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• W3113241502 abstract "To prevent and control public transport safety accidents in advance and guide the safety management and decision-making optimization of public transport vehicles, based on the forewarning and other multisource data of public transport vehicles in Zhenjiang, holographic portraits of public transport safety operation characteristics are constructed from the perspectives of time, space, and driver factors, and a prediction model of fatigue driving and driving risk of bus drivers based on BP neural network is constructed. Finally, model checking and virtual simulation experiments are carried out. The result of the research shows that the driver’s fatigue risk during the period of 7 : 00-8 : 00 am is much higher than other periods. When the bus speed is about 30 km/h, the driver fatigue forewarning events occur the most. Drivers aged 30–34 years have the largest proportion of vehicle abnormal forewarning, drivers aged 40–44 years have the largest proportion of fatigue forewarning events, and drivers with a driving experience of 15–19 years have the largest overall proportion of various forewarning events. When the vehicle speed range is (18, 20) km/h and (42, 45) km/h, the probability of fatigue driving risk and driving risk forewarning increases sharply; and when the vehicle speed is lower than 17 km/h or 41 km/h, the probability of fatigue driving risk and driving risk forewarning, respectively, is almost zero. The probability of fatigue forewarning during low peak hours on rainy days is about 30% lower than that during peak hours. The probability of driving forewarning during flat peak hours is 15% higher than that during low peak hours and about 10% lower than that during peak hours. This paper realized for the first time the use of real forewarning data of buses in the full time, the whole region, and full cycle to carry out research. Related results have important theoretical value and practical significance for scientifically guiding the safety operation and emergency management strategies of buses, improving the service level of bus passenger transportation capacity and safety operation, and promoting the safety, health, and sustainable development of the public transportation industry." @default.
• W3113241502 created "2020-12-21" @default.
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• W3113241502 date "2020-12-18" @default.
• W3113241502 modified "2023-10-13" @default.
• W3113241502 title "Research on Operation Characteristics and Safety Risk Forecast of Bus Driven by Multisource Forewarning Data" @default.
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• W3113241502 doi "https://doi.org/10.1155/2020/6623739" @default.
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