Traffic Prediction System Using IoT Cluster Based Evolutionary Under Sampling Approach

Abstract

Road traffic is increasing nowadays due to the rapid growth of vehicle usage, which necessitates highly accurate traffic prediction systems. The traffic data is time-series data which identifies different pattern during low, moderate and high traffic time. Moving weighted average model that captures this phenomenon by assigning different weights for the different traffic classes to calculate the threshold values. This paper addresses the class imbalance in the traffic dataset due to which has the high impact on precision. The class imbalance problem can be handled by various data-level and algorithm-level techniques. Hybrid methods show better results while handling skewed class distribution in binary class problems. But the multiclass imbalance is a rarely focused research area. In this work, Cluster Based Evolutionary Undersampling with Correlation Relation (CBEUS-CR) is proposed to handle the multiclass imbalance problem of the traffic prediction system. The proposed system uses multiple regression as a fitness function on traffic parameters of an evolutionary algorithm in the majority classes on Apache spark environment. As a result, the sensitivity, and specificity of traffic prediction is improved and achieves 97% accuracy as it balances the number of instances between majority and minority classes.