A traffic dynamic operation risk assessment method using driving behaviors and traffic flow Data: An empirical analysis

Yang, Haiyi; Zhao, Xiaohua; Luan, Sen; Chai, Shushan

doi:10.1016/j.eswa.2024.123619

Cited by 4 publications

(1 citation statement)

References 44 publications

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“…Karatzetzou et al [27] used the multi-hazard risk-assessment model to analyze probabilities in transportation networks, while Chakrabarti and Parikh [28] evaluated risk in hazmat transportation using the HAZAN methodology. Additionally, Yang et al [29] innovatively develop Non-Negative Matrix Factorization (NMF) to explore the quantitative risk under the coupling of multiple risk factors for traffic safety evaluation. Finally, Ambituuni et al [30] developed a framework for regulatory improvement to assess risk in the context of transportation by road.…”

Section: Literature Reviewmentioning

confidence: 99%

Risk Coupling Assessment of Vehicle Scheduling for Shipyard in a Complicated Road Environment

Wang,

Yin,

Khan

2024

JMSE

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Vehicle scheduling at shipyards can involve delays due to numerous risk factors encountered in the complicated shipyard road environment. This paper studies the problems of risk coupling in shipyard vehicle scheduling based on the risk matrix approach, considering the complicated road environment, assessing the degrees of coupling and disorder. Based on safety-engineering theory and comprehensive analysis of the road environment, four key criteria are identified, vehicles, the road environment, the working environment, and humans, including 12 factors and their specific contents. The degree of coupling between various combinations of risk criteria is quantitatively determined utilizing the N-K model. Additionally, the degree of disorder in the risk criteria is assessed based on information entropy theory. The model’s correction coefficients are determined through comparative analysis of experimental data. By integrating the degree of coupling and disorder, delays caused by different combinations of risk criteria in scheduling tasks are computed. The quantitative evaluation model enables accurate appraisal of risk events during shipyard vehicle scheduling. The model provides a valuable managerial tool to analyze delays caused when specific risk criteria are met and to compare these delays to the potential impact on time resulting from adjusting vehicle scheduling plans. This research has significant implications for enhancing vehicle distribution efficiency in shipyards.

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