Controlling the departure time of the trucks from a container hub is important to both the traffic and the logistics systems. This, however, requires an intelligent decision support system that can control and manage truck arrival times at terminal gates. This paper introduces an integrated model that can be used to understand, predict, and control logistics and traffic interactions in the port-hinterland ecosystem. This approach is context-aware and makes use of big historical data to predict system states and apply control policies accordingly, on truck inflow and outflow. The control policies ensure multiple stakeholders satisfaction including those of trucking companies, terminal operators, and road traffic agencies. The proposed method consists of five integrated modules orchestrated to systematically steer truckers toward choosing those time slots that are expected to result in lower gate waiting times and more cost-effective schedules. The simulation is supported by real-world data and shows that significant gains can be obtained in the system.

翻译：控制集装箱枢纽中卡车的离港时间对于交通和物流系统均至关重要。然而，这需要一套能够管理与控制卡车抵达码头闸口时间的智能决策支持系统。本文提出了一种集成模型，可用于理解、预测并管控港口-腹地生态系统中的物流与交通交互行为。该方法具有情境感知能力，利用海量历史数据预测系统状态，并据此对卡车流入与流出施加控制策略。这些控制策略确保了多方利益相关者的满意度，包括卡车运输公司、码头运营商及道路交通管理机构。所提出的方法由五个集成模块组成，通过协同调度系统性地引导卡车司机选择更有可能降低闸口等待时间并实现更具成本效益的日程安排的时间段。仿真基于真实数据开展，结果表明该系统可显著提升系统效能。