Focusing on appraisals: how and why anger and fear influence driving risk perception

Research on Active Safety Situation of Road Passenger Transportation Enterprises: Evaluation, Prediction, and Analysis

The road passenger transportation enterprise is a complex system, requiring a clear understanding of their active safety situation (ASS), trends, and influencing factors. This facilitates transportation authorities to promptly receive signals and take effective measures. Through exploratory factor analysis and confirmatory factor analysis, we delved into potential factors for evaluating ASS and extracted an ASS index. To predict obtaining a higher ASS information rate, we compared multiple time series models, including GRU (gated recurrent unit), LSTM (long short-term memory), ARIMA, Prophet, Conv_LSTM, and TCN (temporal convolutional network). This paper proposed the WDA-DBN (water drop algorithm-Deep Belief Network) model and employed DEEPSHAP to identify factors with higher ASS information content. TCN and GRU performed well in the prediction. Compared to the other models, WDA-DBN exhibited the best performance in terms of MSE and MAE. Overall, deep learning models outperform econometric models in terms of information processing. The total time spent processing alarms positively influences ASS, while variables such as fatigue driving occurrences, abnormal driving occurrences, and nighttime driving alarm occurrences have a negative impact on ASS.

When Artificial Intelligence Voices Human Concerns: The Paradoxical Effects of AI Voice on Climate Risk Perception and Pro-Environmental Behavioral Intention

Artificial intelligence (AI)-enabled text-to-speech transformation has been widely employed to deliver online information in various fields. However, few studies have investigated the effect of the AI voice in environmental risk communication, especially in the field of climate change, an issue that poses a severe threat to global public health. To address this gap, the current study examines how the AI voice impacts the persuasive outcome of climate-related information and the potential mechanism that underlies this process. Based on the social and affect heuristics of voice, we propose a serial mediation model to test the effect of climate-related information delivered by different voice types (AI voice vs. human voice) in eliciting risk perception and motivating pro-environmental behavioral intention. Through an online auditory experiment (N = 397), we found the following. First, the AI voice was as effective as the human voice in eliciting risk perception and motivating pro-environmental behavioral intention. Second, compared with human voice, the AI voice yielded a listener's lower level of perceived identity oneness with the speaker, which decreased risk perception and subsequently inhibited pro-environmental behavioral intention. Third, compared with human voice, the AI voice produced a higher level of auditory fear, which increased risk perception and thereby led to stronger pro-environmental behavioral intention. The paradoxical role of the AI voice and its wise use in environmental risk communication for promoting global public health are discussed.

Interplay between Discrete Emotions and Preventive Behavior in Health Crises: Big Data Analysis of COVID-19

Understanding the interplay between discrete emotions and COVID-19 prevention behaviors will help healthcare professionals and providers to implement effective risk communication and effective risk decision making. This study analyzes data related to COVID-19 posted by the American public on Twitter and identifies three discrete negative emotions (anger, anxiety, and sadness) of the public from massive text data. Next, econometric analyses (i.e., the Granger causality test and impulse response functions) are performed to evaluate the interplay between discrete emotions and preventive behavior based on emotional time series and Google Shopping Trends time series, representing public preventive behavior. Based on the textual analysis of tweets from the United States, the following conclusions are drawn: Anger is a Granger cause of preventive behavior and has a slightly negative effect on the public's preventive behavior. Anxiety is a Granger cause of preventive behavior and has a positive effect on preventive behavior. Furthermore, preventive behavior is a Granger cause of anxiety and has a negative and lagging effect on anxiety. Exploring how discrete emotions, such as anger and anxiety, affect preventive behaviors will effectively demonstrate how discrete emotions play qualitatively different roles in promoting preventive behaviors. Moreover, understanding the impact of preventive behaviors on discrete emotions is useful for better risk communication.

The Novelty Appraisal of the Feeling of Risk in Vehicles

Nowadays, vehicle assistance systems may assess the risks of the traffic situation with the help of advanced sensor technology and optimized algorithms. However, the passengers' feelings of risk in the vehicle have been mostly neglected. According to the Component Process Model of emotions, during the feeling of risk, novelty is one of the relevant event appraisals leading to particular physiological and facial responses. In order to identify whether or not indicators for novelty appraisal may be used for detecting the feeling of risk of vehicle occupants, we investigated physiological responses and facial expressions of individuals experiencing the feeling of risk with different levels of novelty. This secondary analysis of an earlier simulator study revealed that pupil diameter amplitude, skin conductance level changes, and changes in and amplitude of activity in facial expressions (the inner and outer brow raiser, brow lowerer, upper lid raiser and lid tightener) were correlated with the reduction in the novelty, suggesting that they could indicate the novelty of the feeling of risk of vehicle occupants. Hence, this research provides evidence for the novelty appraisal of the feeling of risk. Furthermore, it informs research on affect-aware systems to identify and reduce the feeling of risk of vehicle occupants in order to help to keep trust in automated vehicles high.

