A novel method about the representation and discrimination of traffic state

A novel method about the representation and discrimination of traffic state

Jiang, Junfeng (Wuhan Technology and Business University)
Chen, Qiushi (Wuhan University of Technology)
Xue, J. (TU Delft Safety and Security Science)
Wang, Haobo (Wuhan University of Technology)
Chen, Zhijun (Wuhan University of Technology)

2020

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.

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

http://resolver.tudelft.nl/uuid:bcffaa70-1b38-4021-8298-7c5b11d598be

https://doi.org/10.3390/s20185039

1424-8220

Sensors, 20 (18), 1-17

Institutional Repository

journal article

© 2020 Junfeng Jiang, Qiushi Chen, J. Xue, Haobo Wang, Zhijun Chen