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.
Highlights
IntroductionAt present, China mainly evaluates the safety of buses based on the incidence of traffic accidents. e evaluation indicators and analysis methods are relatively single, and there is still a lack of accurate control, effective prevention, and emergency management countermeasures
By acquiring the historical data of vehicle forewarning of Zhenjiang Public Transport Company in Jiangsu Province of China, 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. is paper excavates the general rules and main hidden dangers of vehicle forewarning events and carries out objective analysis and situation prediction of bus operation risks [2]
Is paper will overcome the shortcomings of the existing research, make full use of the safety forewarning system installed on public vehicles, obtain the real mass historical data of all kinds of public transport forewarning, carry out model construction and simulation analysis, provide auxiliary decision-making for bus operation, dispatching, and safety management, and promote the healthy, green, and sustainable development of urban public transportation [36]
Summary
China mainly evaluates the safety of buses based on the incidence of traffic accidents. e evaluation indicators and analysis methods are relatively single, and there is still a lack of accurate control, effective prevention, and emergency management countermeasures. Is paper will overcome the shortcomings of the existing research, make full use of the safety forewarning system installed on public vehicles, obtain the real mass historical data of all kinds of public transport forewarning, carry out model construction and simulation analysis, provide auxiliary decision-making for bus operation, dispatching, and safety management, and promote the healthy, green, and sustainable development of urban public transportation [36]. Data Acquisition Process e bus forewarning system installed by Zhenjiang Public Transport Company integrates various technologies such as ADAS yaw forewarning [37], fatigue driving video analysis, and BDS terminal [38] It can realize the real-time upload of vehicle operating data and ensure the accuracy and reliability of the data. Data platforms mainly include current online, forward forewarning, driver forewarning, the total number of abnormalities, vehicle distribution, forewarning type distribution, forewarning occurrence trend, and other data. is paper obtained 297,189 forewarning data from November 2019 to March 2020 through the forewarning platform system of Zhenjiang Public Transport Company. e original forewarning data mainly include information such as license plate number, forewarning time, forewarning type, forewarning level, forewarning speed, latitude and longitude coordinates of forewarning points, location of forewarning points, driver names, and other information
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
