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WANG Er-gen, SUN Jian. Merging influence factors recognition and behaviors prediction of on-ramp vehicles of urban expressway[J]. Journal of Traffic and Transportation Engineering, 2018, 18(3): 180-188. doi: 10.19818/j.cnki.1671-1637.2018.03.018
Citation: WANG Er-gen, SUN Jian. Merging influence factors recognition and behaviors prediction of on-ramp vehicles of urban expressway[J]. Journal of Traffic and Transportation Engineering, 2018, 18(3): 180-188. doi: 10.19818/j.cnki.1671-1637.2018.03.018
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Merging influence factors recognition and behaviors prediction of on-ramp vehicles of urban expressway

doi: 10.19818/j.cnki.1671-1637.2018.03.018
  • 1.

    Traffic Management Research Institute of the Ministry of Public Security, Wuxi 214151, Jiangsu, China

  • 2.

    College of Transportation Engineering, Tongji University, Shanghai 201804, China

More Information
  • Author Bio:

    WANG Er-gen(1991-), male, researchintern, ergen_jlu@163.com

  • Received Date: 2017-12-12
  • Publish Date: 2018-06-25
    Abstract
  • The vehicle trajectory data of bottleneck sections on US Highway 101 (US101) and Hongxu Road of Yan-an Expressway in Shanghai (SHHX) were analyzed, and the merging behaviors of on-ramp vehicles on urban expressway were studied.13 instant influence factors and 25 historical experienced influence factors of merging behaviors were considered. The random forests algorithm was used to sort the importances of 38 influence factors, and the key influence factors were identified. The merging behaviors prediction models based on the Bayesian network were established for two bottleneck sections, and the model prediction accuracy was evaluated. Analysis result shows that 14 key influence factors are obtained in the bottleneck sections US101 and SHHX, including 6 historical experienced influence factors. For the bottleneck sectionUS101, the historical experienced influence factors account for 45.45% of the whole key influence factors, while for SHHX, the proportion is 36.36%. Thus, the historical experienced influence factors have significant effect on the decision of final merging. The Bayesian network models that consider the historical experienced influence factors have higher prediction accuracy. The overall prediction accuracies of merging behaviors improve by 2.53% and 8.85% at US101 and SHHX, respectively. Without considering the historical experienced influence factors, the overall prediction accuracies of merging behaviors at US101 and SHHX are 87.94% and 73.17%, respectively. Under considering the historical experienced influence factors, the overall prediction accuracies of merging behaviors at US101 and SHHX are 90.47% and 82.02%, respectively. Additionally, the prediction models are not overly fitted. The difference between the prediction accuracies of merging and nonmerging events is within 1.2% using the testing datasets.

     

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