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Study on Traffic Multi-Source Data Fusion

Author

Listed:
  • Suping Liu

    (Guangdong University of Science and Technology, Guangdong, China)

  • Dongbo Zhang

    (Guangdong Institute of Intelligent Manufacturing, Guangdong, China)

  • Jialin Li

    (Science and Technology Gannan Normal University, Jiangxi, China)

Abstract

In order to alleviate urban traffic congestion, it is necessary to obtain roadway network traffic flow parameters to estimate the traffic conditions. Single-detector data may not be sufficient to obtain a comprehensive, effective, accurate and high-quality traffic flow data. Neural networks and regression analysis data fusion methods are employed to expand data sources as well as for improving data quality. The multi-source detector data can provide fundamental support for traffic management. An empirical analysis was conducted using acquisition technology employed by the Beijing urban expressway to estimate traffic flow parameters. The results show that the proposed data fusion method is feasible and provides reliable data sources.

Suggested Citation

  • Suping Liu & Dongbo Zhang & Jialin Li, 2019. "Study on Traffic Multi-Source Data Fusion," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 13(2), pages 63-75, April.
    • Handle: RePEc:igg:jcini0:v:13:y:2019:i:2:p:63-75
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