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LU Hua-pu, SUN Zhi-yuan, QU Wen-cong. Repair method of traffic flow malfunction data based on temporal-spatial model[J]. Journal of Traffic and Transportation Engineering, 2015, 15(6): 92-100. doi: 10.19818/j.cnki.1671-1637.2015.06.012
Citation: LU Hua-pu, SUN Zhi-yuan, QU Wen-cong. Repair method of traffic flow malfunction data based on temporal-spatial model[J]. Journal of Traffic and Transportation Engineering, 2015, 15(6): 92-100. doi: 10.19818/j.cnki.1671-1637.2015.06.012
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Repair method of traffic flow malfunction data based on temporal-spatial model

doi: 10.19818/j.cnki.1671-1637.2015.06.012

  • Institute of Transportation Engineering, Tsinghua University, Beijing 100084, China

More Information
  • Author Bio:

    LU Hua-pu(1957-), male, professor, PhD, +84-10-62772615, luhp@mail.tsinghua.edu.cn

  • Received Date: 2015-09-16
  • Publish Date: 2015-12-25
    Abstract
  • In order to improve the accuracy of traffic flow data, the characteristics of temporal correlation, spatial correlation and historical correlation of traffic flow big data were considered, and a basic traffic flow temporal-spatial model was built.To ensure the accuracy and speed of data processing, the simplification and calibration of temporal-spatial model were realized.The temporal-spatial model was simplified and abstracted into a bi-level programming model.The operation speed was optimized in the upper level model by controlling the number of temporalspatial correlation coefficients, and the calculation accuracy was optimized in the lower level model by controlling the error.Based on the data-driven method, the bi-level programming model was solved, and the calibration of temporal-spatial model was completed.Based on the proposed temporal-spatial model, a repair method of traffic flow malfunction data was presented.Taking a road section in Beijing City as example, the validity and feasibility of proposed repair method were verified.Verification result indicates that the precisions of repair methods of traffic flow malfunction data based on historical trend, spatial correlation and time series are 79.65%, 85.16%, 89.84% respectively, however, the precision of proposed method based on the temporal-spatial model is 90.91%, that is relatively higher, the characteristics of traffic flow big data can be accurately described by the proposed repair method.

     

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