评估铁路枢纽基础设施:基于队列、与时刻表无关的分析

IF 7.6 1区 工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY
Tamme Emunds, Nils Nießen
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引用次数: 0

摘要

由于需求不断增长,许多基础设施管理者的目标是提高铁路基础设施的容量。虽然铁路线路基础设施的性能计算方法已经非常成熟,但铁路枢纽站容量的确定仍然是一项挑战。本研究利用排队理论的概念,开发了一种铁路枢纽容量计算方法,该方法完全取决于铁路枢纽的基础设施布局以及到达率和服务率。所引入方法的实施基于概率模型检查。该方法可用于决定建设哪种基础设施布局,即是否需要在所分析的铁路枢纽站修建立交桥。所开发的方法满足了对快速、可靠、与时间表无关的铁路枢纽容量评估的需求,特别适合枢纽基础设施的长期战略规划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating railway junction infrastructure: A queueing-based, timetable-independent analysis

Many infrastructure managers have the goal to increase the capacity of their railway infrastructure due to an increasing demand. While methods for performance calculations of railway line infrastructure are already well established, the determination of railway junction capacity remains a challenge. This work utilizes the concept of queueing theory to develop a method for the capacity calculation of railway junctions, solely depending on their infrastructure layout along with arrival and service rates. The implementation of the introduced approach is based on probabilistic model-checking. It can be used to decide which infrastructure layout to build, i.e. whether an overpass for the analysed railway junction is needed. The developed method addresses the need for fast and reliable timetable-independent railway junction capacity evaluation, catering specifically to the long-term strategic planning of junction infrastructure.

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来源期刊
CiteScore
15.80
自引率
12.00%
发文量
332
审稿时长
64 days
期刊介绍: Transportation Research: Part C (TR_C) is dedicated to showcasing high-quality, scholarly research that delves into the development, applications, and implications of transportation systems and emerging technologies. Our focus lies not solely on individual technologies, but rather on their broader implications for the planning, design, operation, control, maintenance, and rehabilitation of transportation systems, services, and components. In essence, the intellectual core of the journal revolves around the transportation aspect rather than the technology itself. We actively encourage the integration of quantitative methods from diverse fields such as operations research, control systems, complex networks, computer science, and artificial intelligence. Join us in exploring the intersection of transportation systems and emerging technologies to drive innovation and progress in the field.
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