Short-Time Traffic State Forecasting Using Adaptive Neighborhood Selection Based on Expansion Strategy

Abstract

Short-term traffic state forecasting is critical for real-time traffic control, but due to its complexity and its nonlinear nature, it is difficult to obtain a high degree of precision. The “k-nearest neighbors” model has been widely used to solve nonlinear regression and time series forecasting. This paper presents a traffic state forecasting method using adaptive neighborhood selection based on expansion strategy to search manifold neighbors to get higher precision with manifold neighbors. We propose a method of linear structure to handle the traffic data in Euclidean space to find a manifold neighbor that is more suitable for predicting traffic states. The results of extensive comparison experiments indicate that the proposed model can produce more accurate forecasting results than other classic algorithms.