Evaluation of Automatic Incident Detection Systems Using the Automatic Incident Detection Comparison and Analysis Tool

Abstract

This thesis presents a new testbed for Automatic Incident Detection (AID) systems that uses real-time traffic video and data feeds from the Ministry of Transportation, Ontario (MTO) COMPASS Advanced Traffic Management System (ATMS). This new testbed, termed the AID Comparison and Analysis Tool (AID CAAT), consists largely of a data warehouse storing a significant amount of traffic video, the corresponding traffic data and an accurate log of incident start/end times. An evaluation was conducted whereby the AID CAAT was used to calibrate, and then analyze the performance of four AID systems: California Algorithm 8, McMaster Algorithm, the Genetic Adaptive Incident Detection (GAID) Algorithm and the Citilog - VisioPAD. The traditional measures of effectiveness (MOE) were initially used for this evaluation: detection rate (DR), false alarm rate (FAR), and mean time to detection (MTTD). However, an in-depth analysis of the test results (facilitated by the AID CAAT) revealed the need for two additional MOEs: False Normal Rate and Nuisance Rate. The justification and sample calculations for these new MOEs are also provided. This evaluation shows the considerable advantages of the AID CAAT, and also suggests the strengths and weaknesses of the AID systems tested.