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Quantitative causality assessment between traffic states and crash risk in freeway segments with closely spaced entrance and exit ramps

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  • Zhao, Jingya
  • Liu, Qingchao

Abstract

Segments with closely spaced entrance and exit ramps are recognized as crash black-pots on freeways. Accurately identifying hazardous traffic states before crash occurrence can help implement targeted control measures, thereby reducing crash risk in such segments. As such, this study developed a structural causal model (SCM) to explore the causality between traffic states and crash risk in segments with closely spaced entrance and exit ramps. Firstly, high-resolution traffic flow data related to three types of lane configurations and three crash types were collected. A deep clustering based on generative adversarial networks (clusterGAN) was developed to classify traffic flow into seven states. Secondly, the causal graph connecting traffic states and crash risk was generated. Backdoor adjustment from the SCM was used to identify seven confounding variables (e.g. traffic volume, ramp volume ratio, speed difference on the inside lanes between the beginning and end of the segment, the shoulder and median width, etc.), which influence both traffic states and crash risk. The inverse-probability-weighted regression adjustment estimator was adopted to estimate the average causal effects of traffic states on crash risk conditional on different lane configurations. The results suggest that hazardous traffic states in each lane configuration are different, even for the same crash types. In addition, the comparison results with the standard logistic models suggest that ignoring confounding effects may cause unreasonable conclusions and biased estimation. These results have the potential to help advanced traffic management systems develop proactive traffic control strategies to improve traffic safety in freeway segments with closely spaced entrance and exit ramps.

Suggested Citation

  • Zhao, Jingya & Liu, Qingchao, 2024. "Quantitative causality assessment between traffic states and crash risk in freeway segments with closely spaced entrance and exit ramps," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
  • Handle: RePEc:eee:phsmap:v:637:y:2024:i:c:s0378437124000955
    DOI: 10.1016/j.physa.2024.129587
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    References listed on IDEAS

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    1. Xu, Chengcheng & Liu, Pan & Wang, Wei & Li, Zhibin, 2014. "Identification of freeway crash-prone traffic conditions for traffic flow at different levels of service," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 58-70.
    2. Wunsch, Guillaume & Mouchart, Michel & Russo, Federica, 2019. "Examining Cause-Effect Relations in the Social Sciences : A Structural Causal Modelling Approach," LIDAM Reprints ISBA 2019049, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Wu, Ning, 2002. "A new approach for modeling of Fundamental Diagrams," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(10), pages 867-884, December.
    4. Dongye Sun & Yunfei Ai & Yunhua Sun & Liping Zhao, 2020. "A highway crash risk assessment method based on traffic safety state division," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-14, January.
    5. Yang, Yang & He, Kun & Wang, Yun-peng & Yuan, Zhen-zhou & Yin, Yong-hao & Guo, Man-ze, 2022. "Identification of dynamic traffic crash risk for cross-area freeways based on statistical and machine learning methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    6. Golob, Thomas F. & Recker, Wilfred W. & Alvarez, Veronica M., 2004. "Safety aspects of freeway weaving sections," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(1), pages 35-51, January.
    7. Federica Russo & Guillaume Wunsch & Michel Mouchart, 2019. "Causality in the Social Sciences: a structural modelling framework," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2575-2588, September.
    8. Wunsch, Guillaume & Mouchart, Michel & Russo, Federica, 2019. "Examining Cause-Effect Relations in the Social Sciences A Structural Causal Modelling Approach," LIDAM Discussion Papers ISBA 2019002, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
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