城市道路网络中基于渗流的交通集群动态计算分析

IF 3.7 3区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Yongsung Kwon , Minjin Lee , Mi Jin Lee , Seung-Woo Son
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引用次数: 0

摘要

了解交通集群的动态对加强城市交通系统至关重要,特别是在管理拥堵和自由流动状态方面。本研究运用计算渗透理论分析城市道路网络中交通集群的形成和增长,使用来自中国成都的高分辨率出租车数据。将道路网络呈现为一个时间依赖的、加权的、有向图,我们通过巨型连接组件(gcc)的增长模式识别交通拥堵和自由流集群中的不同行为。海湾合作委员会规模曲线之间的持续差距,特别是在高峰时段,突出了空间交通相关性驱动的差异。这些都是通过长期的权重相关性量化的,为交通动态提供了一种新的计算度量。我们的方法展示了网络拓扑和时间变化对集群形成的影响,为复杂交通系统的建模提供了一个强大的框架。研究结果对交通管理具有实际意义,包括动态信号优化、基础设施优先级和缓解拥堵的策略。本研究将图论、渗流分析和交通建模相结合,推进了城市交通分析的计算方法,为大规模交通系统的优化提供了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A computational analysis of traffic cluster dynamics using a percolation-based approach in urban road networks
Understanding the dynamics of traffic clusters is crucial for enhancing urban transportation systems, particularly in managing congestion and free-flow states. This study applies computational percolation theory to analyze the formation and growth of traffic clusters within urban road networks, using high-resolution taxi data from Chengdu, China. Presenting the road network as a time-dependent, weighted, directed graph, we identify distinct behaviors in traffic jam and free-flow clusters through the growth patterns of giant connected components (GCCs). A persistent gap between GCC size curves, especially during rush hours, highlights disparities driven by spatial traffic correlations. These are quantified through long-range weight-weight correlations, offering a novel computational metric for traffic dynamics. Our approach demonstrates the influence of network topology and temporal variations on cluster formation, providing a robust framework for modeling complex traffic systems. The findings have practical implications for traffic management, including dynamic signal optimization, infrastructure prioritization, and strategies to mitigate congestion. By integrating graph theory, percolation analysis, and traffic modeling, this study advances computational methods in urban traffic analysis and offers a foundation for optimizing large-scale transportation systems.
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来源期刊
Journal of Computational Science
Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
5.50
自引率
3.00%
发文量
227
审稿时长
41 days
期刊介绍: Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).
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