A hybrid traffic flow model for truck transportation management: CTM for whole-journey with CA at black-spots

The rapid growth of road freight has led to an increasing number of trucks, where their long braking distance, large mass, and limited maneuverability make them particularly vulnerable to safety risks, especially at high-risk nodes such as tunnels, bridges, and step grades. These risks are generated at the microscopic level but accumulate and propagate along the entire trip, creating the need for a modeling framework capable of capturing both local safety mechanisms and system-level traffic evolution. To address this need, this study proposes a multiscale hybrid traffic flow model that integrates an improved Cellular Automata (CA) model for detailed characterization of accident-prone segments and a Cell Transmission Model (CTM) for long-distance freight transport. Two transition areas are designed to ensure consistent information exchange between the discrete CA and continuous CTM domains. Using naturalistic driving data for calibration and validation, the hybrid model accurately reproduces truck-car interaction patterns and system-level traffic dynamics. The results show that vehicle heterogeneity and different vehicle combinations lead to distinct car-following behaviors and safety outcomes, as reflected in surrogate safety measures such as Time Headway and Time-to-Collision. Although trucks tend to adopt more cautious strategies, truck–truck interactions still exhibit the highest risk levels due to reduced maneuverability and accumulated disturbance. These findings provide actionable insights for freight operations and road safety management, supporting the development of targeted regulations and truck-specific safety strategies at high-risk locations.