A Case Study on Periodic Spatio- Temporal Hotspot Detection in Azure Traffic Data

Venkata M. V. Gunturi, Rakesh Rajeev, Vipul Bondre, Aaditya Barnwal, Samir Jain, Ashank Anshuman, Manish Gupta
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Abstract

Given a spatio-temporal event framework E and a collection of time-stamped events A (over E), the goal of the periodic spatio-temporal hotspot detection (PST-Hotspot) problem is to determine spatial regions which show high “intensity” of events at certain periodic intervals. The output of the PST-Hotspot detection problem consists of the following: (a) a col-lection of spatial regions (which show high intensity of events) and, (b) their respective time intervals of high activity and periodicity values (e.g., daily, weekday-only, etc). PST-Hotspot detection poses significant challenge for designing a suitable interest measure. The aim over here is to design a mathematical representation of a PST-Hotspot such that it can differentiate interesting periodic patterns from trivial persistent patterns in the dataset. The current state of the art in the area of spatial and spatio-temporal hotspot detection focus on non-periodic patterns. In contrast, our proposed approach is able to determine periodic hotspots. We experimentally evaluated our proposed algorithm using real Azure traffic dataset from the Indian region.
基于Azure交通数据的周期性时空热点检测实例研究
给定一个时空事件框架E和一组时间戳事件a(在E上),周期时空热点检测(PST-Hotspot)问题的目标是确定在一定的周期间隔内显示出高“强度”事件的空间区域。PST-Hotspot检测问题的输出包括以下内容:(a)空间区域的集合(显示事件的高强度)和(b)它们各自的高活动和周期性值的时间间隔(例如,每天,仅工作日等)。pst -热点检测对设计合适的兴趣测量提出了重大挑战。这里的目标是设计一个PST-Hotspot的数学表示,这样它就可以区分数据集中有趣的周期性模式和琐碎的持久模式。目前在空间和时空热点检测领域的研究主要集中在非周期模式。相比之下,我们提出的方法能够确定周期性热点。我们使用来自印度地区的真实Azure交通数据集对我们提出的算法进行了实验评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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