Edge Computing-driven Smart Transportation: Analysis and Prediction of Urban Traffic Flow

Abstract

With the continuous development of artificial intelligence, the research and application of automated connected driving is receiving more and more attention. The combination of mobile edge computing and Telematics provides crucial technical support. In this paper, we propose a rasterization method for mobile edge computing scenario. We establish a grid classification model based on K-means to facilitate targeted and distributed optimization of computing resources. Meanwhile, we dynamically adjust the allocation of computational resources and the location of edge servers in advance deployment by predicting vehicle traffic. The experimental results demonstrate that the K-means-based grid classification model can accurately classify the grids and differentiate the traffic patterns of the grids, such as remote suburban areas, suburban areas, residential living areas, production industrial areas, and commercial center areas. The Backpropagation (BP) neural network-based grid traffic flow prediction model can simulate the overall changes of the grid traffic flow throughout a day to the maximum extent. Finally, we plan optimization scheme in urban mobile edge scenarios based on the results obtained from our analysis and prediction, and through our results, we can make edge computing resources be utilized efficiently.