Improvement of Search Strategy With K-Nearest Neighbors Approach for Traffic State Prediction


Having access to the future traffic state information is crucial in maintaining successful intelligent transportation systems (ITS). However, predicting the long run traffic state could be a difficult analysis subject involving prediction reliability issues. Predictive performance measures, including the accuracy, potency, and stability, are typically considered as the most vital priorities in the analysis of prediction modules. Researchers have developed various K-nearest-neighbors-based mostly searching algorithms that find the long run state from the historical traffic patterns. Interestingly, there has not been sufficient effort made for improving the performance. For the emerging massive data era, incorporating an efficient search strategy has become increasingly important since the applicability of the prediction module in ITS heavily relies on the potency of the looking methodology used. This paper develops a novel sequential search strategy for traffic state predictions. The proposed sequential strategy is found to be outperforming the conventional single-level search approach in terms of prediction measures, that are prediction accuracy, efficiency, and stability. Compared with the standard approach, the proposed sequential technique yields considerably additional accurate results via internal hierarchical improvements across sublevels whereas maintaining excellent efficiency and stability.

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