The Short-Term Traffic Flow Prediction Based on MapReduce

Abstract

Short-term traffic volume forecasting represents a critical need for Intelligent Transportation Systems. In this paper, we propose an improved K-Nearest Neighbor model, named I-KNN, in a general MapReduce framework of distributed modeling on a Hadoop platform, to enhance the accuracy and efficiency of short-term traffic flow forecasting. More specifically, I-KNN considers the spatial–temporal correlation and weight of traffic flow with trend adjustment features, to optimize the search mechanisms containing state vector, proximity measure, prediction function, and K selection. The results of the performance testing conducted in this paper demonstrates the superior predictive accuracy and drastically lower computational requirements of the I-KNN compared to either the neural network or the nearest neighbor approach. And also significantly improves the efficiency and scalability of short-term traffic flow forecasting over existing approaches.