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Jianfeng Xi
College of Transportation, Jilin University, Changchun 130022, China

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Journal article
Published: 02 August 2021 in Applied Sciences
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Whether in developing or developed countries, traffic accidents caused by freight vehicles are responsible for more than 10% of deaths of all traffic accidents. Fatigue driving is one of the main causes of freight vehicle accidents. Existing fatigue driving studies mostly use vehicle operating data from experiments or simulation data, exposing certain drawbacks in the validity and reliability of the models used. This study collected a large quantity of real driving data to extract sample data under different fatigue degrees. The parameters of vehicle operating data were selected based on significant driver fatigue degrees. The k-nearest neighbor algorithm was used to establish the detection model of fatigue driving behaviors, taking into account influence of the number of training samples and other parameters in the accuracy of fatigue driving behavior detection. With the collected operating data of 50 freight vehicles in the past month, the fatigue driving behavior detection models based on the k-nearest neighbor algorithm and the commonly used BP neural network proposed in this paper were tested, respectively. The analysis results showed that the accuracy of both models are 75.9%, but the fatigue driving detection model based on the k-nearest neighbor algorithm is more reliable.

ACS Style

Jianfeng Xi; Shiqing Wang; Tongqiang Ding; Jian Tian; Hui Shao; Xinning Miao. Detection Model on Fatigue Driving Behaviors Based on the Operating Parameters of Freight Vehicles. Applied Sciences 2021, 11, 7132 .

AMA Style

Jianfeng Xi, Shiqing Wang, Tongqiang Ding, Jian Tian, Hui Shao, Xinning Miao. Detection Model on Fatigue Driving Behaviors Based on the Operating Parameters of Freight Vehicles. Applied Sciences. 2021; 11 (15):7132.

Chicago/Turabian Style

Jianfeng Xi; Shiqing Wang; Tongqiang Ding; Jian Tian; Hui Shao; Xinning Miao. 2021. "Detection Model on Fatigue Driving Behaviors Based on the Operating Parameters of Freight Vehicles." Applied Sciences 11, no. 15: 7132.

Research article
Published: 26 May 2021 in Advances in Civil Engineering
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In order to comprehensively analyze the risk factors and accurately find the high risk factors related to accidents, an analysis model of risk factors of urban bus operation is proposed, in which the advantages of the structural analysis of the Fault Tree Analysis (FTA) and the correlation analysis of the Cumulative Logistic Regression (CLR) are combined. Firstly, based on the accident data in Northeast China, FTA is used to compile the urban bus operation fault tree. In the fault tree, 16 bus operation risk factors are classified, while the risk factors are sorted and compared from three aspects: structural importance, probability importance, and critical importance. And then, the 11 higher risk factors are selected according to the discriminant principle. Secondly, bus operation accidents are divided into fatal accidents, injury accidents, and major economic loss accidents. The CLR model is used to fit the much higher risk factors that lead to urban bus operation accidents from above 11 higher risk factors. Finally, the scientific rationality and applicability of the model are verified, through the goodness of fit test and the comparison test with the actual probability of occurrence.

ACS Style

Jianfeng Xi; Yunhe Zhao; Tongqiang Ding; Jian Tian; Lianjie Li. Analysis Model of Risk Factors of Urban Bus Operation Based on FTA-CLR. Advances in Civil Engineering 2021, 2021, 1 -8.

AMA Style

Jianfeng Xi, Yunhe Zhao, Tongqiang Ding, Jian Tian, Lianjie Li. Analysis Model of Risk Factors of Urban Bus Operation Based on FTA-CLR. Advances in Civil Engineering. 2021; 2021 ():1-8.

Chicago/Turabian Style

Jianfeng Xi; Yunhe Zhao; Tongqiang Ding; Jian Tian; Lianjie Li. 2021. "Analysis Model of Risk Factors of Urban Bus Operation Based on FTA-CLR." Advances in Civil Engineering 2021, no. : 1-8.

