Analysis of Shapelet Transform Usage in Traffic Event Detection

Abstract

Automatic traffic incident detection from sensors data is a long studied topic that has been advancing with the introduction of new algorithms and recently from machine learning. While the traffic incidents detection problem can be treated as a time series classification task, there are not many attempts in this area and further investigations should be conducted. Recently, the Shapelet Transform algorithm has been proposed as a promising solution for time series classification. In this paper, we study the usage of Shapelet Transform in the field of traffic event detection. We first prove the applicability of the algorithm for automatic incident detection where it provides comparable performance to other techniques. In addition, we show how the Shapelet Transform algorithm can help in improving the detection by guiding the expert input in a cognitive approach. We test our approach using a real data set produced from road sensors of the M25 London Circular road. Results show an improvement comparing to using Shapelet Transform solely.