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Comprehensive Evaluation Method for Traffic Flow Data Quality Based on Grey Correlation Analysis and Particle Swarm Optimization

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Abstract

Nowadays, data are more and more used for intelligent modeling and prediction, and the comprehensive evaluation of data quality is getting more and more attention as a necessary means to measure whether the data are usable or not. However, the comprehensive evaluation method of data quality mostly contains the subjective factors of the evaluator, so how to comprehensively and objectively evaluate the data has become a bottleneck that needs to be solved in the research of comprehensive evaluation method. In order to evaluate the data more comprehensively, objectively and differentially, a novel comprehensive evaluation method based on particle swarm optimization (PSO) and grey correlation analysis (GCA) is presented in this paper. At first, an improved GCA evaluation model based on the technique for order preference by similarity to an ideal solution (TOPSIS) is proposed. Then, an objective function model of maximum difference of the comprehensive evaluation values is built, and the PSO algorithm is used to optimize the weights of the improved GCA evaluation model based on the objective function model. Finally, the performance of the proposed method is investigated through parameter analysis. A performance comparison of traffic flow data is carried out, and the simulation results show that the maximum average difference between the evaluation results and its mean value (MDR) of the proposed comprehensive evaluation method is 33.24% higher than that of TOPSIS-GCA, and 6.86% higher than that of GCA. The proposed method has better differentiation than other methods, which means that it objectively and comprehensively evaluates the data from both the relevance and differentiation of the data, and the results more effectively reflect the differences in data quality, which will provide more effective data support for intelligent modeling, prediction and other applications.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their insightful suggestions and remarks, which helped in improving the manuscript. This work was supported by the Scientific Research Funding Project of Liaoning Education Department of China under Grant No. JDL2020005, No. LJKZ0485, and the National Key Research and Development Program of China under Grant No. 2018YFA0704605.

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Authors and Affiliations

  1. School of Automation and Electrical Engineering, Dalian Jiaotong University, Dalian, 116028, China

    Wei Ba & Baojun Chen

  2. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116023, China

    Qi Li

Authors
  1. Wei Ba
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  2. Baojun Chen
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  3. Qi Li
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Correspondence to Qi Li.

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The authors declare no conflict of interest.

Additional information

Wei Ba received the B.S. degree in automation from Dalian University of Technology, Dalian, China, in 2003 and she received the Ph.D in control theory and control engineering from Dalian University of Technology, Dalian, China, in 2010. She is now an associate professor of Dalian Jiaotong University, Dalian, China. Her research interests include data quality analysis, big data modeling and artificial intelligence technology.

Baojun Chen received the B.S. degree in automation from Dalian University of Technology, Dalian, China, in 2002 and she received the Ph.D in circuit and system from Dalian University of Technology, Dalian, China, in 2012. She is now an associate professor of Dalian Jiaotong University, Dalian, China. Her research interests include data quality analysis, artificial intelligence technology and microwave device technology.

Qi Li received the B.S. degree in automation from Dalian University of Technology, Dalian, China, in 2002 and he received the Ph.D in control theory and control engineering from Dalian University of Technology, Dalian, China, in 2008. He is now an associate professor of Dalian University of Technology, Dalian, China. His research interests include advanced process control, soft sensor and artificial intelligence algorithm.

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Ba, W., Chen, B. & Li, Q. Comprehensive Evaluation Method for Traffic Flow Data Quality Based on Grey Correlation Analysis and Particle Swarm Optimization. J. Syst. Sci. Syst. Eng. 33, 106–128 (2024). https://doi.org/10.1007/s11518-023-5585-5

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