Journal article
Published: 21 July 2019 in Advances in Mechanical Engineering
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Rear-end collision accounts for the main type of traffic accidents occurring on the freeway. In order to extract the significant influence factors of rear-end collision on the freeway, this study utilized the data of freeway traffic accidents between 2010 and 2015 in China. First, based on quasi-induced exposure theory, the information of driver, vehicle, and road environment was analyzed. Gender, age, driving age, vehicle safety, load, weather, fatigue, driving speed, road alignment, accident time, and visibility were selected as the important factors that might affect rear-end collision. Second, based on logistic regression model, the influencing factors analysis model of freeway rear-end collision was established. In the regression analysis, the possible important factors selected were taken as the independent variables, and the accident responsibility was taken as the dependent variable. Then, the factors that had significant influence on rear-end collision were selected from candidate independent variables by stepwise regression method. Finally, the specific influence of driving age, load, weather, accident time, visibility, fatigue, and driving speed on rear-end collision occurring on the freeway was discussed. The analysis results were explained according to the odds ratio. The research results of this article can provide guidance for the prevention of rear-end collision on the freeway and theoretical support for the development of freeway early warning system.

ACS Style

Jianfeng Xi; Hongyu Guo; Jian Tian; Lisa Liu; Weifu Sun. Analysis of influencing factors for rear-end collision on the freeway. Advances in Mechanical Engineering 2019, 11, 1 .

AMA Style

Jianfeng Xi, Hongyu Guo, Jian Tian, Lisa Liu, Weifu Sun. Analysis of influencing factors for rear-end collision on the freeway. Advances in Mechanical Engineering. 2019; 11 (7):1.

Chicago/Turabian Style

Jianfeng Xi; Hongyu Guo; Jian Tian; Lisa Liu; Weifu Sun. 2019. "Analysis of influencing factors for rear-end collision on the freeway." Advances in Mechanical Engineering 11, no. 7: 1.

Research article
Published: 22 May 2019 in Advances in Mechanical Engineering
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A classification and recognition method for the severity of road traffic accident based on rough set theory and support vector machine was proposed in this article. Rough set theory was used to calculate the importance of attributes in human, vehicle, road, environment, and accident. On the basis of importance ranking, the factors affecting the severity of accident were extracted. Then, with the general accident and major accident as two classification labels, the classification and recognition model of the severity of road traffic accident was established by using support vector machine. The results show that the model could improve the recognition accuracy and reduce the computational workload. Moreover, it has the good ability in classification and recognition as well as generalization compared with the model using support vector machine alone.

ACS Style

Xi JianFeng; Guo Hongyu; Tian Jian; Lisa Liu; Liu Haizhu. A classification and recognition model for the severity of road traffic accident. Advances in Mechanical Engineering 2019, 11, 1 .

AMA Style

Xi JianFeng, Guo Hongyu, Tian Jian, Lisa Liu, Liu Haizhu. A classification and recognition model for the severity of road traffic accident. Advances in Mechanical Engineering. 2019; 11 (5):1.

Chicago/Turabian Style

Xi JianFeng; Guo Hongyu; Tian Jian; Lisa Liu; Liu Haizhu. 2019. "A classification and recognition model for the severity of road traffic accident." Advances in Mechanical Engineering 11, no. 5: 1.

Journal article
Published: 08 June 2015 in Information
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Intersection traffic delay research has traditionally placed greater emphasis on the study of through and left-turning vehicles than right-turning ones, which often renders existing methods or models inapplicable to intersections with heavy pedestrian and non-motorized traffic. In the meantime, there is also a need for understanding the relations between different types of delay and how they each contribute to the total delay of the entire intersection. In order to address these issues, this paper first examines models that focus on through and left-turn traffic delays, taking into account the presence of heavy mixed traffic flows that are prevalent in developing countries, then establishes a model for calculating right-turn traffic delay and, last, proposes an approach to analyzing how much each of the three types of traffic delay contributes to the total delay of the intersection, based on the application of shift-share analysis (SSA), which has been applied extensively in the field of economics.

ACS Style

Jianfeng Xi; Wei Li; Shengli Wang; Chuanjiu Wang. An Approach to an Intersection Traffic Delay Study Based on Shift-Share Analysis. Information 2015, 6, 246 -257.

AMA Style

Jianfeng Xi, Wei Li, Shengli Wang, Chuanjiu Wang. An Approach to an Intersection Traffic Delay Study Based on Shift-Share Analysis. Information. 2015; 6 (2):246-257.

Chicago/Turabian Style

Jianfeng Xi; Wei Li; Shengli Wang; Chuanjiu Wang. 2015. "An Approach to an Intersection Traffic Delay Study Based on Shift-Share Analysis." Information 6, no. 2: 246-257.