Driving Risk Assessment in Work Zones Using Cloud Model

• Published in: Mathematical problems in engineering 2018-01, Vol.2018 (2018), p.1-9
• Main Authors: Zhang, Min, Ma, Xiongying, Zhang, Hong, Zhang, Chi, Wang, Shiwei
• Format: Article
• Language: English
• Subjects: Activation; Algorithms; Artificial intelligence; Clouds; Coefficient of variation; Computer simulation; Deceleration; Drivers; Driving conditions; Earthquake damage; Earthquakes; Highway construction; Highways; Methods; Principal components analysis; R&D; Research & development; Risk assessment; Roads & highways; Seismic engineering; System theory; Traffic accidents; Traffic accidents & safety; Traffic flow
• ISSN: 1024-123X; 1563-5147
• DOI: 10.1155/2018/8759580

Work zones are prone to traffic accidents. They are considered as dangerous parts of expressways not only for drivers but also for highway construction workers, as they face a higher risk of traffic accidents in work zones. In order to identify the driving risks and to provide guidance to detect traffic risks in work zone, a comprehensive risk assessment method based on cloud model is developed to examine the driving risks in work zones. The proposed model relies on three parameters to determine the driving risks in work zones, namely, coefficient of variation of speed, deceleration, and minimum safety distance. VISSIM simulation software is used as a tool to construct the work zone driving conditions and the reverse cloud model is used to divide the concept and concept jump. The maximum activation intensity is considered as the base factor to determine the core risk level. Other activation intensities are used as a basis to optimize the edge level effect and generate a comprehensive function. The reconstruction area in Anhui Province is used as a case study to assess the driving risks in three expressway work zones. The results revealed that the risk scores of the three work zones 1, 2, and 3 are 48.48, 62.49, and 34.33, respectively. The results obtained by the developed driving risk assessment model are in good agreement with the experimental results. Hence, the model proposed in this paper can accurately assess the driving risks in work zones using a more scientific and intuitive approach, which provides an excellent tool to design safe expressway work zones.