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.

模型基于大样本历史数据，采用上下文感知并综合多个模块进行协同工作，旨在预测和控制货车到达终端大门的时间，以提高物流效率和交通流畅。

一个基于数据驱动和多智能体的集装箱码头时段管理决策支持系统：以鹿特丹港为案例研究

A Data-driven and multi-agent decision support system for time slot  management at container terminals: A case study for the Port of Rotterdam

The development of automated vehicles has the potential to revolutionize
transportation, but they are currently unable to ensure a safe and
time-efficient driving style. Reliable models predicting human behavior are
essential for overcoming this issue. While data-driven models are commonly used
to this end, they can be vulnerable in safety-critical edge cases. This has led
to an interest in models incorporating cognitive theory, but as such models are
commonly developed for explanatory purposes, this approach's effectiveness in
behavior prediction has remained largely untested so far. In this article, we
investigate the usefulness of the \emph{Commotions} model -- a novel
cognitively plausible model incorporating the latest theories of human
perception, decision-making, and motor control -- for predicting human behavior
in gap acceptance scenarios, which entail many important traffic interactions
such as lane changes and intersections. We show that this model can compete
with or even outperform well-established data-driven prediction models across
several naturalistic datasets. These results demonstrate the promise of
incorporating cognitive theory in behavior prediction models for automated
vehicles.

这篇文章研究了采用包含最新的人类感知、决策 - making 和运动控制理论的新颖认知似然模型 --Commotions 模型用于预测人类在交通互动中的行为，展示了此模型在自然情景数据集中与数据驱动模型相竞争甚至胜出，证明了吸纳认知理论在自动驾驶行为预测模型中的巨大潜力。