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Identification of Critical Factors for Non-Recurrent Congestion Induced by Urban Freeway Crashes and Its Mitigating Strategies

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  • Younshik Chung

    (Department of Urban Planning and Engineering, Yeungnam University, Gyeongsan 38541, Korea)

Abstract

Given the extreme difficulty of estimating crash likelihoods, the most important aspect of the development of congestion management strategies is the identification of the factors that affect non-recurrent congestion caused by crashes. Such factors must be identified to develop crash management strategies and congestion management strategies. The objectives of this study are to identify causal factors that affect non-recurrent congestion and to propose some operational strategies for mitigating crash-induced non-recurrent traffic congestion. To achieve these objectives, a case study was conducted to identify spatiotemporal non-recurrent congestion regions using a previously developed method based on historical inductance loop detector data collected from six major freeways in Orange County, California. Based on the case study results, potentially significant factors in non-recurrent congestion were identified using the Cox proportional hazard model. Additionally, with the factors identified as significant, operational strategies were proposed for mitigating non-recurrent congestion due to freeway crashes.

Suggested Citation

  • Younshik Chung, 2017. "Identification of Critical Factors for Non-Recurrent Congestion Induced by Urban Freeway Crashes and Its Mitigating Strategies," Sustainability, MDPI, vol. 9(12), pages 1-14, December.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:12:p:2331-:d:122890
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    References listed on IDEAS

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    Cited by:

    1. Lian Zhu & Linjun Lu & Wenying Zhang & Yurou Zhao & Meining Song, 2019. "Analysis of Accident Severity for Curved Roadways Based on Bayesian Networks," Sustainability, MDPI, vol. 11(8), pages 1-17, April.
    2. Navin Ranjan & Sovit Bhandari & Pervez Khan & Youn-Sik Hong & Hoon Kim, 2021. "Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder," Sustainability, MDPI, vol. 13(9), pages 1-26, May.
    3. Shumin Bai & Xiaofeng Ji & Bingyou Dai & Yongming Pu & Wenwen Qin, 2022. "An Integrated Model for the Geohazard Accident Duration on a Regional Mountain Road Network Using Text Data," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    4. Siham G. Farrag & Fatma Outay & Ansar Ul-Haque Yasar & Moulay Youssef El-Hansali, 2020. "Evaluating Active Traffic Management (ATM) Strategies under Non-Recurring Congestion: Simulation-Based with Benefit Cost Analysis Case Study," Sustainability, MDPI, vol. 12(15), pages 1-15, July.

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