Volume 39 Issue 1
Feb.  2021
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Article Navigation >  Journal of Transport Information and Safety  > 2021  >  39(1): 64-75
MA Tianyi, WEN Jiaqiang, WANG Liyuan, LYU Nengchao, WANG Yugang. An Estimation Model of Section Traffic Parameters Based on Connected ADAS Spatiotemporal Data[J]. Journal of Transport Information and Safety, 2021, 39(1): 64-75. doi: 10.3963/j.jssn.1674-4861.2021.01.008
Citation: MA Tianyi, WEN Jiaqiang, WANG Liyuan, LYU Nengchao, WANG Yugang. An Estimation Model of Section Traffic Parameters Based on Connected ADAS Spatiotemporal Data[J]. Journal of Transport Information and Safety, 2021, 39(1): 64-75. doi: 10.3963/j.jssn.1674-4861.2021.01.008
shu

An Estimation Model of Section Traffic Parameters Based on Connected ADAS Spatiotemporal Data

doi: 10.3963/j.jssn.1674-4861.2021.01.008
  • 1.

    Second Highway Consultant Company LTD., CCCC, Wuhan 430056, China

  • 2.

    Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China

  • 3.

    Engineering Research Center for Transportation Safety of Ministry of Education, Wuhan University of Technology, Wuhan 430063, China

  • Received Date: 2020-11-26
  • Publish Date: 2021-02-28
    Abstract
  • Real-time acquisition of traffic parameters is an essential basis for road traffic control. A method for estimating section traffic parameters using the connected ADAS data is studied for the limited observation range of fixed detectors and the great demand for floating vehicles. A traffic parameter-estimation model under unsteady traffic conditions is established by analyzing the relationship between forward target parameters perceived by on-board ADAS and traffic parameters, the definition of generalized traffic volume, and the relative motion characteristics of the ADAS vehicle and its neighboring vehicle in a multi-lane environment. According to the simulation, the calibration data set and the verification data set are obtained to complete the parameter calibration and verification of the model. Also, the paper discusses the influences of time and space resolutions, and ADAS vehicle penetration rates on the estimation accuracy of the model. The analysis shows that when the time resolution is reduced by 5 min, the estimation error is reduced by 3.4% on average; reducing the time resolution can improve the estimation accuracy of the proposed model. When the space resolution is reduced by 500 m, the estimation error of flow and density is reduced by 1.68% on average; however, it may lead to an average increase of 5.19% in speed estimation error. The increased penetration rate of ADAS vehicles can enhance the overall fit between estimated traffic parameters and observed traffic parameters in the time-space area of the road sections. In the context of the gradual application of ADAS, the proposed model of traffic parameter estimation can quickly obtain the traffic volume information in the continuous time-space range of the road sections.

     

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