# Improving Freight Traffic Efficiency at Urban Intersections Using Heavy Vehicle Platooning
## 2 Related Work  
Connected Vehicle (CV) technology, coupled with traffic signal-control algorithms, has garnered increasing attention as researchers seek to optimize traffic flow and reduce logistic delays at intersections [13, 14]. Many recent studies leverage real-time vehicle data--such as speed, location, and acceleration/deceleration--to inform signal timing decisions, leading to reduced queue lengths and delays [15, 16, 17, 18, 19]. While these CV-based methods often outperform traditional control strategies, they frequently assume high or even full CV penetration rates [20], an assumption that is unlikely to be realized in the near term [21]. Simultaneously, research on intersection logistics highlights the unique challenges faced by heavy vehicles (e.g., large freight carriers), which account for a substantial share of urban goods movement yet experience disproportionately longer idling times due to slower acceleration, larger spatial footprints, and more frequent stop-and-go cycles [22]. These delays inflate operational costs and increase environmental impacts, underscoring a clear need for tailored traffic signal strategies that accommodate the specific demands of freight vehicles [23]. An emerging area that promises to address both CV-based control and heavy vehicle management is platooning, wherein connected heavy vehicles follow a designated leader at coordinated speeds [24]. Studies indicate that platooning can streamline traffic flow, reduce fuel consumption, and cut emissions [25]. However, many existing approaches to CV platooning and adaptive signal control either assume homogeneous traffic (mostly passenger vehicles) or 100% CV penetration [14, 26], limiting their applicability in mixed-fleet conditions where both connected and non-connected vehicles coexist. Moreover, few  
Figure 2: Satellite image of the selected intersection used by logistics drivers in the city. of these studies consider heavy vehicle-specific factors such as platoon size, inter-vehicle spacing, cargo priority, or their impact on signal timing optimization in oversaturated intersections [27]. Other research strands--such as reservation-based strategies for intersections--show potential in reducing vehicle delays by allowing vehicles to reserve time blocks at a signal [28,29]. While these approaches may theoretically improve intersection performance, concerns about safety, compatibility with existing infrastructure, and partial penetration of CV technology persist [30]. Furthermore, a number of studies propose CV-based algorithms that optimize individual vehicle trajectories for reducing delay or fuel consumption [31,32], but these algorithms can become computationally intensive, hindering their real-world deployment [33]. Adjusting signal timings in a connected vehicle (CV) environment can unintentionally disrupt traffic dynamics and vehicle performance, despite the goal of improving flow and reducing delays [34,35]. Sudden changes in signal phases may force connected vehicles to perform more acceleration and deceleration maneuvers, creating shock waves in traffic flow that increase idling, fuel consumption, and emissions, ultimately reducing the expected efficiency of coordinated vehicle movements [36]. Simulation-based approaches and spatiotemporal models for delay analysis have proven valuable in modeling intersection performance [37], yet they frequently overlook how CV technology and platooning specifically transform traffic dynamics [38]. Overall, existing literature underscores the promise of CV-enabled traffic control for mitigating intersection delay but reveals several critical gaps. Namely, heavy connected vehicles require specialized strategies that account for their distinct operational profiles and that function effectively under partial CV environments. Addressing these issues is key to unlocking substantial improvements in freight efficiency and environmental performance.