A Systematic Review of Traffic Incident Detection Algorithms

Abstract

Traffic incidents have negative impacts on traffic flow and the gross domestic product of most countries. In addition, they may result in fatalities and injuries. Thus, efficient incident detection systems have a vital role in restoring normal traffic conditions on the roads and saving lives and properties. Researchers have realized the importance of Automatic Incident Detection (AID) systems and conducted several studies to develop AID systems to quickly detect traffic incidents with an acceptable performance level. An incident detection system mainly consists of two modules: a data collection module and a data processing module. The performance of AID systems is assessed using three performance measures; Detection Rate (DR), False Alarm Rate (FAR) and Mean Time to Detect (MTTD). Based on data processing and incident detection algorithms, AID can be categorized into four categories: comparative, statistical, artificial intelligence-based and video–image processing algorithms. The aim of this paper is to investigate and summarize the existing AID systems by assessing their performance, strengths, limitations and their corresponding data collection and data processing techniques. This is useful in highlighting the shortcomings of these systems and providing potential solutions that future research should focus on. The literature is sought through an extensive review of the existing refereed publications using the Google Scholar search engine and Scopus database. The methodology adopted for this research is a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This study can serve as a reference for researchers who are interested in developing new AID systems.