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Research on Operation Characteristics and Safety Risk Forecast of Bus Driven by Multisource Forewarning Data
Journal of Advanced Transportation ( IF 2.3 ) Pub Date : 2020-12-18 , DOI: 10.1155/2020/6623739
Shejun Deng 1 , Hongru Yu 1 , Caoye Lu 1
Affiliation  

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.

中文翻译:

多源预警数据驱动的公交车运行特性及安全风险预测研究

为了提前预防和控制公共交通安全事故,指导公共交通车辆的安全管理和决策优化,在镇江公共交通车辆的预警和其他多源数据的基础上,构建了公共交通安全运行特征的全息图。从时间,空间和驾驶员因素的角度出发,构建了基于BP神经网络的疲劳驾驶和公交驾驶员驾驶风险预测模型。最后,进行了模型检查和虚拟仿真实验。研究结果表明,在上午7:00-8:00期间驾驶员的疲劳风险要比其他时期高得多。当公交车速度约为30 km / h时,驾驶员疲劳预警事件最多发生。年龄在30-34岁的驾驶员具有最大的车辆异常预警比例,年龄在40-44岁的驾驶员具有最大的疲劳预警预警比例,并且具有15-19岁驾驶经验的驾驶员在各种预警预警中总体比例最高。当车速范围为(18,20)km / h和(42,45)km / h时,疲劳驾驶风险和预警驾驶风险的可能性急剧增加;当车速低于17 km / h或41 km / h时,疲劳驾驶风险和预警驾驶风险的概率分别几乎为零。在雨天的低高峰时段,疲劳预警的可能性比高峰时段低30%。在高峰时段,提前警告的概率比高峰时段低15%,比高峰时段低10%。本文首次实现了在整个时间,整个区域和整个周期内使用公共汽车的真实预警数据进行研究。相关结果对科学指导公交车安全运行和应急管理策略,提高公交客运能力和安全运营服务水平,促进公交行业安全健康和可持续发展具有重要的理论价值和实际意义。 。
更新日期:2020-12-18
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