ABSTRACT

The importance of traffic pattern prediction for traffic management systems has significantly increased in recent years. This paper presents a novel method to find unusual traffic patterns by using topic modeling. We have employed topic models to provide an abstraction of speed camera data from Tehran, the capital of Iran. In this methodology, topic modeling is applied to days of weeks and months in a year and extracts weekly and monthly traffic patterns. Analysis of the abstract descriptions and their adaptation to actual urban traffic patterns prove the effectiveness of the proposed method. The model training convergence is also practically verified. Based on our experiments, our method achieves an accuracy of 99% in detecting abnormal conditions, which indicates the fitness of the topic modeling abstraction. Such a powerful abstraction capability can be exploited as a method for data comparison and search procedures.

摘要

近年来，交通模式预测对交通管理系统的重要性显着增加。本文提出了一种利用主题建模发现异常流量模式的新方法。我们使用主题模型来提供来自伊朗首都德黑兰的测速相机数据的抽象。在这种方法中，主题建模应用于一年中的数周和数月，并提取每周和每月的流量模式。抽象描述的分析及其对实际城市交通模式的适应证明了所提出方法的有效性。模型训练收敛性也得到了实际验证。根据我们的实验，我们的方法在检测异常情况方面达到了 99% 的准确率，这表明了主题建模抽象的适用性。