# Improving Freight Traffic Efficiency at Urban Intersections Using Heavy Vehicle Platooning
## 4 Results
### Real-World Applicability, Challenges, and Future Research Directions  
The effectiveness of the platoon-based signal-optimization algorithm in simulation suggests its potential for real-world implementation. However, several challenges must be addressed to ensure its practical deployment, while further research is needed to enhance its adaptability to various traffic and logistical conditions.  
One of the key challenges in real-world implementation is the dependency on a well-established CV infrastructure, including vehicle-to-infrastructure (V2I) communication and reliable data-collection systems. The effectiveness of the platoon-based approach relies on a sufficient number of CHVs forming and maintaining platoons. In scenarios with low CV penetration rates, the benefits of the algorithm may be diminished, necessitating strategies to handle mixed traffic conditions. Additionally, variations in driver compliance and platoon stability due to unexpected traffic disruptions must be considered.  
Integrating the algorithm with existing traffic-management systems presents another challenge. While the proposed approach optimizes signal timings dynamically, real-world constraints such as emergency vehicle prioritization, pedestrian crossings, and mixed traffic conditions introduce complexities that must be accounted for in future research. Moreover, computational efficiency, which has been demonstrated in simulation, must be validated under real-time traffic conditions to ensure the algorithm can function effectively within urban signal-control frameworks.  
To improve the robustness and adaptability of the platoon-based approach, future research should explore the impact of different logistics and HV parameters on algorithm performance. Factors such as vehicle load, acceleration and deceleration characteristics, and route scheduling could influence platoon behavior and signal timing efficiency. Incorporating these parameters into the optimization process could lead to further refinements and increased applicability across diverse freight corridors.  
Sensitivity analysis should also be conducted to assess how variations in key parameters, such as CV penetration rate, traffic volume, platoon size, and intersection configurations, influence the algorithm's performance. Understanding these sensitivities will help refine the model to ensure it remains effective under a wide range of operating conditions.  
Additionally, integrating artificial intelligence (AI) and machine learning techniques could enhance the adaptability of the algorithm by predicting traffic-demand fluctuations and adjusting signal timings dynamically. AI-driven models could also help refine platoon formation strategies, optimize gap acceptance, and improve decision-making in mixed  
Figure 9: Comparison of computational efficiency between the vehicle-based and platoon-based algorithms.  
traffic conditions. Further research could explore how AI-based approaches compare to rule-based optimization methods in different urban settings.  
Extending the optimization to multi-intersection networks, rather than a single isolated intersection, would also provide a more comprehensive traffic-management strategy for urban environments. Additionally, evaluating the algorithm's performance under adverse weather conditions, such as heavy rain or fog, would be essential to ensure its reliability in real-world deployments.  
Finally, integrating the developed algorithm with existing traffic-management platforms and conducting real-world pilot studies will be crucial steps in validating its effectiveness beyond simulations. Collaboration with transportation agencies and logistics companies could facilitate testing in operational environments, helping bridge the gap between simulation-based research and large-scale implementation.  
These research directions will contribute to refining the platoon-based signal-optimization approach, making it a more adaptable and scalable solution for improving intersection performance in connected vehicle environments.