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Mathematical characterization of spatiotemporal congested traffic patterns: mixed speed data analysis in the greater Toronto and Hamilton area, Canada

机译:时空交通拥堵模式的数学表征:加拿大大多伦多和汉密尔顿地区的混合速度数据分析

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摘要

This paper formulates a comprehensive methodology for analyzing, quantifying and identifying congestion characteristics based on speed distribution. Utilizing vehicle speed data, a mathematical approach is applied, in order to characterize roadway segments, in terms of travel reliability, congestion severity and duration. We argue that the Gaussian mixture model (GMM) and its parameter combination is the appropriate tool if we are to obtain quantitative congestion measures and rank roadway performance. A significant contribution of our approach is that it is based on assumptions regarding mixed components as well as speed distribution and can be applied to large databases. We test our framework on the greater Toronto and Hamilton area in Ontario, Canada, and conclude that congestion quantification through the application of the GMM can be successfully accomplished. Results indicate that speed patterns differ significantly between counties as well as days of the week.
机译:本文提出了一种基于速度分布来分析,量化和识别拥塞特征的综合方法。利用车辆速度数据,应用数学方法,以根据行驶可靠性,拥堵严重程度和持续时间来表征道路段。我们认为,高斯混合模型(GMM)及其参数组合是获得定量拥堵措施和对道路性能进行排名的合适工具。我们方法的一个重要贡献是,它基于有关混合组件以及速度分布的假设,并且可以应用于大型数据库。我们在加拿大安大略省的大多伦多地区和汉密尔顿地区测试了我们的框架,并得出结论,通过GMM的应用可以成功完成拥塞量化。结果表明,各州之间以及一周中的几天之间的速度模式差异很大。

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