minimizing stop-and-go movements and optimizing signal timings, the algorithm reduces fuel consumption, leading to lower greenhouse gas (GHG) emissions. Over time, widespread adoption of such an approach in freight corridors could contribute to significant reductions in transportation-related carbon footprints. From an economic standpoint, logistics companies could benefit from reduced fuel costs, improved delivery reliability, and lower vehicle maintenance expenses due to decreased braking and acceleration cycles. Future research should focus on quantifying these benefits using empirical fuel-consumption and emissions data to further validate the long-term sustainability of platoon-based signal control. The implementation of the proposed algorithm in urban traffic systems requires a robust CV infrastructure, including vehicle-to-infrastructure (V2I) communication and widespread HCV adoption. While the algorithm has shown promising results in simulation, real-world deployment would need to address several challenges. One major challenge is the mixed traffic environment, where not all vehicles are equipped with CV technology. The effectiveness of the platoon-based approach may be limited when CV penetration rates are low, requiring adaptive strategies to handle conventional vehicles. Additionally, integration with existing traffic-management systems must consider factors such as pedestrian crossings, emergency vehicle prioritization, and multi-modal transportation networks. Despite these challenges, the potential benefits include reduced congestion, improved freight mobility, and enhanced traffic flow efficiency, making the algorithm a valuable component in future smart city transportation systems. Although the study provides strong evidence supporting the effectiveness of the proposed algorithm, certain limitations should be acknowledged. First, the results are based on simulation data, which inherently involves assumptions about traffic conditions, vehicle behavior, and infrastructure capabilities. Real-world traffic dynamics, including unpredictable driving behavior and external disruptions, could influence the performance of the algorithm. Additionally, the study assumes relatively high CV penetration rates, which may not yet be feasible in all regions.