What factors predict drivers' self-reported lane change violation behavior at urban intersections? A study in China

Identification and screening of key traffic violations: based on the perspective of expressing driver's accident risk

Drawing on the core idea of Propensity Score Matching, this study proposes a new concept named Historical Traffic Violation Propensity to describe the driver's historical traffic violations, and combines the new concept with an improved mutual information-based feature selection algorithm to construct a method for screening key traffic violations from the perspective of expressing driver's accident risk. The validation analysis based on the real data collected in Shenzhen demonstrated that drivers' state of Historical Traffic Violation Propensity on 19 key traffic violations screened have a stronger predictive ability of their subsequent accidents compared to the level in existing research. The positive state of Historical Traffic Violation Propensity on 'Drinking', 'Parking in dangerous areas', 'Wrong use of turn lights', 'Violating prohibited and restricted traffic regulations', and 'Disobeying prohibition sign' will increase the probability of a driver's subsequent accident by more than 1.7 times. The research provides directions to more efficiently and accurately capture the driver's accident risk through historical traffic violations, which is valuable for identifying high-risk drivers as well as the key psychological or physical risk factors that manifest in daily driving activities and lead to subsequent accidents.

Effects of the theory of planned behavior and nudge strategy-based intervention on the adherence to anticoagulation treatment in patients with non-valvular atrial fibrillation

BACKGROUND

Non-valvular atrial fibrillation is associated with the incidence of thromboembolism. Current guidelines recommend preferential use of novel oral anticoagulants(NOAC) in patients with nonvalvular atrial fibrillation. Oral anticoagulation medication adherence rate was relatively low among discharged patients with non-valvular atrial fibrillation.

AIM

To investigate the effects of the anticoagulation programs based on the theory of planned behavior and nudge strategy among patients with non-valvular atrial fibrillation.

METHODS

130 patients with non-valvular atrial fibrillation were randomized to the intervention group or control group, 72 patients in the intervention group, and 58 patients in the control group with a 6-month follow-up. Medication adherence,intention,attitude, perceived behavioral control and subjective norm and quality of life were assessed.

RESULTS

There were significantly differences in the attitude and subjective norm between the two groups at one month,three months and six months follow-up(P <0.01).There were significantly differences in the perceived behavioral control between the two groups at six months follow-up(P <0.01).There were significantly differences in the intention scale between the two groups at three months follow-up(P <0.01). The medication adherence scale score was higher in the intervention group than in the control group at six months follow-up.However, there were no differences in quality of life between the two groups at six months follow-up.

CONCLUSIONS

The program based on the theory of planned behavior and nudge strategy can improve medication adherence in patients with non-valvular atrial fibrillation.

The extended theory of planned behavior considering heterogeneity under a connected vehicle environment: A case of uncontrolled non-signalized intersections

With the emergence of connected vehicle (CV) technology, there is a doubt whether CVs can improve driver intentions and behaviors, and thus protect them from accidents with the provision of real-time information. In order to understand the possible impacts of the real-time information provided by CV technology on drivers, this paper aims to develop a model which considers the heterogeneity between drivers with the aid of the extended theory of planned behavior. At the uncontrolled non-signalized intersections, a stated preference (SP) questionnaire survey was conducted to build the dataset consisting of 1001 drivers. Based on the collected dataset, the proposed model examines the relationships between subjective norms, attitudes, risk perceptions, perceived behavioral control and driving intentions, and studies how such driving intentions are simultaneously related to driver characteristics and experiences in the CV environment. Furthermore, driver groups which are homogenous with respect to personality traits are formed, and then are employed to analyze the heterogeneity in responses to driving intentions. Four key findings are obtained when analyzing driver responses to the real-time information provided by CV technology: 1) the proposed H-ETPB model is verified with a good fitness measure; 2) irrespective to driver personality traits, attitudes and perceived behavioral control have a direct and indirect association with driving intentions to accelerate; 3) driving intentions of high-neurotic drivers to accelerate are significantly related to subjective norms, while that of low-neurotic drivers are not; 4) elder high-neurotic drivers, and low-neurotic drivers who have unstable salaries or ever joined in online car hailing service have a strong intention in accelerating. The findings of this study could provide the theoretical framework to optimize traffic performance and information design, as well as provide in-vehicle personalized information service in the CV and CAV environments and assist traffic authorities to design the most acceptable traffic rules for different drivers at an uncontrolled non-signalized intersection.