Law Enforcement Officers’ Ability to Recognize Emotions: The Role of Personality Traits and Basic Needs’ Satisfaction

Background: This study intended to explore the role of personality traits and basic psychological needs in law enforcement officers’ ability to recognize emotions: anger, joy, sadness, fear, surprise, disgust, and neutral. It was significant to analyze law enforcement officers’ emotion recognition and the contributing factors, as this field has been under-researched despite increased excessive force use by officers in many countries. Methods: This study applied the Big Five–2 (BFI-2), the Basic Psychological Needs Satisfaction and Frustration Scale (BPNSFS), and the Karolinska Directed Emotional Faces set of stimuli (KDEF). The data was gathered using an online questionnaire provided directly to law enforcement agencies. A total of 154 law enforcement officers participated in the study, 50.65% were females, and 49.35% were males. The mean age was 41.2 (age range = 22–61). In order to analyze the data, SEM and multiple linear regression methods were used. Results: This study analyzed variables of motion recognition, personality traits, and needs satisfaction and confirmed that law enforcement officers’ personality traits play a significant role in emotion recognition. Respondents’ agreeableness significantly predicted increased overall emotion recognition; conscientiousness predicted increased anger recognition; joy recognition was significantly predicted by extraversion, neuroticism, and agreeableness. This study also confirmed that law enforcement officers’ basic psychological needs satisfaction/frustration play a significant role in emotion recognition. Respondents’ relatedness satisfaction significantly predicted increased overall emotion recognition, fear recognition, joy recognition, and sadness recognition. Relatedness frustration significantly predicted decreased anger recognition, surprise recognition, and neutral face recognition. Furthermore, this study confirmed links between law enforcement officers’ personality traits, satisfaction/frustration of basic psychological needs, and emotion recognition, χ2 = 57.924; df = 41; p = 0.042; TLI = 0.929; CFI = 0.956; RMSEA = 0.042 [0.009–0.065]. Discussion: The findings suggested that agreeableness, conscientiousness, extraversion, and neuroticism play an essential role in satisfaction and frustration of relatedness needs, which, subsequently, link to emotion recognition. Due to the relatively small sample size, the issues of validity/reliability of some instruments, and other limitations, the results of this study should preferably be regarded with concern.

A novel hazard avoidance model based on young drivers' characteristics: A driving simulator study

This paper aims to examine the key factors influencing young drivers' hazard avoidance in Iran. In this study, a hazard avoidance index is defined and calculated for investigating the effect of impulsiveness, behavioral, and psychological characteristics on driving hazard avoidance. A questionnaire and go-no-go and flanker computer tests as well as a driving simulator are used for collecting data from 173 Iranian drivers under the age of 30. For this purpose, different scenarios were used, such as pedestrian crossing, the presence of fixed obstacles, and animals crossing the road, whose various characteristics are also changed. Structural equation modeling and the partial least squares method are applied for investigating the relationship between different variables and the hazard avoidance index. The results show that the driver hazard avoidance index is mostly affected by variables such as inhibitory control, opposite directional response, attentional impulsiveness, motion impulsiveness, non-planning impulsiveness, disregarding the law, and lapses and error. The results could help policymakers to assess diving hazard avoidance and improve training programs related to traffic safety based on the variables that are more effective on inexperienced and novice drivers' hazard avoidance.

Age Differences in Hazard Perception of Drivers: The Roles of Emotion

With the increasingly powerful functions of vehicle-mounted entertainment facilities, people (especially young drivers) like to listen to music while driving to render different atmospheres and emotions. However, emotions are important factors affecting drivers’ decisions, behavior and may reduce drivers’ hazard perception (HP), even promote dangerous driving behaviors of drivers. The purpose of this study is to explore the young and elderly drivers in assessing the HP difference under different emotional states. We conducted a 3 × 2 mixed experimental design with emotion as a within-participants variable and age as a between-participants factor. A sample of 14 young drivers (mean age = 22.21, SD = 1.05) and 13 elderly drivers (mean age = 54.08, SD = 2.72) completed the HP self-assessment of road traffic warning signs under negative emotion, neutral emotion, and positive emotion, randomly. The results showed that the young had the highest self-assessment HP under the negative emotion arousal condition, while the old had the highest self-assessment HP under the positive emotion arousal condition. In addition, When both groups were in a positive arousal state, the older group perceived more hazards than the young group. The results could help designers create driving emotions suitable for different driver groups, thus improving their perception of hazards and reducing risky driving.

