Traffic State Estimation of Signalized Intersections Based on Stacked Denoising Auto-Encoder Model

To solve the problem of fuzziness and uncertainty of traffic states in signalized intersection, a method was proposed for estimating traffic condition based on stacked de-noising auto-encoder model. The simulation data and empirical data were used to train the model, and the K-means clustering method was used to determine the traffic state thresholds and the data were divided into three categories based on the threshold values. Relevant features based on the reconstruction theory of de-noising auto-encoder were automatically extracted, and unsupervised greedy layer-wise pre-training and supervised fine-tuning were utilized to train the deep auto-encoder network, so that it had good robust performance on obtaining the traffic state characters with low quality in the complex environment. From the experimental results, the proposed method obtains an accuracy of 91.5% in simulation data and 88% in empirical data, which is better by 7.1% than using decision tree model.

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