A Hybrid Approach for Automatic Incident Detection

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

54 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Article number6525409
Pages (from-to)1176-1185
Journal / PublicationIEEE Transactions on Intelligent Transportation Systems
Volume14
Issue number3
Online published6 Jun 2013
Publication statusPublished - Sept 2013

Abstract

This paper presents a hybrid approach to automatic incident detection (AID) in transportation systems. It combines time series analysis (TSA) and machine learning (ML) techniques in light of the fault diagnosis theory. In this approach, the time series component is to forecast the normal traffic for the current time point based on prior (normal) traffic. The ML component aims to detect incidents using features of real-time traffic, predicted normal traffic, as well as differences between the two. We validate our approach using a real-world data set collected in previous research. The results show that the hybrid approach is able to detect incidents more accurately [higher detection rate (DR)] and faster (shorter mean time to detect) under the requirement of a similar false alarm rate (FAR), as compared with state-of-the-art algorithms. This paper lends support to further studies on combining TSA with ML to address problems related to intelligent transportation systems (ITS).

Research Area(s)

  • Automatic incident detection (AID), hybrid approach, machine learning (ML), time series analysis (TSA)

Citation Format(s)

A Hybrid Approach for Automatic Incident Detection. / Wang, Jiawei; Li, Xin; Liao, Stephen Shaoyi et al.
In: IEEE Transactions on Intelligent Transportation Systems, Vol. 14, No. 3, 6525409, 09.2013, p. 1176-1185.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review