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Data Signature-Based Multiyear Analysis of BLK-NB 6-Minute Interval Datasets of 2006 to 2009

机译:基于数据签名的2006年至2009年BLK-NB 6分钟间隔数据集的多年分析

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In this paper, we present a multiyear analysis of the North Luzon Expressway Balintawak-NorthBound (NLEX BLKNB) Datasets of 2006 to 2009 using the 6-minute Interval traffic volume obtained from the National Center of Transportation Studies (NCTS). Previous works have only explored the behavior and characteristics of hourly interval datasets where in, however descriptive, we think is still limited. The analysis includes extracting valuable information like common problems faced by motorist and traffic enforcers based from the trends and behaviors of the traffic flow given the traffic volume of the Balintawak corridor in NLEX for each 6-minute interval in every week of the years 2006 to 2009. By getting the data signature and applying X-Means clustering algorithm, Vector Fusion and nMDS Algorithms to obtain visualizations needed for analysis, we are able to detect 12 outliers, some of which are previously known and unknown. We are also able to extract 90 different types of potential outliers from the datasets and analyze their behavior and commonality. Moreover, we are able to realize recurring patterns and relate gathered results to different factors such as holidays and weather disturbances. In addition, we are able to gather data about what may be the role of the SBPy component in characterizing the dataset. Finally, we conclude that the 6-minute Interval dataset has provided us with an effective visualization model used for traffic data analysis and a more in-depth level of characterization of the traffic behavior in NLEX, BLK-NB.
机译:在本文中,我们使用从国家运输研究中心(NCTS)获得的6分钟间隔交通量,对2006至2009年北吕宋高速公路Balintawak-NorthBound(NLEX BLKNB)数据集进行了多年分析。以前的工作仅探讨了小时间隔数据集的行为和特征,尽管在描述性方面,我们认为仍然有限。分析包括根据2006年至2009年期间每隔6分钟间隔NLEX的Balintawak走廊的交通量,根据交通流的趋势和行为,提取诸如驾驶员和交通执法者所面临的常见问题之类的有价值的信息。通过获取数据签名并应用X-Means聚类算法,向量融合和nMDS算法来获取分析所需的可视化效果,我们能够检测出12个离群值,其中一些是先前已知和未知的。我们还能够从数据集中提取90种不同类型的潜在异常值,并分析其行为和共性。此外,我们能够实现重复发生的模式,并将收集的结果与假期和天气干扰等不同因素相关联。此外,我们能够收集有关SBPy组件在表征数据集方面可能起什么作用的数据。最后,我们得出结论,6分钟的时间间隔数据集为我们提供了用于交通数据分析的有效可视化模型,并为NLEX,BLK-NB中的交通行为提供了更深入的表征。

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