The effect of anger on pedestrian avoidance in a simulated driving task

Studies of the influence of emotions on driving behaviour have produced contradictory conclusions. This confusion is related to two factors: emotional arousal and driving tasks. The purpose of this study is to explore the effects of anger and happiness on the driving behaviour of drivers who encounter a pedestrian-crossing event on an unmarked road, which requires strategic and behavioural choices. Thirty-nine drivers completed a simulated driving task to avoid pedestrians under the influence of state emotion. The results showed that anger increased the average driving speed, the minimum speed when encountering a pedestrian, the probability of passing in front of a pedestrian, and the lateral distance to the pedestrian from the right. However, there was no difference between the impacts of happy and neutral moods on driving behaviour. These results suggest that general risky driving behaviour (e.g., speeding) is mainly affected by anger state. Meanwhile avoidance behaviour patterns in pedestrian-crossing tasks, as a driving behaviour related to prosocial attitudes, are also affected by emotional valence. Recommendations and implications for further research on driving anger are discussed.

It's not a hard and fast rule: A qualitative investigation into factors influencing speeding among young drivers

INTRODUCTION

Research demonstrates that young drivers exceed the posted speed limit more frequently than older drivers, and this demographic is also subsequently overrepresented in road crash statistics. This behavior remains a worldwide problem despite legal enforcement efforts that have proven to have limited efficiency and/or produce counterintuitive results.

METHOD

Consequently, this study undertook a qualitative analysis in order to understand the factors that are perceived to both prevent and promote speeding behavior among young drivers, guided by self-determination theory (SDT). Focus groups were conducted with a total of 60 young drivers aged 17-25 years. An inductive thematic analysis approach was taken to analyze the data.

RESULTS

Findings revealed the following factors influence the prevention of speeding: (1) legal consequences were perceived to deter low-level speeding, (2) fear of injury was believed to prevent high-level speeding, and (3) speed awareness monitors were considered to be an effective countermeasure. Meanwhile, factors perceived to contribute to violating speed restrictions included: (1) perceiving it is safe to do so, (2) a perceived norm to speed, (3) emotions, and (4) unintentional speeding. According to SDT, the factors that were perceived to prevent speeding promote externalized self-regulatory processes and are therefore limited by short-term behavior change.

PRACTICAL APPLICATIONS

The findings have important implications for designing countermeasures aimed at young drivers, not least illuminating the need to have education and media campaigns that target and build drivers' perceived competence, autonomy, and relatedness in relation to speeding in order to promote longer term compliance.

Deterrence and defiance as responses to copyright enforcement policies of digital content: appraisal tendency perspective

Purpose

Today, digital piracy remains a growing challenge for legislators and businesses operating in the entertainment industry. For these reasons, policymakers place significant efforts in reducing piracy activities through copyright enforcement policies. Yet, the effectiveness of these frameworks remains questionable since empirical evidence offers countervailing insights. For these reasons, this study adopts a demand-side approach to investigate how users' emotional reactions to the copyright enforcement policy influence their digital piracy-related judgments and intention.

Design/methodology/approach

The author used a scenario-based approach to test the hypotheses and surveyed 262 users who actively stream movies and TV shows.

Findings

By drawing on the appraisal tendency framework, the author finds that among individuals experiencing fear due to the copyright enforcement policy, there is a decreasing intention to continue using illegal streaming services. On the contrary, individuals experiencing indignation are more likely to have increased intention to use illegal streaming services. On top of this, we reveal indirect effects by mediating individuals' vulnerability to sanctions (fear path) and subjective norms (indignation path) as judgments cues.

Originality/value

The author contributes to digital piracy literature in two ways with the study. First, the author unfolds the affective background that explains the emergence of deterrence or defiance effects when users respond to the copyright enforcement policy. Thus, the author shows that fear is a trigger for decreasing while indignation increases the usage of illegal streaming services. Second, through the appraisal tendency framework, the author enriches the literature by theorizing and providing empirical evidence on how previously established vulnerability to sanctions and subjective norms reinforce affective influences on encouraging or discouraging digital piracy intentions. Overall, the findings also provide policymakers with original insights on designing their copyright enforcement tools to combat digital piracy further.

"I Feel Therefore I Decide": Effect of Negative Emotions on Temporal Discounting and Probability Discounting

Temporal and probability discounting are considered two fundamental constructs in economic science, as they are associated with phenomena with major societal impact and a variety of sub-optimal behaviors and clinical conditions. Although it is well known that positive and negative affective states bear important cognitive/behavioral consequences, the effect of emotional experiences on decision-making remains unclear due to the existence of many conflicting results. Inspired by the need to understand if and to what extent the current COVID-19 pandemic has determined changes in our decision-making processes by means of the unusual, prolonged experience of negative feelings, in this study we investigate the effect of anger, fear, sadness, physical and moral disgust on intertemporal and risky choices. Results show that all emotions significantly increase subjects' preferences for immediate rewards over delayed ones, and for risky rewards over certain ones, in comparison to a "neutral emotion" condition, although the magnitude of the effect differs across emotions. In particular, we observed a more pronounced effect in the case of sadness and moral disgust. These findings contribute to the literature on emotions and decision-making by offering an alternative explanation to the traditional motivational appraisal theories. Specifically, we propose that the increased preference for immediate gratification and risky outcomes serves as a mechanism of self-reward aimed at down-regulating negative feelings and restore the individual's "emotional balance".

