Road Traffic Status Prediction Approach Based on Kmeans-Decision Tree Model

Abstract

An effective way to solve the problem of urban traffic congestion is to predict the road traffic status accurately and take effective traffic control measures in time. Considering the impact of visibility on traffic, the pavement status and time characteristics were finely divided, and a regression decision tree was used to establish the traffic flow velocity prediction model with pavement status, time characteristics, and working day characteristics as characteristic parameters. Furthermore, based on the perspective of avoiding using velocity as a single parameter to classify the road traffic status levels, the Kmeans clustering algorithm was used to obtain the classification label results. Moreover, the traffic flow velocity and pavement status were used as characteristic parameters of the classification decision tree to establish the multi-parameter road traffic status prediction model. The experimental result showed that the prediction accuracy of the proposed road traffic status prediction model was 81.31%, and this method has good applicability and certain application value for road traffic status prediction.

Entropy-based distributed denial of service attack detection in software-defined networking

“…It prompts the host to search for a snooping application on the port regularly, and if none is found, it sends an ICMP (destination unreachable) packet. This procedure reduces host resources, causing unavailability [9], [10].…”