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

机译：K最近邻法在交通状态预测中的搜索策略改进

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摘要

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.
机译：访问未来的交通状态信息对于维持成功的智能交通系统（ITS）至关重要。但是，预测未来的交通状况是一项具有挑战性的研究课题，涉及预测可靠性问题。包括准确性，效率和稳定性在内的预测性能度量通常被视为评估预测模块中最重要的优先事项。研究人员已经开发出各种基于K近邻的搜索算法，这些算法可从历史流量模式中查找未来状态。有趣的是，还没有做出足够的努力来改善性能。对于新兴的大数据时代，合并有效的搜索策略变得越来越重要，因为ITS中预测模块的适用性在很大程度上取决于所用搜索方法的效率。本文为交通状态预测开发了一种新颖的顺序搜索策略。发现在预测精度（预测精度，效率和稳定性）方面，所提出的顺序策略优于传统的单级搜索方法。与常规方法相比，所提出的顺序方法通过跨子级别的内部分层改进而产生了更为准确的结果，同时保持了出色的效率和稳定性。

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