Assessing Willingness to Engage in Risky Driving Behaviour Using Naturalistic Driving Footage: The Role of Age and Gender

Young novice drivers are more prone than older drivers to get involved in a risky driving situation. Some young drivers underestimate risk while overestimating their driving abilities, increasing the likelihood of engaging in risky behaviour. Age and inexperience both influence risk estimation, though it is not clear which of these variables is more important. Can drivers’ willingness to engage in risky behaviour be assessed in a similar way to hazard perception skill using video-based risky situations? The aim of the current study was to assess whether a video-based tool could measure the willingness to participate in risky driving situations and whether it can distinguish between different types of risky driving scenarios across gender and driver age groups. We also explored the moderating effect of age and gender on drivers’ experience in relation to the risky manoeuvres and participants’ willingness to engage in risky situations. Participants were presented with naturalistic videos from the perspective of the driver that contained active risky situations (result of driver’s own actions) and were asked to make a decision regarding a potential action (to overtake a bus/bicycle or pass through an amber light) and whether they would accelerate at this point. Participants reported that they were more willing to accelerate and overtake cyclists and buses and less willing to pass a light in amber. Young drivers were more willing to both engage in the risky behaviours and accelerate than older drivers, with young males reporting higher scores than the other groups. Gender differences were observed, with males being more prone to overtake and pass through a light in amber than females; however, this difference was not observed for the intention to accelerate. All the above effects remained when we tested the impact of experience on decision making while controlling for age and gender, although driving experience was no longer significant. These results demonstrate that drivers’ intention to assume risk can indeed be measured in a similar video-based methodology to that used by hazard perception tests. The findings raise the possibility of assessing and training drivers on a wider range of safety-related behaviours.

Examining Bayesian network modeling in identification of dangerous driving behavior

Traffic safety problems are still very serious and human factor is the one of most important factors affecting traffic crashes. Taking Next Generation Simulation (NGSIM) data as the research object, this study defines six control indicators and uses principal component analysis and K-means++ clustering methods to get the driving style of different drivers. Then use the Bayesian Networks Toolbox (BNT) and MCMC algorithm to realize the structure learning of Bayesian network. and parameter learning was completed through Netica software. Finally, the vehicle-based traffic crash risk model was created to conduct sensitivity analysis, posterior probability inference, and simulation data was used to detect the feasibility of the model. The results show that the Bayesian network modeling can not only express the relationship between the crash risk and various driving behaviors, but also dig out the inherent relationship between different influencing factors and investigate the causes of driving risks. The results will be beneficial to accurately identify and prevent risky driving behavior.

Driving Risk Assessment Using Near-Miss Events Based on Panel Poisson Regression and Panel Negative Binomial Regression

This study proposes a method for identifying and evaluating driving risk as a first step towards calculating premiums in the newly emerging context of usage-based insurance. Telematics data gathered by the Internet of Vehicles (IoV) contain a large number of near-miss events which can be regarded as an alternative for modeling claims or accidents for estimating a driving risk score for a particular vehicle and its driver. Poisson regression and negative binomial regression are applied to a summary data set of 182 vehicles with one record per vehicle and to a panel data set of daily vehicle data containing four near-miss events, i.e., counts of excess speed, high speed brake, harsh acceleration or deceleration and additional driving behavior parameters that do not result in accidents. Negative binomial regression (AICoverspeed = 997.0, BICoverspeed = 1022.7) is seen to perform better than Poisson regression (AICoverspeed = 7051.8, BICoverspeed = 7074.3). Vehicles are separately classified to five driving risk levels with a driving risk score computed from individual effects of the corresponding panel model. This study provides a research basis for actuarial insurance premium calculations, even if no accident information is available, and enables a precise supervision of dangerous driving behaviors based on driving risk scores.

COVID-19 event strength, psychological safety, and avoidance coping behaviors for employees in the tourism industry

The impact of COVID-19 on the emotions or behaviors of employees in tourism enterprises would be worthwhile for investigation since COVID-19 has harmed not only people's health and lives but also most tourism enterprises. By identifying the effect of COVID-19 event strength on avoidance coping behaviors, the behaviors isolated from customers unlikely beneficial for tourism enterprises, we not only revealed that COVID-19 event strength indirectly affects avoidance coping behaviors through either the fear of external threat or psychological safety, but also disclosed that supervisor safety support would moderate the effect of psychological safety on such coping behaviors. We argue that understanding potential causes, such as the fear of external threat and psychological safety, and identifying possible solutions, like supervisor safety support, may be key factors for restarting tourism enterprises successfully under the severe impact of the COVID-19 pandemic.

