查看文章

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 …