Connectedness Efficiency Analysis of Weighted U. S. Freight Railroad Networks

Majed Hamed, Yujie Mao, B. Ayyub, Magdy Elsibaie, Tarek Omar
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引用次数: 3

Abstract

Freight rail networks serve a key role in transporting bulk goods to accommodate changing market demands and to serve public needs. Network analyses of such systems can provide important insights into enhancing transportation efficiency and system resilience. This paper develops and investigates a topological analysis model for network efficiency, which is associated with the connectedness of a network's nodes by its links and their corresponding network attributes. This model allows analyzing network topologies with or without assigned weights to their nodes and links based on different attributes. Key attributes include physical length of links, dwell-time at nodes, types of goods moved, and origins and destination of goods. The model presented here enables (1) defining distinctions that may be employed for the assignment of node and link weights, (2) gaining an understanding of node and link criticality, and (3) providing methods for objectively maintaining and enhancing network performance. Such analyses can inform rail managers and executives in planning expansions, route or freight changes, or preparations for potential node or link failures. A case study of an aggregated U.S. freight rail network along with other example topologies is presented to demonstrate the use of selected network attributes and their influence on connectedness efficiency and the impacts of node and link failures on the overall transport efficiency.
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加权美国货运铁路网连通性效率分析
货运铁路网络在运输散装货物以适应不断变化的市场需求和满足公众需要方面发挥着关键作用。对此类系统的网络分析可以为提高运输效率和系统弹性提供重要见解。本文建立并研究了一个网络效率的拓扑分析模型,该模型与网络节点的连通性及其相应的网络属性有关。该模型允许对网络拓扑进行分析,并根据不同的属性为节点和链路分配权重。关键属性包括链接的物理长度、在节点上的停留时间、移动的货物类型以及货物的起源和目的地。本文提出的模型能够(1)定义可用于分配节点和链路权重的区别,(2)获得对节点和链路临界性的理解,以及(3)提供客观维护和增强网络性能的方法。这种分析可以为铁路管理人员和高管规划扩建、路线或货运变化,或为潜在的节点或链路故障做准备提供信息。本文提出了一个美国货运铁路网络的案例研究以及其他拓扑示例,以展示所选网络属性的使用及其对连通性效率的影响,以及节点和链路故障对整体运输效率的影响。
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CiteScore
5.20
自引率
13.60%
发文量
34
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