A K-nearest neighbor locally search regression algorithm for short-term traffic flow forecasting

Accurate short-term traffic flow forecasting is of great significance for real-time traffic control, guidance and management. The k-nearest neighbor (k-NN) model is a classic data-driven method which is relatively effective yet simple to implement for short-term traffic flow forecasting. For conventional prediction mechanism of k-NN model, the k nearest neighbors' outputs weighted by similarities between the current traffic flow vector and historical traffic flow vectors is directly used to generate prediction values, so that the prediction results are always not ideal. It is observed that there are always some outliers in k nearest neighbors' outputs, which may have a bad influences on the prediction value, and the local similarities between current traffic flow and historical traffic flows at the current sampling period should have a greater relevant to the prediction value. In this paper, we focus on improving the prediction mechanism of k-NN model and proposed a k-nearest neighbor locally search regression algorithm (k-LSR). The k-LSR algorithm can use locally search strategy to search for optimal nearest neighbors' outputs and use optimal nearest neighbors' outputs weighted by local similarities to forecast short-term traffic flow so as to improve the prediction mechanism of k-NN model. The proposed algorithm is tested on the actual data and compared with other algorithms in performance. We use the root mean squared error (RMSE) as the evaluation indicator. The comparison results show that the k-LSR algorithm is more successful than the k-NN and k-nearest neighbor locally weighted regression algorithm (k-LWR) in forecasting short-term traffic flow, and which prove the superiority and good practicability of the proposed algorithm.