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A hybrid CEEMD-GMM scheme for enhancing the detection of traffic flow on highways

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Abstract

Many highways are acquiring smart transportation systems to improve traffic efficiency, safety and management. Intelligent transportation systems can monitor traffic congestion by providing estimated time of arrival or suggesting a diversion route. However, due to the chaotic complexity of the highway traffic road network and the short-term mobility of the population, traffic flow prediction is affected by many complex factors, and the development of an effective traffic flow forecasting system is very challenging. This paper establishes a novel framework to enhance the prediction of highway traffic flow based on the integration of Complementary Ensemble Empirical Mode Decomposition and Gaussian Mixture Model (CEEMD-GMM). Firstly, the complementary ensemble empirical mode decomposition is used to decompose and reduce the noise of traffic flow data. The empirical modal components are combined by calculating the sample entropy (SE), which makes the time series stable and reduces the time cost of forecasting. Secondly, the histogram of oriented gradient (HoG) is computed to extract the distinct features of various traffic flows and enhance the traffic flow prediction. Furthermore, the highway traffic data is classified using Gaussian mixture model to realize the feature extraction of different types of highway traffics. The performance of the model is evaluated in terms of mean absolute error (MAE), root mean square error (RMSE), mean absolute percent error (MAPE), and average correlation. The model is extensively verified using traffic flow data under individual weather conditions and different traffic flow datasets. Compared with the traditional support vector machine (SVM), artificial neural network (ANN), random forest (RF) and Adaboost algorithms, the proposed model shows 2.90% MAE, 6.07% RMSE, 0.38% MAPE and average correlation coefficient of 0.74 for traffic flow detection. It is concluded that the model can predict the highways traffic flow with high accuracy as compared to other contemporary models.

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Funding

This study was funded by the Zhejiang Provincial Department of Transportation Science and Technology Program Project “Research and Demonstration Application of Key Technologies for Precise Sensing of Expressway Thrown Objects” (No. 202204).

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Authors and Affiliations

  1. Zhejiang Institute of Communications, Hangzhou, 310012, Zhejiang, China

    Huili Dou, Yuanyuan Liu, Sirui Chen & Hongyang Zhao

  2. Department of Automation, University of Science and Technology of China, Hefei, 2300271, China

    Hazrat Bilal

Authors
  1. Huili Dou
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  2. Yuanyuan Liu
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  3. Sirui Chen
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  4. Hongyang Zhao
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  5. Hazrat Bilal
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Correspondence to Huili Dou.

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Dou, H., Liu, Y., Chen, S. et al. A hybrid CEEMD-GMM scheme for enhancing the detection of traffic flow on highways. Soft Comput 27, 16373–16388 (2023). https://doi.org/10.1007/s00500-023-09164-y

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