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An analytical capacity model for mixed traffic flow on freeways considering truck infiltration under connected automated vehicle environments
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DOI：10.1016/j.physa.2026.131796.png)
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Abstract
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As connected automated vehicle (CAV) technologies continue to develop rapidly, highways will remain in a mixed-traffic state where human-driven vehicles and CAVs coexist for a long time. Meanwhile, freight trucks account for a non-negligible proportion of highway traffic. Focusing on basic freeway segments, this paper proposes an analytical capacity model for mixed traffic flow that includes four vehicle classes. These classes are human-driven cars (HDCs), human-driven trucks (HDTs), connected automated cars (CACs), and connected automated trucks (CATs). Three key parameters are introduced: the car proportion α, the CAV penetration rate among cars β, and the CAV penetration rate among trucks γ. Based on these parameters, the proportions of each vehicle class in the mixed traffic are derived. Under the assumption of random mixing, eight car-following types are defined. Using the corresponding headway parameters, a weighted average headway is derived, and an analytical expression for roadway capacity is established. Systematic numerical experiments are conducted to examine how vehicle-class penetration rates, the truck proportion, and key headway parameters affect capacity. The results show that increasing the CAV penetration rate can significantly improve capacity. In particular, introducing CAT yields more pronounced capacity gains when the truck proportion is high. This study provides a theoretical basis for infrastructure planning and traffic management under mixed traffic conditions. The proposed model is intended for basic freeway segments under random vehicle mixing conditions.
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