Optimal channel management model for mixed traffic flow with automated and human-operated ships in seaports

Abstract

The coexistence of autonomous ships (ASs) and human-operated ships (HSs) will persist in seaport traffic systems for the foreseeable future. To improve traffic efficiency in such mixed environments, it is essential to make efficient use of limited channel resources. To address this issue, this paper develops an optimization model for channel management. Three channel navigation strategies are introduced: dedicated channels for ASs (DC-AS), dedicated channels for HSs (DC-HS), and mixed channels (MC). A macroscopic traffic capacity model of different channel navigation strategies is established by incorporating heterogeneous ship-following modes and ASs platooning characteristics. Based on this, an optimization model for channel management is formulated to maximize total channel traffic capacity under constraints of traffic demand, flow distribution balance, and fairness. Numerical experiments are conducted to verify the effectiveness of the model. Results indicate that: (1) penetration rate of ASs improves capacity, especially when a critical threshold is exceeded; (2) larger platoon sizes lead to higher capacity gains under congested conditions; (3) the optimal navigation strategy shifts from MC to DC-AS as ASs penetration rate increases. This study provides a theoretical framework for efficient channel management in future mixed maritime traffic scenarios.