A Case Study in China to Determine Whether GPS Data and Derivative Indicator Can Be Used to Identify Risky Drivers

Fu, Rui; Tong, Le; Yu-xi, Guo; Shi-wei, Zhang; Cheng, Wendong

doi:10.1155/2019/9072531

Cited by 4 publications

(3 citation statements)

References 30 publications

(39 reference statements)

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“…The key indicators that influence their accident risk are azimuthal change, acceleration dispersion, and average speed. This relationship has also been consistently found in the research [65,66]. This study confirmed the positive correlation between these three indicators and bus driver accidents.…”

Section: Discussionsupporting

confidence: 88%

Accident Probability Prediction and Analysis of Bus Drivers Based on Occupational Characteristics

Ding,

Yuan,

et al. 2023

Applied Sciences

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A city bus carries a large number of passengers, and any traffic accidents can lead to severe casualties and property losses. Hence, predicting the likelihood of accidents among bus drivers is paramount. This paper considered occupational driving characteristics such as cumulative driving duration, station entry and exit features, and peak driving times, and categorical boosting (CatBoost) was used to construct an accident probability prediction model. Its effectiveness was confirmed by the daily management data of a Chongqing bus company in June. For data processing, Multiple Imputation by Chained Equations for Random Forests (MICEForest) was used for data filling. In terms of prediction, a comparative analysis of four boosted trees revealed that CatBoost exhibited superior performance. To analyze the critical factors affecting the probability of bus driver accidents, SHapley Additive exPlanations (SHAP) was applied to visualize and interpret the results. In addition to the significant effects of age, rainfall, and azimuthal change, etc., we innovatively discovered that the proportion of driving duration during peak duration, the dispersion when entering and exiting stations, the proportion of driving duration within a week, and the accumulated driving duration of the previous week also had varying degrees of impact on accident probability. Our research and findings provide a new idea of accident prediction for professional drivers and direct theoretical support for the accident risk management of bus drivers.

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Section: Discussionsupporting

confidence: 88%

Accident Probability Prediction and Analysis of Bus Drivers Based on Occupational Characteristics

Ding,

Yuan,

et al. 2023

Applied Sciences

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“…All statistical analyses were conducted using SPSS Statistics software [32]. For the datafitting normal distribution, the analysis of variance (ANOVA) was used for the hypothesis test.…”

Section: Discussionmentioning

confidence: 99%

Effectiveness of Active Luminous Lane Markings on Highway at Night: A Driving Simulation Study

et al. 2021

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Road lane markings play an essential role in maintaining traffic order and improving traffic safety and efficiency. Active luminous lane markings have emerged with advances in technology recently. However, it is still not completely clear what impact their application will have on drivers. This paper aimed to study the effectiveness of active luminous lane markings on highways at night. A driving simulation experiment was carried out based on advanced driving simulators at Tongji University. The driving simulation experiment involved 31 participants and 9 simulation scenes with 6 different types of lane markings models and the same 2-way highway segment, which was 5300-m long with four 3.75-m wide driving lanes. The study participants drove through the simulated highway while the vehicle operation data and the driver’s eyes changing data were continuously captured. Overall, the pupil area change rate, steering wheel speed, brake pedal force, gas pedal, lane departure, and operating speed indicators were selected to evaluate the effectiveness of the active luminous lane markings. The results are shown as follows: (1) the active luminous lane markings have excellent visual recognition performance at night. Compared with the passive luminous lane markings, the active luminous markings can reduce the mental and physical loads of drivers, increase the early braking distance significantly, improve the lane-keeping ability and smooth the operating speed; (2) for the specific parameter settings of the active luminous lane markings at night, the yellow lane markings are better than the white ones, the point-line-type lane markings are superior to the conventional-type ones, and the blinking frequency is reasonable to set, at a moderate level, as 40 times per min. The results suggest that there are positive effects of active luminous lane markings on the promotion of highway traffic safety and efficiency at night, providing theoretical support for the popularization and application of active luminous road lane markings.

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“…To this end, the lane-changing data are classified into three categories according to the acceleration value of the FV when the SV starts to change lanes to verify the validity of the warning model. That is, the acceleration value of the FV less than −0.5 m/s 2 is used as the hazard perception threshold, and the lane-changing data are classified into the hazardous area [42,43]; data with slight braking (−0.5 < a F ≤ −0.15 m/s 2 ) is classified into the potential conflict area [44]; and data with an acceleration value above −0.15 m/s 2 is considered as safe lane-changing data and is classified into the safety area. The distribution of the acceleration of the FV when the SV starts to change lanes is shown in Figures 15 and 16.…”

Section: Dss =mentioning

confidence: 99%

Lane-Change Risk When the Subject Vehicle Is Faster Than the Following Vehicle: A Case Study on the Lane-Changing Warning Model Considering Different Driving Styles

Tong

Wang

et al. 2022

Sustainability

Self Cite

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The research of early warning and control strategy considering driving styles during lane changes is a hotspot in the field of automatic driving. However, many lane-changing studies only emphasize the warning analysis when the following vehicle is faster than the subject vehicle, while neglecting the potential risk when the subject vehicle is faster than the following vehicle in the adjacent lane during lane changes. To study the lane-changing characteristics of drivers considering driving styles and to establish a personalized lane-changing warning model under different relative speed conditions, fifty participants (three females and forty-seven males) were recruited to carry out a real road driving test. A novel Gaussian mixture model with the results of k-means clustering was established to classify driving styles based on two-dimensional variables: average time gap and average minimum time to collision. The clustering result was then verified. In addition, by analyzing the relationship between the subject vehicle and the following vehicle in the adjacent lane during lane changes, a lane-changing warning model considering driving styles under different relative speed conditions was established. Results show that the clustering algorithm proposed in this paper has high separability between samples, achieving a much softer clustering result that can provide a reference for the parameter setting of the personalized driver assistance system. Furthermore, the overall recognition accuracy of the hazardous lane-changing behaviors improved after drivers were classified into different driving styles. The established lane-changing warning model has a better recognition performance for aggressive drivers when compared with the other two driver types. The results provide a basis for the algorithm design of the intelligent lane-changing warning system and can improve the user acceptance of an advanced driver assistance system for self-driving vehicles.

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A Case Study in China to Determine Whether GPS Data and Derivative Indicator Can Be Used to Identify Risky Drivers

Cited by 4 publications

References 30 publications

Accident Probability Prediction and Analysis of Bus Drivers Based on Occupational Characteristics

Accident Probability Prediction and Analysis of Bus Drivers Based on Occupational Characteristics

Effectiveness of Active Luminous Lane Markings on Highway at Night: A Driving Simulation Study

Lane-Change Risk When the Subject Vehicle Is Faster Than the Following Vehicle: A Case Study on the Lane-Changing Warning Model Considering Different Driving Styles

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