Structural Equation Modeling of Drivers' Situation Awareness Considering Road and Driver Factors

Driver’s situation awareness (SA) is one of the key elements that affect driving decision-making and driving behavior. SA is influenced by many factors, and previous studies have focused only on individual factors. This study presents a comprehensive study to explore the path relationships and influence mechanism between SA and all influential factors, including road characteristics, driver characteristics and states, distracting elements, and cognitive ability. A structural equation model that relates SA to its influential factors is developed. A total of 324 valid questionnaires were collected to analyze and identify the relationships between the factors. The results show that the preceding influential factors have significant effects on SA, which is consistent with previous research. Based on path coefficients, positive effects were: cognitive abilities (0.500), driver state (0.360), age (0.277), driving experience (0.198), and gender (0.156). Negative effects were: distracting elements (−0.253) and road characteristics (−0.213). The results of this comprehensive study provide a valuable reference for the development of driver training programs and driving regulations.

Predicting Errors, Violations, and Safety Participation Behavior at Nuclear Power Plants

Commissioning workers at nuclear power plants have long been ignored in previous studies, although their performance is closely related to the overall safety of plants. This study aimed to explain and predict three types of behavior, i.e., errors, violations, and safety participation, of commissioning workers, under the general framework of the theory of planned behavior (TPB) and by considering organization and planning factors. The validity of the model was evaluated with a sample of 167 commissioning workers who completed a self-reported questionnaire. The results showed that perceived behavioral control, along with organization and planning, significantly affected all types of behavior. It was also found that violations and errors were a direct result of attitude. Besides, errors were predicted by subjective norm; unexpectedly, this occurred in a positive way. These findings revealed the underlying mechanisms for the development of errors, violations, and safety participation among commissioning workers and provided practical implications for safety improvement at the commissioning workplace.

Analysis of the influencing factors of the unsafe driving behaviors of online car-hailing drivers in china

Online car-hailing drivers are a special group between professional drivers and private car drivers. The paper built the unsafe driving behavior model of online car-hailing drivers based on the theory of planned behavior (TPB), explored the socio-psychological factors underlying drivers’ motivation for unsafe driving behavior and examined how these factors predict their behaviors. 239 online car-hailing drivers were surveyed with a self-reported questionnaire. Factors analysis proved the TPB questionnaire to be valid and reliable. Structural equation modeling showed that attitude to behavior (0.18), subjective norm(0.39) significantly influenced drivers' behavioral intention, perceived behavioral control (0.27) could both affected drivers' behavioral intention (0.27) and behavior(0.21),behavioral intention was the most direct and important predictor of behavior. This study provided a valuable contribution to designing more effective interventions to improve driving safety of online car-hailing drivers.

Investigating influence factors of traffic violations at signalized intersections using data gathered from traffic enforcement camera

To effectively reduce traffic violations that often cause severe crashes at signalized intersections, exploring their contributing factors seems hugely urgent and essential. This study attempted to investigate the influence factors of wrong-way driving (WWD), red-light-running (RLR), violating traffic markings (VTM), and driving in the inaccurate oriented lane (DIOL) at signalized intersections by using data collected from traffic enforcement camera in Hohhot, China. To this end, an ordinary multinomial logit model was developed. By considering the unobserved heterogeneity between observations, a random effects multinomial logit model was proposed as well. After that, the marginal effects of explanatory variables were computed. The outcomes showed that non-local vehicles were more likely to commit WWD and VTM than local vehicles. WWD and RLR frequently occurred in the daytime and evening (6:00–23:59), and on most days within a week. RLR and DIOL mainly happened in June and July. The left-turn lane ratio significantly increased RLR and DIOL. The cloudy, partly cloudy, and rainy days obviously increased WWD and VTM. The temperature from 21 to 30 degrees centigrade was apparently associated with the higher likelihoods of RLR and DIOL. According to the findings of this study, some intervention measures, targeting different vehicle types and considering temporal factors, road, and weather conditions, were recommended to reduce WWD, RLR, VTM, and DIOL at signalized intersections.