An Intelligent Control Method for Urban Traffic using Fog Processing in the IoT Environment based on Cloud Data Processing of Big Data

Computer and Knowledge Engineering

مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 23 بهمن 1401  

نوع مقاله: Internet of Thing (IoT)-Yaghmaee

شناسه دیجیتال (DOI): 10.22067/cke.2023.78257.1066

نویسندگان

alireza soleimany ¹؛ yousef farhang ²؛ amin Babazadeh Sangar³

¹Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran & University faculty membe,

²Department of Computer Engineering, Khoy Branch, Islamic Azad University, Khoy, Iran assistant professor

³Department of IT and Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran assistant professor

چکیده

Due to the disadvantages of current traffic light control methods such as waste of time, waste of fuel and resources, increased air pollution and many other cases, providing an intelligent traffic light control system that leads to vehicles and pedestrians the shortest waiting time Have a vital issue. Given the high priority of this issue, this paper presents an intelligent urban traffic system framework based on IoT data and fog processing. In this article, we first collect data through the Internet of Things. Then the preprocessing operation and extraction of effective fields in the cloud processing section is performed using the k-nearest neighbor improved machine learning algorithm. Traffic on each road is predicted in the next time slot and this information is sent for use in the fog processing layer to make traffic control decisions. The concept of Euclidean distance network with Gaussian weight has been used to predict the future traffic situation and the KNN model has been included in the algorithm output to increase the forecasting accuracy and finally solve the problem of traffic light control. The results of the evaluations show that the proposed framework in terms of absolute mean error percentage, absolute mean error percentage of traffic forecast, average waiting time of each vehicle has a much better performance than the previous two methods.