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Analyzing Traffic Patterns on Street Segments Based on GPS Data Using R

机译：使用R基于GPS数据分析路段的交通模式

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Nowadays GPS enabled devices are widely spread between drivers making the collection of GPS data more accessible. So there is an opportunity to infer useful patterns and trends. In this research, we plan to apply a statistical approach on 10000 vehicle GPS traces, from around 3600 drivers which are mined to extract the outlier traffic pattern to be used further in an Intelligent Transportation System. We choose to divide the urban area into a grid and organizing the road infrastructure as segments in a graph. Further, at a given time we can make an assumption regarding the congestion level in a specific area taking into account the visits for each vehicle, using the GPS trace data. Over time, the visited segments will settle into a pattern and vary periodically. In this study we will use R software in conjunction with a set of libraries. They provide an environment in which we can perform statistical analysis and produce graphics to annotate different results. Our objective is to identify contiguous set of road segments and time intervals which have the largest statistically significant relevance in forming traffic patterns. Taking into account the number of drivers that submitted their routes in correlation with the entire population on New Haven we can state that a 2-3% penetration rate of smart phones is enough to provide accurate measurements of the traffic flow and identification of traffic patterns.
机译：如今，具有GPS功能的设备已广泛分布在各个驱动程序之间，从而使GPS数据的收集更加方便。因此，有机会推断出有用的模式和趋势。在这项研究中，我们计划对来自大约3600名驾驶员的10000辆GPS GPS轨迹应用统计方法，以提取异常的交通模式，以在智能交通系统中进一步使用。我们选择将市区划分为网格，并将道路基础设施组织为图形的一部分。此外，在给定的时间，我们可以使用GPS跟踪数据，在考虑到每辆车的访问次数的情况下，对特定区域的拥堵程度做出假设。随着时间的流逝，访问的细分受众群将逐渐形成一种模式并定期变化。在这项研究中，我们将结合使用R软件和一组库。它们提供了一个环境，我们可以在其中进行统计分析并生成图形以注释不同的结果。我们的目标是确定在形成交通模式时具有统计意义上最大相关性的连续路段和时间间隔集。考虑到在纽黑文（New Haven）上提交的路线数量与整个人口相关的驾驶员数量，我们可以说，智能手机的2-3％的渗透率足以提供准确的流量测量和交通模式识别。

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