Volume 40 Issue 4
Aug.  2022
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Article Navigation >  Journal of Transport Information and Safety  > 2022  >  40(4): 128-137
ZHANG Chi, HU Ruilai, XIANG Delong, ZHANG Hong, ZHANG Min. A Prediction Model for Operation Speed of Six-axis Articulated Trains in Uphill Sections of Expressways[J]. Journal of Transport Information and Safety, 2022, 40(4): 128-137. doi: 10.3963/j.jssn.1674-4861.2022.04.014
Citation: ZHANG Chi, HU Ruilai, XIANG Delong, ZHANG Hong, ZHANG Min. A Prediction Model for Operation Speed of Six-axis Articulated Trains in Uphill Sections of Expressways[J]. Journal of Transport Information and Safety, 2022, 40(4): 128-137. doi: 10.3963/j.jssn.1674-4861.2022.04.014
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A Prediction Model for Operation Speed of Six-axis Articulated Trains in Uphill Sections of Expressways

doi: 10.3963/j.jssn.1674-4861.2022.04.014
  • 1.

    School of Highway, Chang'an University, Xi'an 710064, China

  • 2.

    Research Center of Digital Construction and Management for Transport Infrastructure in Shanxi Province, Xi'an 710064, China

  • 3.

    Sichuan Highway Planning, Survey, Design and Research Institute Ltd, Chengdu 610041, China

  • Received Date: 2022-06-20
    Available Online: 2022-09-17
    Abstract
  • The prediction error of operation speed of 6-axis articulated trains in uphill sections of expressways is large, and its safe operation management is challenging. An Operation Speed Prediction Model (OSPM) is proposed to address this issue. Radars and AxleLight side lasers are used to collect the traffic flow data of six-axis articulated trains at five continuous uphill sections of an expressway from Southwest China. Moreover, the actual operation speed is compared with the results from an existing standard prediction model. Then the OSPM for six-axis articulated trains in uphill sections of expressways is developed by taking the gradient and the length of the uphill, the specific power, and the initial speed of the trains as variables. Lastly, an error correction method is developed, and effectiveness of the proposed method is analyzed. The main results are shown as follows: the average rate of prediction error of the standard model reaches 25.37%, and the prediction error is relatively significant. The operation speed is negatively correlated to the gradient and the length of uphill, while it is positively correlated to specific power of the train. The goodness of fit of multiple linear regression model R2 is 0.978, meeting the test requirement. The difference between the predicted speed and the actual speed is in the range of 2-4 km/h, and the average relative error is 8.86%, which is 16.51% less than the standard model. Considering the influences of traffic density, the revised model can reduce the error within 1 km/h; the average relative error is 1.08%, which is 7.78% less than the original model and 24.29% less than the standard model. These results reveal that the proposed OSPM can considerably improve the accuracy of the operation speed prediction.

     

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