Research on Operation Characteristics and Safety Risk Forecast of Bus Driven by Multisource Forewarning Data
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...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/6623739 |
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| author | Shejun Deng Hongru Yu Caoye Lu |
| author_facet | Shejun Deng Hongru Yu Caoye Lu |
| author_sort | Shejun Deng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-786cf0ce22644a9e9b848e8f5835f9ef |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-786cf0ce22644a9e9b848e8f5835f9ef2025-02-03T01:04:02ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/66237396623739Research on Operation Characteristics and Safety Risk Forecast of Bus Driven by Multisource Forewarning DataShejun Deng0Hongru Yu1Caoye Lu2College of Civil Science and Engineering, Yangzhou University, YangZhou, JiangSu 225009, ChinaCollege of Civil Science and Engineering, Yangzhou University, YangZhou, JiangSu 225009, ChinaCollege of Civil Science and Engineering, Yangzhou University, YangZhou, JiangSu 225009, ChinaTo 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.http://dx.doi.org/10.1155/2020/6623739 |
| spellingShingle | Shejun Deng Hongru Yu Caoye Lu Research on Operation Characteristics and Safety Risk Forecast of Bus Driven by Multisource Forewarning Data Journal of Advanced Transportation |
| title | Research on Operation Characteristics and Safety Risk Forecast of Bus Driven by Multisource Forewarning Data |
| title_full | Research on Operation Characteristics and Safety Risk Forecast of Bus Driven by Multisource Forewarning Data |
| title_fullStr | Research on Operation Characteristics and Safety Risk Forecast of Bus Driven by Multisource Forewarning Data |
| title_full_unstemmed | Research on Operation Characteristics and Safety Risk Forecast of Bus Driven by Multisource Forewarning Data |
| title_short | Research on Operation Characteristics and Safety Risk Forecast of Bus Driven by Multisource Forewarning Data |
| title_sort | research on operation characteristics and safety risk forecast of bus driven by multisource forewarning data |
| url | http://dx.doi.org/10.1155/2020/6623739 |
| work_keys_str_mv | AT shejundeng researchonoperationcharacteristicsandsafetyriskforecastofbusdrivenbymultisourceforewarningdata AT hongruyu researchonoperationcharacteristicsandsafetyriskforecastofbusdrivenbymultisourceforewarningdata AT caoyelu researchonoperationcharacteristicsandsafetyriskforecastofbusdrivenbymultisourceforewarningdata |