Traffic volume responsive incident detection

Incident detection is one of the major concerns of freeway operators, as incidents account for more than 60% of the travel delays faced by motorists, especially under recurrent traffic congestion. Algorithms detecting incidents are categorized based on the intelligence they use to analyse the measurements taken by traffic monitoring (volume, occupancy, speed). As roadway geometry and traffic conditions affect the algorithms' accuracy, the objectives of this paper is to take them into account when specifying the threshold values, and to assess the effect on the algorithms' performance on the network operations. The main steps of the methodology are the calibration of available incident detection algorithms (California # 7 kappa alpha iota DELOS) to a set of traffic and incident data and the validation of the threshold values. Further adaptation of these values is attempted to the changing traffic volumes. When the threshold values were being calibrated on the prevailing traffic conditions, an improvement was observed on all three performance indices (detection rate increase by 20%, false alarm rate decrease by 25% and time to detect at the same levels). (C) 2017 The Authors. Published by Elsevier B.V.