# Improving Freight Traffic Efficiency at Urban Intersections Using Heavy Vehicle Platooning
## 5 Conclusions
The algorithm's effectiveness under mixed traffic conditions, where only a fraction of HCVs are connected, needs further investigation. Furthermore, while computational efficiency was validated within the simulation framework, real-time implementation in large-scale networks may present additional processing constraints. Future studies should assess these challenges to ensure the practical feasibility of the proposed approach. To further enhance the applicability of the platoon-based signal-optimization approach, several future research directions are proposed. First, integrating artificial intelligence (AI) and machine learning techniques could improve the adaptability of the algorithm by enabling real-time predictive adjustments based on evolving traffic patterns. AI-driven models could also help refine platoon formation strategies, optimize gap acceptance, and improve decision-making in mixed traffic conditions. Second, real-world pilot studies should be conducted to validate the algorithm's effectiveness beyond simulation, assessing its performance in mixed traffic environments and varying infrastructure conditions. Third, expanding the optimization framework to multi-intersection networks would provide a more comprehensive traffic-management strategy, allowing coordinated platoon movements across multiple traffic signals. Additionally, sensitivity analysis should be conducted to examine the impact of different traffic and logistics parameters, such as varying vehicle loads, acceleration characteristics, and route scheduling, on the algorithm's performance. Lastly, the integration of the platoon-based approach with smart city infrastructures, including connected traffic-control centers and autonomous freight systems, could further enhance the efficiency and sustainability of urban transportation networks. This study presents a platoon-based signal-optimization algorithm that significantly reduces HCV delay and stops while improving computational efficiency. While real-world implementation faces challenges related to CV infrastructure, traffic heterogeneity, and system integration, the potential benefits for freight transportation and overall intersection performance are substantial. Future research should focus on AI-based optimizations, real-world pilot implementations, and multi-intersection scalability to ensure the broader applicability of the proposed approach. With continued advancements in connected vehicle technology, the integration of platoon-based signal control into urban traffic systems could play an important role in improving mobility and efficiency in the freight sector. Conceptualization, M.D.A. ; methodology, A.S.A. ; software, A.S.A. ; validation, A.S.A. ; formal analysis, M.D.A. ; investigation, M.D.A. ; resources, M.D.A. ; data curation, A.S.A. ; writing--original draft preparation, M.D.A. ; writing--review and editing, A.S.A. ; visualization, A.S.A. ; supervision, M.D.A. ; project administration, M.D.A. ; funding acquisition, M.D.A. All authors have read and agreed to the published version of the manuscript. **Funding:** The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number MoE-IF-UJ-R2-22-04100753-1. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request. **Conflicts of Interest:** The authors declare no conflicts of interest.