The Moderating Effects of Emotions on the Relationship Between Self-Reported Individual Traits and Actual Risky Driving Behaviors

Purpose

Researches addressing driving behaviors have not fully revealed how emotions affect risky driving behaviors and tend to focus on the effects of some negative emotions rather than those of more specific emotions. This study aimed to test the potential moderating effects of eight common driving emotions on the relationship between self-reported individual traits (sensation seeking and driving style) and actual risky driving behaviors, sequentially providing some implications for the risky driving behavior prevention.

Participants and Methods

A total of 78 licensed drivers were recruited from undergraduate students, company employees and taxi drivers in China. The participants’ data on self-reported driving style (SDBS) and self-reported sensation seeking (SSSS) were obtained through questionnaires. The participants’ data on actual risky driving behaviors (ARD) in eight driving emotional activation states were obtained through a series of emotion induction experiments and driving experiments. The Structural Equation Modeling (SEM) and moderating effect tests were employed to investigate the relationships of driving emotions, SDBS, SSSS and ARD.

Results

Results showed that anger and pleasure affected risky driving behaviors positively by enhancing the relationship between SDBS and ARD, while surprise and fear were negatively related to risky driving behaviors by weakening this relationship. Anxiety positively affected risky driving behaviors by synchronously enhancing the relationship between SDBS and ARD and the relationship between SSSS and ARD, while helplessness and relief affected risky driving behaviors negatively by weakening the two relationships. Contempt affected risky driving behaviors positively by enhancing the relation between SSSS and ARD.

Conclusion

The results illustrated the effects of different emotions on risky driving behaviors, and also partly explained the reasons for these effects. This research provided a source of reference for reducing traffic accidents caused by risky driving behaviors.

The mediating effect of driver characteristics on risky driving behaviors moderated by gender, and the classification model of driver's driving risk

High-risk drivers are more likely to be involved in traffic accidents, and the driving risk level of drivers could be affected by many potential factors, such as demographics and personality traits. Based on the Structural Equation Model (SEM), this study involves a sample of 3150 drivers from the Strategic Highway Research Program 2 (SHRP 2), to explore the relationships among drivers' demographic characteristics (gender, age, and cumulative driving years), sensation seeking, risk perception, and risky driving behaviors. More specifically, the mediation model of driver characteristics on risky driving behaviors moderated by gender is constructed by the SEM. The results show that the effects of driving experience on risky driving behaviors are partially mediated by sensation seeking and risk perception for male drivers, while those are completely mediated by sensation seeking and risk perception for female drivers. Moreover, the development trend of risky driving behavior engagements declines greater with the growing of driving experience for female drivers than male drivers. Finally, a classification model of the driver's driving risk is proposed by the Random Forest classifier, in which the driving risk level of the driver evaluated by the crash and near-crash rate could be classified through the driver's self-reported demographics, sensation seeking, risk perception, and risky driving behaviors. The classification accuracy achieves up to 90 percent, which offers an alternative approach to identifying potential high-risk drivers to reduce property losses, injuries, and death caused by traffic accidents.

Children's fear in traffic and its association with pedestrian decisions

INTRODUCTION

Research on risk for child pedestrian injury risk focuses primarily on cognitive risk factors, but emotional states such as fear may also be relevant to injury risk. The current study examined children's perception of fear in various traffic situations and the relationship between fear perception and pedestrian decisions.

METHOD

150 children aged 6-12-years old made pedestrian decisions using a table-top road model. Their perceived fear in the pedestrian context was assessed.

RESULTS

Children reported greater emotional fear when they faced quicker traffic, shorter distances from approaching traffic, and red rather than green traffic signals. Children who were more fearful made safer pedestrian decisions in more challenging traffic situations. However, when the least risky traffic situation was presented, fear was associated with more errors in children's pedestrian decisions: fearful children failed to cross the street when they could have done so safely. Perception of fear did not vary by child age, although safe pedestrian decisions were more common among the older children.

CONCLUSIONS

Children's emotional fear may predict risk-taking in traffic. When traffic situations are challenging to cross within, fear may appropriately create safer decisions. However, when the traffic situation is less risky, feelings of fear could lead to excessive caution and inefficiency. Practical applications: Child pedestrian safety interventions may benefit by incorporating activities that introduce realistic fear of traffic risks into broader safety lessons.

Negativity bias towards anger in dangerous drivers: Empirical evidence from ERPs

Because of the increase in the numbers of vehicles and drivers, traffic congestion causes anger on the road to occur frequently. In addition to the impact of personality traits, the information processing of emotional stimuli also influences drivers' behaviours. Research is needed to understand how drivers interact with anger stimuli and the differences in processing anger information between safe and dangerous drivers. This study aimed to explore the differences between safe and dangerous drivers' negativity bias towards anger and discuss the causes of dangerous driving behaviours from the perspective of information processing. In total, 34 participants were divided into a safe group and dangerous group based on their traffic violation history and driving behaviour trends. Participants completed an emotional Stroop task with event-related potential (ERP) data. The results showed that the P200 amplitudes of dangerous drivers were significantly reduced compared with those of safe drivers when processing angry and neutral faces, indicating the inadequate assessment of angry faces in early emotional processing. The N170 amplitudes of dangerous drivers were significantly reduced compared with those of safe drivers when processing angry faces, demonstrating decreased negative input that is potentially related to dispositional fearlessness and uncaring traits. This study revealed negativity bias towards anger in dangerous drivers based on ERP evidence, enabling us to understand the differences in driving behaviours from the perspective of physiological mechanisms.

