CPS: Synergy: Cyber Physical Regional Freight Transportation System Start

该项目的目标是开发优化和控制技术，并将其与实时仿真模型相结合，以实现复杂网络中的负载平衡。 应用案例是区域货运系统。 铁路和公路网络上的货物运输也由乘客分担。 这些网络今天独立工作，即使它们高度相互依赖，其结果是以拥堵，污染和过度燃料消耗的形式存在的效率低下。 这些低效率可以通过例如跨越时间和空间的需求高峰来观察。 效率低下的部分原因是缺乏信息和适当的工具，部分原因是缺乏能够促进更加一体化的行动的政策和体制结构。该问题变得更加复杂，由于大量的真实的时间数据，将可用于通知decision-making.This研究开发的理论基础，一种新的方法称为COSMO跨复杂的动态网络的时间和空间特性的负载平衡。与使用简单的数学模型来预测网络状态的当前实践相比，该方法采用计算仿真模型，通过考虑动态和复杂的相互作用，计算仿真模型在估计网络状态方面要准确得多。 该项目开发了网络物理系统的优化和负载平衡控制部分，并将其与以货运为驱动应用领域的真实的网络仿真模型相结合。 该研究还探讨了如何确定的障碍和政策问题/激励措施可以被纳入优化的动态货运负载平衡系统的数学约束和/或控制变量。支持分析的数据可能包括货运特征、交通、天气和其他大数据量。 该项目将利用洛杉矶/长滩地区港口的真实的时间数据来验证该方法。 洛杉矶/长滩港是进入西海岸的大部分货物的入境港，并提供了丰富的数据集，这些数据集将刺激模型，特别是在涉及公路/铁路/港口网络之间相互作用的区域运输中。 这一重要交通领域的基础技术可以直接应用于其他大型货运中心，也可以应用于其他应用领域，包括网络和智能电网。除了通过更有效地分配运输资源产生更广泛的影响外，该项目还提供教育宣传和制作课程单元。

The objective of this project is to develop optimization and control techniques and integrate them with real-time simulation models to achieve load balancing in complex networks. The application case is the regional freight system. Freight moves on rail and road networks which are also shared by passengers. These networks today work independently, even though they are highly interdependent, and the result is inefficiencies in the form of congestion, pollution, and excess fuel consumption. These inefficiencies are observed for example by the peaks of demand across time and space. Inefficiencies exist in part due to lack of information and appropriate tools, and in part due to lack of policies and institutional structures that would promote more integrated operations. The problem is made even more complex due to the large quantities of real time data that will be available to inform the decision-making.This research develops the theoretical foundations of a new approach referred to as COSMO to balance loads across complex dynamical networks with temporal and spacial characteristics. In contrast to current practices where simple mathematical models are used to predict the states of the network the method employs computational simulation models that are far more accurate in estimating the states of the network by taking into account dynamics and complex interactions. The project develops the optimization and load balancing control segments of the cyber physical system and integrate them with real time network simulation models using freight transportation as the driving application area. The research also examines how identified barriers and policy issues/incentives can be incorporated as mathematical constraints and/or control variables in the optimized dynamic freight load balancing system. Data supporting the analysis may include freight characterization, traffic, weather, and other large data volumes. The project will utilize real time data from the port of Los Angeles /Long Beach area to validate the approach. The port of Los Angeles/Long Beach is the port of entry for much of the freight that enters the West Coast, and provides rich sets of data that will stimulate the model especially in regional transportation involving interaction between road/rail/port networks. This fundamental technology in this important transportation domain with direct applications to other large scale freight centers, can be applied to other application domains including networking and smart grid. Besides the broader impact derived from more efficient allocation of transportation resources, the project also provide educational outreach and produce course modules.