Short-term traffic prediction under normal and incident conditions using singular spectrum analysis and the k-nearest neighbour method

Short-term traffic prediction is an important area in Intelligent Transport Systems (ITS) research. A number of ITS applications such as Advanced Traveller Information Systems (ATIS), Dynamic Route Guidance (DRG) and Urban Traffic Control (UTC) can benefit from improved prediction of traffic variables for the short-term future. Traffic prediction during abnormal condition, such as incidents, is especially important to these applications. However, this is an area not well-researched. This paper presents a novel improvement to a k-Nearest Neighbour (kNN) based traffic predictor with Singular Spectrum Analysis (SSA) technique based data preprocessing. This SSA-kNN framework is implemented for short-term traffic prediction under both normal and incident traffic conditions. A key feature of this approach is the data pre-processing step, which is designed to accommodate the extremely noisy sensor inputs that arise during incident conditions. This paper compares the prediction accuracy of the SSA-kNN approach with three other commonly used machine learning methods, kNN, Grey System Model (GM) and Support Vector Regression (SVR). Moreover, the sensitivity of traffic prediction accuracy to various kNN design parameters is explored. The results show that the proposed SSA-kNN based approach has the best prediction accuracy among the methods used in this study, especially during non-recurring incidents. The concept behind the proposed method can be extended to other machine learning tools to improve the accuracy of short-term traffic forecasting models.