Abstract The qualitative collection of traffic information is an important task. It allows to make adequate decisions at the design stage, reconstruction, repair of roads, and also helps to manage traffic. The quality of traffic control systems depends on the accuracy of determining parameters of traffic flow. The main parameter is intensity. In this article proposed to consider the problem of determining the intensity not as a problem of the classification of objects, but as a problem of segmentation. A software implementation of a method for determining the intensity of traffic, which based on a neural network and analysis of information from video cameras of traffic monitoring, has been developed. The neural network was trained on data that was obtained using the method of motion detection. It is proposed to use the TLCR (Traffic lane congestion ratio) parameter instead of AADT (Average Annual Daily Traffic) for more accurate transmission of information about the current traffic situation.
Traffic Lane Congestion Ratio Evaluation by Video Data
18.07.2019
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
AUTONOMOUS-MODE TRAFFIC LANE SELECTION BASED ON TRAFFIC LANE CONGESTION LEVELS
Europäisches Patentamt | 2018
|System and method for monitoring traffic lane congestion
Europäisches Patentamt | 2022
|SYSTEMS AND METHODS FOR MONITORING TRAFFIC LANE CONGESTION
Europäisches Patentamt | 2021
|SYSTEMS AND METHODS FOR MONITORING TRAFFIC LANE CONGESTION
Europäisches Patentamt | 2023
|Lane congestion prompting method, lane congestion prompting system and terminal
Europäisches Patentamt | 2023
|