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Crash Risk Prediction Modeling Based on the Traffic Conflict Technique and a Microscopic Simulation for Freeway Interchange Merging Areas

Author

Listed:
  • Shen Li

    (School of Transportation, Southeast University, Si Pai Lou #2, Nanjing 210096, China)

  • Qiaojun Xiang

    (School of Transportation, Southeast University, Si Pai Lou #2, Nanjing 210096, China)

  • Yongfeng Ma

    (School of Transportation, Southeast University, Si Pai Lou #2, Nanjing 210096, China)

  • Xin Gu

    (School of Transportation, Southeast University, Si Pai Lou #2, Nanjing 210096, China)

  • Han Li

    (School of Transportation, Southeast University, Si Pai Lou #2, Nanjing 210096, China)

Abstract

This paper evaluates the traffic safety of freeway interchange merging areas based on the traffic conflict technique. The hourly composite risk indexes (HCRI) was defined. By the use of unmanned aerial vehicle (UAV) photography and video processing techniques, the conflict type and severity was judged. Time to collision (TTC) was determined with the traffic conflict evaluation index. Then, the TTC severity threshold was determined. Quantizing the weight of the conflict by direct losses of different severities of freeway traffic accidents, the calculated weight of the HCRI can be obtained. Calibration of the relevant parameters of the micro-simulation simulator VISSIM is conducted by the travel time according to the field data. Variables are placed into orthogonal tables at different levels. On the basis of this table, the trajectory file of every traffic condition is simulated, and then submitted into a surrogate safety assessment model (SSAM), identifying the number of hourly traffic conflicts in the merging area, a statistic of HCRI. Moreover, the multivariate linear regression model was presented and validated to study the relationship between HCRI and the influencing variables. A comparison between the HCRI model and the hourly conflicts ratio (HCR), without weight, shows that the HCRI model fitting degree was obviously higher than the HCR. This will be a reference to design and implement operational planners.

Suggested Citation

  • Shen Li & Qiaojun Xiang & Yongfeng Ma & Xin Gu & Han Li, 2016. "Crash Risk Prediction Modeling Based on the Traffic Conflict Technique and a Microscopic Simulation for Freeway Interchange Merging Areas," IJERPH, MDPI, vol. 13(11), pages 1-14, November.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:11:p:1157-:d:83267
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    References listed on IDEAS

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    1. Li Yuan & He-wei Yuan & Yong-feng Ma & Ying-wei Ren, 2014. "Development of a Safety Evaluation Model for Provincial Highway," Discrete Dynamics in Nature and Society, Hindawi, vol. 2014, pages 1-10, January.
    2. Sun, Jie & Li, Zhipeng & Sun, Jian, 2015. "Study on traffic characteristics for a typical expressway on-ramp bottleneck considering various merging behaviors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 440(C), pages 57-67.
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    Cited by:

    1. Cao, Jieyu & Chen, Junlan & Guo, Xiucheng & Wang, Ling, 2023. "Trajectory data-based severe conflict prediction for expressways under different traffic states," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 621(C).
    2. Maksymilian Mądziel, 2023. "Vehicle Emission Models and Traffic Simulators: A Review," Energies, MDPI, vol. 16(9), pages 1-31, May.
    3. Alex Pauwels & Nadia Pourmohammad-Zia & Frederik Schulte, 2022. "Safety and Sustainable Development of Automated Driving in Mixed-Traffic Urban Areas—Considering Vulnerable Road Users and Network Efficiency," Sustainability, MDPI, vol. 14(20), pages 1-23, October.
    4. Bong Gu Kang & Byeong Soo Kim, 2023. "A Study on Cognitive Error Validation for LED In-Ground Traffic Lights Using a Digital Twin and Virtual Environment," Mathematics, MDPI, vol. 11(17), pages 1-16, September.
    5. Pan Wang & Shunying Zhu & Xiaoyue Zhao, 2023. "Identification and Factor Analysis of Traffic Conflicts in the Merge Area of Freeway Work Zone," Sustainability, MDPI, vol. 15(14), pages 1-18, July.

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