Development of Integrated Sensor System for Intelligent Transportation System

Traffic congestion has always been a major problem in the Philippines. This project study developed an integrated sensor system for intelligent transportation, which addressed the lack of automatic traffic monitoring systems to achieve traffic efficiency and safety. The researchers utilized different sensors in gathering data such as water level, temperature, and humidity within a certain area as well as a camera in capturing images and compared the three image compression algorithms, mainly the DCT (Discrete Cosine Transform), DWT (Discrete Wavelet Transform) and SVD (singular value decomposition) in terms of different parameters used in describing and identifying the best type of image compression to be applied prior to transmission to other different nodes. MSE (mean squared error) value, PSNR (Peak signal-to-noise ratio), Compress Ratio, and Process Time were the values of comparison in determining the best compression technique or algorithm for the gathered images. As a result, it was found that DCT characterized the best parameters for image compression. DCT produced the highest PSNR (52.41979dB), the lowest value for MSE (0.37247), and the lowest process time (0.16181s), while SVD was able to produce the most compressed image. This project also utilized solar renewable energy for the power management system, which enabled the system to run independently without any other external power source. This will be beneficial for the community to identify which roads can be used to optimize the mobility of the vehicles and maximize the use of renewable energy; hence will help reduce the traffic congestion issues in the country.