Differences in Driving Intention Transitions Caused by Driver's Emotion Evolutions

Joining worldwide efforts to understand the relationship between driving emotion and behavior, the current study aimed at examining the influence of emotions on driving intention transition. In Study 1, taking a car-following scene as an example, we designed the driving experiments to obtain the driving data in drivers’ natural states, and a driving intention prediction model was constructed based on the HMM. Then, we analyzed the probability distribution and transition probability of driving intentions. In Study 2, we designed a series of emotion-induction experiments for eight typical driving emotions, and the drivers with induced emotion participated in the driving experiments similar to Study 1. Then, we obtained the driving data of the drivers in eight typical emotional states, and the driving intention prediction models adapted to the driver’s different emotional states were constructed based on the HMM severally. Finally, we analyzed the probabilistic differences of driving intention in divers’ natural states and different emotional states, and the findings showed the changing law of driving intention probability distribution and transfer probability caused by emotion evolution. The findings of this study can promote the development of driving behavior prediction technology and an active safety early warning system.

The Effects of the Driver's Mental State and Passenger Compartment Conditions on Driving Performance and Driving Stress

Globalization has increased the number of road trips and vehicles. The result has been an intensification of traffic accidents, which are becoming one of the most important causes of death worldwide. Traffic accidents are often due to human error, the probability of which increases when the cognitive ability of the driver decreases. Cognitive capacity is closely related to the driver’s mental state, as well as other external factors such as the CO₂ concentration inside the vehicle. The objective of this work is to analyze how these elements affect driving. We have conducted an experiment with 50 drivers who have driven for 25 min using a driving simulator. These drivers completed a survey at the start and end of the experiment to obtain information about their mental state. In addition, during the test, their stress level was monitored using biometric sensors and the state of the environment (temperature, humidity and CO₂ level) was recorded. The results of the experiment show that the initial level of stress and tiredness of the driver can have a strong impact on stress, driving behavior and fatigue produced by the driving test. Other elements such as sadness and the conditions of the interior of the vehicle also cause impaired driving and affect compliance with traffic regulations.

Effects of speech-based intervention with positive comments on reduction of driver's anger state and perceived workload, and improvement of driving performance

Research suggests that speech-based interventions can mitigate driving anger and enhance road safety. The present study found that both positive and negative comments can reduce anger state and perceived workload, and improve driving performance. In addition, positive comment including description of the driving environment and comment on drivers is more effective than negative comment intervention, which is indicated by larger effect size and higher user satisfaction and acceptance. The research findings could provide practical implications on the design of in-vehicle intelligent agents for driving behavior intervention.

Quantification of risk perception: Development and validation of the construction worker risk perception (CoWoRP) scale

INTRODUCTION

The construction sector is leading in the number of accidents and fatalities; risk perception is the key to driving these numbers. Previous construction safety studies on risk perception quantification have not considered affective risk perception of construction workers or conducted comprehensive reliability and validity testing. Thus, this study aims to fill this need by developing a psychometrically sound instrument - the Construction Worker Risk Perception (CoWoRP) Scale - to assess the risk perception of construction workers.

METHOD

Four phases of scale development, namely, item development, factor analysis, reliability assessment, and validity assessment were conducted with the collection and testing of data from a group (n = 469) of voluntary construction workers in Hong Kong.

RESULTS

The CoWoRP Scale with 13 items was shown to have acceptable test-retest reliability, internal consistency reliability, as well as content, convergent, discriminant, and criterion-related validity. Also, the CoWoRP Scale was affirmed to have three dimensions of worker risk perception, namely risk perception - probability, risk perception - severity, risk perception - worry and unsafe. These three dimensions of worker risk perception were negatively correlated with their risk-taking behavior.

CONCLUSIONS

The CoWoRP Scale is a reliable and valid instrument for measuring the risk perception of construction workers and is expected to facilitate the construction safety studies that take risk perception of construction workers into account. Practical applications: The CoWoRP Scale could serve as an aptitude test to identify the characteristics of construction workers most likely to perceive lower risk in risky work situations. In turn, this information could help safety management provide safety training programs to those workers to enhance their risk perception and thereby minimizing their risk-taking behavior, reducing unnecessary training costs, and improving the construction safety performance.

