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A two-stage black-spot identification model for inland waterway transportation

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  • Zhang, Jinfen
  • Wan, Chengpeng
  • He, Anxin
  • Zhang, Di
  • Soares, C. Guedes

Abstract

Inland shipping plays a significant role in the integrated transport system. Maritime safety has been one of the top concerns due to its high-risk characteristics. The historical accident data is treated as a valuable source for identifying the riskiest waters (also called black-spots) where special attention is necessary. In view of this, a two-stage black-spot identification model is proposed in this paper to identify and locate waterways with higher accident rates. In stage 1, the dynamic segmentation and equivalent accident number methods are proposed to identify the preliminarily black-spots. In stage 2, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is introduced to pinpoint the precise locations of the detailed black-spots based on the results from the first step. The model is further applied to the Jiangsu section of the Yangtze River based on the historical accident data between 2012 and 2016. The results show that altogether 12 preliminary black-spots and 5 detailed black-spots are identified in the investigated waters. This research provides helpful reference for optimizing the allocations of search and rescue resource as well as differentiated safety management of black-spot waters.

Suggested Citation

  • Zhang, Jinfen & Wan, Chengpeng & He, Anxin & Zhang, Di & Soares, C. Guedes, 2021. "A two-stage black-spot identification model for inland waterway transportation," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:reensy:v:213:y:2021:i:c:s0951832021002155
    DOI: 10.1016/j.ress.2021.107677
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    References listed on IDEAS

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    Cited by:

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    2. Yang, Zhisen & Wan, Chengpeng & Yang, Zaili & Yu, Qing, 2021. "Using Bayesian network-based TOPSIS to aid dynamic port state control detention risk control decision," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    3. Rishuang Sun & Chi Zhang & Yujie Xiang & Lei Hou & Bo Wang, 2022. "Identification Method for Crash-Prone Sections of Mountain Highway under Complex Weather Conditions," Sustainability, MDPI, vol. 14(22), pages 1-16, November.
    4. Antão, P. & Sun, S. & Teixeira, A.P. & Guedes Soares, C., 2023. "Quantitative assessment of ship collision risk influencing factors from worldwide accident and fleet data," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    5. Puisa, Romanas & Montewka, Jakub & Krata, Przemyslaw, 2023. "A framework estimating the minimum sample size and margin of error for maritime quantitative risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    6. Liu, Zhichen & Li, Ying & Zhang, Zhaoyi & Yu, Wenbo, 2022. "A new evacuation accessibility analysis approach based on spatial information," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    7. Zhang, Mingyang & Zhang, Di & Fu, Shanshan & Kujala, Pentti & Hirdaris, Spyros, 2022. "A predictive analytics method for maritime traffic flow complexity estimation in inland waterways," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    8. Gao, Dawei & Zhu, Yongsheng & Guedes Soares, C., 2023. "Uncertainty modelling and dynamic risk assessment for long-sequence AIS trajectory based on multivariate Gaussian Process," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    9. Cai, Mingyou & Zhang, Jinfen & Zhang, Di & Yuan, Xiaoli & Soares, C. Guedes, 2021. "Collision risk analysis on ferry ships in Jiangsu Section of the Yangtze River based on AIS data," Reliability Engineering and System Safety, Elsevier, vol. 215(C).

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