ESS Collaborative Research: A Methodology for Real-Time Train Dispatching in High Density Networks

ESS 协作研究：高密度网络中实时列车调度的方法

• 批准号: 0223479
• 负责人: Maged Dessouky
• 金额: $ 9万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-01-01 至 2005-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0223479&HistoricalAwards=false
• 关键词: ESS; Collaborative; Research; Methodology; Real

This research addresses real-time dispatching of heavy-haul trains in a high-density complex railway network with multiple-track configurations, multiple priorities and multiple speed limits. The research will provide the theory and dynamic control algorithms for deadlock-free dispatching with minimal total train delay in complex heavy-haul rail networks that are increasingly common in urban areas. Determining the optimal dispatch policies that minimizes train delays and ensures deadlock-free operations is NP-hard. Therefore, most of the research efforts in this direction have focused on developing heuristics as well as detailed simulations to plan dispatching policies. Theoretical efforts thus far have only addressed trackage configurations like single, double, and partially double rail lines. However complicating entities in a rail system like junctions, crossovers, sidings, speed limits and goods priorities need to be considered in order to extend the current theory to large-scale urban rail networks. To address this need, our research will consist of the following tasks: (1) theoretical studies focusing on developing a network representation of this complicated physical system, and characterization of the nonlinear and stochastic dynamics underlying the operations for laying the foundations of developing effective dispatching algorithms, (2) development of deadlock-free and near-optimal algorithms for the real-time dispatching of trains operating in complex general rail networks, and (3) development of a simulation methodology to model complicated rail networks akin to Southern California rail networks, and validate the performance of algorithms and theory using this simulation model.As global trade continues to increase, cargo traffic at the nation's ports continues to increase at dramatic levels. For example, the Ports of Los Angeles and Long Beach are among the busiest ports in America. Booming trade with Pacific Rim nations has seen the annual trade in the two ports exceed 100 million tons. The total volume that these ports handle is evenly divided between transcontinental and local shipments. Furthermore, a large portion of the local shipments are packaged and sorted at local facilities for shipment across the continent. Railways form the major means to transcontinentally move these goods. Forecasts show that volume growth of high-value containerized freight is set to triple in the next 25 years. The main impact of the proposed research will have a significant bearing on real-world urban rail operations, many of whose pertinent complexities have been ignored in theoretical studies thus far.

这项研究解决了具有多轨道配置、多个优先级和多个速度限制的高密度复杂铁路网络中重载列车的实时调度问题。该研究将为在城市地区日益常见的复杂重载铁路网络中实现无死锁调度和最小列车总延误的理论和动态控制算法提供理论和动态控制算法。确定能够最大程度地减少列车延误并确保无死锁运行的最佳调度策略是 NP 困难的。因此，这个方向的大部分研究工作都集中在开发启发式方法以及详细的模拟来规划调度策略。迄今为止的理论工作仅涉及单轨、双轨和部分双轨等轨道配置。然而，为了将当前的理论扩展到大规模的城市铁路网络，需要考虑铁路系统中复杂的实体，如交叉口、交叉路口、支线、速度限制和货物优先级。为了满足这一需求，我们的研究将包括以下任务：（1）理论研究侧重于开发这种复杂物理系统的网络表示，以及表征操作背后的非线性和随机动力学，为开发有效的调度算法奠定基础，（2）开发无死锁和近乎最优的算法，用于在复杂的通用铁路网络中运行的列车的实时调度，以及（3） 开发一种模拟方法来对类似于南加州铁路网的复杂铁路网络进行建模，并使用该模拟模型验证算法和理论的性能。随着全球贸易的持续增长，美国港口的货物运输量继续急剧增加。 例如，洛杉矶港和长滩港是美国最繁忙的港口之一。与环太平洋国家的贸易蓬勃发展，两港年贸易量突破亿吨。这些港口处理的总货量均匀地分为跨大陆运输和本地运输。 此外，大部分本地货物在当地设施进行包装和分类，以便运往整个非洲大陆。铁路是横贯大陆运输这些货物的主要方式。预测显示，未来 25 年高价值集装箱货运量将增长两倍。拟议研究的主要影响将对现实世界的城市轨道交通运营产生重大影响，其中许多相关的复杂性迄今为止在理论研究中被忽略。