Analysis of algorithms for detecting traffic incidents on highways using stationary vehicle detectors

Abstract

Incident detection algorithms from an automation point of view can be divided into two categories: automatic and non-automatic incident detection. Automatic algorithms refer to those algorithms that automatically identify an incident based on traffic flow data received from traffic detectors. Manual algorithms or procedures rely on reports from human witnesses. Based on functional characteristics, incident detection algorithms are divided into algorithms for highways and algorithms for street networks. Based on data acquisition methods, incident detection algorithms are divided into three groups: algorithms using data from stationary vehicle detectors (inductive loops, radars, video cameras, etc.); algorithms using mobile sensors (Bluetooth, wi-fi RFID, GPS, Glonass sensors, toll system transponders, etc.). algorithms that use information from drivers (GSM communications, navigation services, Internet applications, etc.). This article discusses algorithms that use data from stationary vehicle detectors. The disadvantages of incident detection algorithms using stationary transport detectors include: the need to install and operate transport detectors (inductive, video, etc.) leads to interference with traffi fl and sometimes to temporary closure of traffic The location of installation of vehicle detectors, the frequency of their installation and the number are critical from the point of view of detecting an incident on a particular section of the highway. However, it is extremely labor and capital-intensive to install stationary detectors along the entire length of the highway. Also, inductive vehicle detectors, which are mainly used to determine the parameters of traffic flow on highways, are unreliable and often fail, which makes it ineffective to detect incidents on a particular section of the road. The advantages of the algorithms under consideration include their proven reliability and accuracy in identifying incidents over decades, which is their undoubted advantage over algorithms that use mobile sensors or information from drivers.