Traffic Data Collection and Analysis based on Wireless Sensor Network

Abstract

The development of urban environments lead to many issues, one among them is the traffic blockage. For this speedy running world, adequate traffic measures should be taken with the help of modern technology. In the process of building smart cities, we should plan for the proper management of traffic. To control this traffic issue, we need effective computation and prediction of traffic flow. To achieve this, the traffic flow data should be collected and analyzed properly. Wireless sensor network (WSN) is an auto-configured group of sensors that monitor and record physical parameters of the environment like temperature, pressure, sound, motion, etc. and organize the data at a central location. These WSNs find their application in various fields these days like in the military for battlefield surveillance, in health monitoring systems, and many more. One such application can be used in the traffic management system. The traffic flow data can be collected using the wireless sensor network, it constitutes wireless modules and magnetometer sensors which gives the vehicle flow data along with their speed and location accurately. This system is cost-efficient, consumes less power, and requires less maintenance. The data is collected and transmitted to the cloud server using the mobile internet. The traffic flow data is then computed using a method based on incremental noise addition for chaotic identification. This method involves the addition of noise signals with different intensities incrementally by the Pseudo-periodic surrogate (PPS) method and the mean value of delayed mutual information (MD) is used to measure the complexity of the signal. Depending upon the trends of the changes in the noise mixed-signal, the characteristics can be determined. Hence, we conclude that this method helps in bringing out an organized traffic management system.