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

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Having access to the future traffic state information is crucial in maintaining successful intelligent transportation systems (ITS). However, predicting the future traffic state is a challenging research subject involving prediction reliability issues. Predictive performance measures, including the accuracy, efficiency, and stability, are generally considered as the most important priorities in the evaluation of predictionmodules. Researchers have developed various K-nearest-neighbors-based searching algorithms that find the future state from the historical traffic patterns. Interestingly, there has not been sufficient effort made for improving the performance. For the emerging big data era, incorporating an efficient search strategy has become increasingly important since the applicability of the prediction module in ITS heavily relies on the efficiency of the searching method 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, which are prediction accuracy, efficiency, and stability. Compared with the conventional approach, the proposed sequential method yields significantly more accurate results via internal hierarchical improvements across sublevels while maintaining excellent efficiency and stability.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2016-04
Language
English
Article Type
Article
Keywords

TRAVEL-TIME PREDICTION; FEATURE-SELECTION; NONPARAMETRIC REGRESSION; NEURAL-NETWORKS; FREEWAY BOTTLENECKS; FILTER APPROACH; KALMAN FILTER; MISSING DATA; FLOW; FEATURES

Citation

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, v.17, no.4, pp.1146 - 1156

ISSN
1524-9050
DOI
10.1109/TITS.2015.2498408
URI
http://hdl.handle.net/10203/208753
Appears in Collection
CE-Journal Papers(저널논문)
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