Trait driving anger and driving styles among Colombian professional drivers

This study analyzes the association between trait driving anger and driving styles in a sample of Colombian professional drivers. Additionally, the internal and external validity of the Deffenbacher's Driving Anger Scale (DAS-14) was examined in the study population. The DAS-14 and the Spanish Version of the Multidimensional Driving Style Inventory (S-MDSI) were administered to 492 urban bus and taxi operators. Average trait driving anger scores in the study population were similar to those reported in previous validation studies from Spain, Argentina, China, and Malaysia. After deleting three cross-loaded items, confirmatory factor analyses revealed a three-dimensional latent structure for the DAS-14, similar but not equal to the previous Spanish speaking validations. This factorial structure fits the data reasonably well. Finally, linear regression analyses revealed that the three factors of the DAS-14 (impeded progress by others, illegal driving, and direct hostility) significantly predict adaptive and maladaptive driving styles. Overall, the results of this study suggest that the DAS-14 is a reasonably reliable measure of driving anger traits among professional drivers, and it also provides relevant insights for the prevention of risky driving styles in this occupational group.

Angry Drivers Take Risky Decisions: Evidence from Neurophysiological Assessment

The present study investigated the risk-taking behaviors of angry drivers, which were coincidentally measured via behavioral and electroencephalographic (EEG) recordings. We manipulated a driving scenario that concerned a Go/No-Go decision at an intersection when the controlling traffic light was in its yellow phase. This protocol was based upon the underlying format of the Iowa gambling task. Variation in the anger level was induced through task frustration. The data of twenty-four drivers were analyzed via behavioral and neural recordings, and P300 was specifically extracted from EEG traces. In addition, the behavioral performance was indexed by the percentage of high-risk choices minus the number of the low-risk choices taken, which identified the risk-taking propensity. Results confirmed a significant main effect of anger on the decisions taken. The risk-taking propensity decreased across the sequence of trial blocks in baseline assessments. However, with anger, the risk-taking propensity increased across the trial regimen. Drivers in anger state also showed a higher mean amplitude of P300 than that in baseline state. Additionally, high-risk choices evoked larger P300 amplitude than low-risk choices during the anger state. Moreover, the P300 amplitude of high-risk choices was significantly larger in the anger state than the baseline state. The negative feedback induced larger P300 amplitude than that recorded in positive feedback trials. The results corroborated that the drivers exhibited higher risk-taking propensity when angry although they were sensitive to the inherent risk-reward evaluations within the scenario. To reduce this type of risk-taking, we proposed some effective/affective intervention methods.

Differentiating the influence of incidental anger and fear on risk decision-making

Previous research has revealed that incidental emotions of different valence (positive/negative/neutral) produce distinct impacts on risk decision-making. This study went on to compare the effects of different emotions of which the valence are identical. We focused on anger and fear, both of which are negative emotions but differ in motivational and appraisal dimensions. Participants finished a forced-choice gambling task, during which incidental emotions (anger/fear/happy) were elicited by facial stimuli selected from the Chinese Facial Affective Picture System. Behavioral and event-related potential (ERP) data were recorded in the experiment, which showed that anger and fear were different in their influence on behavioral risk preference and the relationship between outcome processing and subsequent risk decisions. Regarding the behavioral results, risk preference in the anger condition was higher than the fear condition, but lower than the happy condition. Regarding the ERP results elicited by outcome feedback (gain/loss), in the fear condition, the feedback-related negativity (FRN) was positively correlated with risk preference; in the anger condition, the gain-related P3 component was positively correlated with risk preference; in the happy condition, both the FRN and the loss-related P3 was negatively correlated with risk preference. The current findings provide novel insight into distinguishing the effect of different incidental emotions on risk preference.

Anger expression among Danish cyclists and drivers: A comparison based on mode specific anger expression inventories

Based on the short form of the driving anger expression inventory (DAX-short, 15-item), the present study developed an adapted version of the DAX for cyclists (CAX, 14 items). The data basis was an online survey of 2000 inhabitants of Denmark. A principle component analysis on the translated DAX-short confirmed the 4-factor solution of the original study differentiating between (1) personal physical aggressive expression, (2) use of a vehicle to express anger, (3) verbal aggressive expression and (4) adaptive/constructive expression. In case of cycling, the factor "use of a vehicle to express anger" only included one item and was left out. Based on the results, reliable subscales were developed. Drivers scored higher in verbal aggressive expression than cyclists, while there was no significant difference in constructive expression. The subscales for drivers and cyclists showed significant relations to age, gender, self-reported aggressive behaviours and traffic fines: Women scored for instance lower in physical expression, while older people scored higher in constructive expression. The effect of age and gender on anger expression among drivers and cyclists remained significant when controlling for exposure and other factors in linear regression analyses. These analyses also showed a relationship between a positive attitude towards driving and higher levels of anger expression among drivers, while this was not the case for cyclists.

