A novel method about the representation and discrimination of traffic state

Junfeng Jiang, Qiushi Chen, Jie Xue, Haobo Wang, Zhijun Chen

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

The representation and discrimination of various traffic states play an essential role in solving traffic accidents and congestion as the foundation of traffic state prediction. However, the existing representation of the traffic state usually only considers the road congestion layer and divides the traffic state into congested and unblocked. Representation only at the congestion layer is difficult to reflect the road traffic state comprehensively. Therefore, we select three indicators from the layers of road congestion, road safety, and road stability, respectively, then utilizing K-means to cluster the traffic state. The clustering results can be regarded as a new type for the representation of a traffic state. As a result, the traffic states are divided into four classes, which comprehensively reflects the level of road congestion, safety, and stability. Using the four traffic states obtained from the clustering results as class labels, we applied a multi-layer perceptron (MLP) to classify the different traffic states, and the receiver operating characteristic (ROC) curve is assessed to verify the superiority of the classification results. Finally, a visual display of the real-time traffic state in a city’s central area was given.

Original languageEnglish
Article number5039
Pages (from-to)1-17
Number of pages17
JournalSensors (Switzerland)
Volume20
Issue number18
DOIs
Publication statusPublished - 2020

Keywords

  • K-means
  • Multi-layer perceptron (MLP)
  • Road safety
  • Traffic accidents
  • Traffic congestion
  • Traffic flow
  • Traffic state

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