Traffic flow models and statistical analysis using compressed data from acquisition module

Congested traffic in big cities represents one of the current problems of society because it has harmful effects on people’s quality of life by increasing the level of pollution, increasing fuel consumption of the cars or the increased time spent in traffic. That is why traffic analysis is necessary and automatic solutions based on traffic prediction and traffic flow models are needed for decongestion and its improvements. In this paper, a universal, portable, lightweight, and highautonomy module for acquisition of the relevant traffic data (GPS data) is created and proposed, based on a custom printed circuit board that can be placed in a personal car or in a vehicle from public transport. For the data acquired by testing the module, a compression algorithm is applied to extract the relevant data, and these will be used in the implementation of traffic flow models such as Greenshields, Greenberg, Underwood, and Pipes to analyze the relationship between speed, flow and density of the traffic. The results of the implementation of these traffic flow models are compared and the combination of them will be applied for multiple regime models.