Improving Truck Merging at Ramps in a Mixed Traffic Environment: A Multi-human-in-the-loop (MHuiL) Approach

Freeway ramp merging is a challenging task for an individual vehicle (in particular a truck) and a critical aspect of traffic management that often leads to bottlenecks and accidents. While connected and automated vehicle (CAV) technology has yielded efficient merging strategies, most of them overlook the differentiation of vehicle types and assume uniform CAV presence. To address this gap, our study focuses on enhancing the merging efficiency of heavy-duty trucks in mixed traffic environments. We introduce a novel multi-human-in-the-loop (MHuiL) simulation framework, integrating the SUMO traffic simulator with two game engine-based driving simulators, enabling the investigation of interactions between human drivers in diverse traffic scenarios. Through a comprehensive case study analyzing eight scenarios, we assess the performance of a connectivity-based cooperative ramp merging system for heavy-duty trucks, considering safety, comfort, and fuel consumption. Our results demonstrate that guided trucks exhibit advantageous characteristics, including an enhanced safety margin with larger gaps by 23.2%, a decreased speed deviation by 30.4% facilitating smoother speed patterns, and a reduction in fuel consumption by 3.4%, when compared with non-guided trucks. This research offers valuable insights for the development of innovative approaches to improve truck merging efficiency, enhancing overall traffic flow and safety.