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

Simon Oh, Young Ji Byon, Hwasoo Yeo

Research output: Contribution to journalArticlepeer-review

60 Scopus citations


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 prediction modules. 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.

Original languageBritish English
Article number7353174
Pages (from-to)1146-1156
Number of pages11
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number4
StatePublished - Apr 2016


  • Data-driven approach
  • intelligent transportation systems (ITS)
  • K-nearest neighbors method (K-NN)
  • sequential search strategy
  • traffic state prediction


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