Threat appeals in health communication: messages that elicit fear and enhance perceived efficacy positively impact on young male drivers

Background

Health communications often present graphic, threat-based representations of the potential consequences of health-risk behaviours. These ‘threat appeals’ feature prominently in public health campaigns, but their use is controversial, with studies investigating their efficacy reporting inconsistent, and often negative, findings. This research examined the impact of a threat-based road safety advertisement on the driving behaviour of young male drivers.

Methods

To address limitations of previous research, we first identified a road safety advertisement that objectively and subjectively elicited fear using physiological and subjective measures. Study 1 (n = 62) examined the effect of this advertisement, combined with a manipulation designed to increase perceived efficacy, on speed choice. Study 2 (n = 81) investigated whether a state emotion, anger, impacts on the effectiveness of the advertisement in changing four distinct driving behaviours. Both studies examined short-term effects only.

Results

Study 1 findings indicated that a high threat message, when combined with high perceived efficacy, can lead to a decrease in speed choice. Study 2 results suggested that increased levels of state anger may counteract the potential value of combining fear-arousing threats and efficacy-building messages.

Conclusions

Findings suggest that threat-based road safety communications that target affective (fear) and cognitive (perceived efficacy) mechanisms can positively affect driving behaviours. State emotions, such as anger, may negatively impact on the effectiveness of the message. Taken together, these findings provide additional support for the use of efficacy-building messages in threat-based communications, but highlight the need for further research into the complex array of affective influences on driving.

Electronic supplementary material

The online version of this article (doi:10.1186/s12889-016-3227-2) contains supplementary material, which is available to authorized users.

Effect of exposure to aggressive stimuli on aggressive driving behavior at pedestrian crossings at unmarked roadways

BACKGROUND

Aggressive driving, influenced by the proneness of driving aggression, angry state and provoking situation, is adversely affecting traffic safety especially in developing countries where pedestrians frequently cross an unmarked crosswalk. Exposure to aggressive stimuli causes driving anger and aggressive driving behaviors, but the exposure effect on higher and lower aggression drivers and their cumulative changes under successive exposures need more investigation.

OBJECTIVES

An experiment was conducted to examine (1) driving behaviors of individuals with higher and lower aggressive driving traits when approaching pedestrian crossings at unmarked roadways with and without aggressive provocation; and (2) cumulative changes of driving performance under repeated provocations.

METHOD

We conducted a driving simulator study with 50 participants. Trait of aggressive driving served as a between-subjects variable: participants with an Aggressive Driving Scale (ADS) total score of 30 or more (for men) or 23 or more (for women) were regarded as higher aggressive drivers; lower aggressive drivers were those individuals whose ADS total scores were 21 or less (for men) or 13 or less (for women). Exposure to aggressive stimuli (provoked vs. non-provoked condition) served as a within-subjects variable. Several aspects of the participants' minimum driving speed, lateral distance from a simulated pedestrian, lateral deviation, and subjective measures were collected.

RESULTS

We found that drivers with higher aggressive driving traits were more likely to feel irritated and fail to give way for pedestrians and drove closer to pedestrians when exposed to sustained honking and improper passing compared to the non-provoked condition. This trait×state interaction only occurred when pedestrians crossed the street from the right roadway edge line. In addition, we observed an accumulation effect of exposure to aggressive stimuli on driver's aggressive behaviors at pedestrian crossings.

CONCLUSIONS

Environmental design, law enforcement, and educational campaign may have practical value for reducing pedestrian and driver conflicts at unmarked roadways.

Negativity Bias in Dangerous Drivers

The behavioral and cognitive characteristics of dangerous drivers differ significantly from those of safe drivers. However, differences in emotional information processing have seldom been investigated. Previous studies have revealed that drivers with higher anger/anxiety trait scores are more likely to be involved in crashes and that individuals with higher anger traits exhibit stronger negativity biases when processing emotions compared with control groups. However, researchers have not explored the relationship between emotional information processing and driving behavior. In this study, we examined the emotional information processing differences between dangerous drivers and safe drivers. Thirty-eight non-professional drivers were divided into two groups according to the penalty points that they had accrued for traffic violations: 15 drivers with 6 or more points were included in the dangerous driver group, and 23 drivers with 3 or fewer points were included in the safe driver group. The emotional Stroop task was used to measure negativity biases, and both behavioral and electroencephalograph data were recorded. The behavioral results revealed stronger negativity biases in the dangerous drivers than in the safe drivers. The bias score was correlated with self-reported dangerous driving behavior. Drivers with strong negativity biases reported having been involved in mores crashes compared with the less-biased drivers. The event-related potentials (ERPs) revealed that the dangerous drivers exhibited reduced P3 components when responding to negative stimuli, suggesting decreased inhibitory control of information that is task-irrelevant but emotionally salient. The influence of negativity bias provides one possible explanation of the effects of individual differences on dangerous driving behavior and traffic crashes.