Inattention and distraction in fatal road crashes - Results from in-depth crash investigations in Norway

Driving impairment due to psychoactive substances and attention deficit disorder: A pilot study

Objective

Attention disorder and substance use disorder are linked to driving impairment and increased road crash involvement. This study explores attention deficits in a population of drivers found driving under the influence (DUI) of psychoactive substances.

Methods

A case-control study was conducted comparing subjects with a previous DUI episode (cases) to subjects who were negative for DUI offenses (controls). Personal, socio-demographic, and DUI data were collected for both groups. All subjects were administered the Continuous Performance Test-third edition (CPT-3), which measures dimensions of attention, including inattentiveness, impulsivity, sustained attention, and vigilance. Possible associations with a previous DUI episode, the use of illicit substances or excessive alcohol use, and road crash involvement were analyzed statistically.

Results

Overall, the study included 147 subjects (100 cases, 47 controls). The parameter distributions of detectability, probability of ADHD, and inattentiveness indicated statistical differences between the two groups. No attention deficits predicted substance use disorder or excessive alcohol consumption. Inattentiveness was an independent risk factor for previous road collision involvement.

Conclusions

The results suggest that alterations exist in some attention dimensions in a population of DUI subjects who were users of alcohol or other psychoactive substances and involved in road traffic crashes. The CPT-3 had successfully distinguished between the two study groups, and after validation, it could be useful in the process of reinstating a driver's license. Future research should expand the study sample to better understand the relevance of the proposed methodological approach in terms of prevention, rehabilitation, and the monitoring of subjects evaluated for driving eligibility requirements.

Driving impairment and altered ocular activity under the effects of alprazolam and alcohol: A randomized, double-blind, placebo-controlled study

BACKGROUND

Alprazolam, also known by trade-name Xanax, is regularly detected along with alcohol in blood samples of drivers injured or killed in traffic collisions. While their co-consumption is principally legal, policy guidelines concerning fitness-to-drive are lacking and methods to index impairment are underdeveloped.

METHODS

In this randomized, double-blind, placebo-controlled, crossover trial, we examined whether legally permissible levels of alcohol [target 0.04% blood alcohol concentration (BAC)], alprazolam (1mg), and their combination impacts driving performance, and whether driving impairment can be indexed by ocular activity. Participants completed a test battery consisting of a 40-minute simulated highway drive with ocular parameters assessed simultaneously, the Karolinska Sleepiness Scale, and a confidence to drive assessment following four separate treatment combinations. The predictive efficacy of ocular parameters to identify alcohol and alprazolam-related driving impairment was also examined.

RESULTS

Among 21 healthy, fully licensed drivers (37% female, mean age 28.43, SD ± 3.96), driving performance was significantly impacted by alprazolam, alcohol, and their combination. Linear regression models revealed that the odds of an out-of-lane event occurring increased five-fold under the influence alprazolam alone and when combined with alcohol. An increase in gaze transition entropy (GTE) demonstrated the strongest association with the odds of an out-of-lane event occurring in the same minute, with both microsleeps and fixation rate achieving moderate accuracy across treatments.

CONCLUSIONS

Alprazolam and alcohol, alone and in combination, impaired select aspects of vehicle control over time. GTE, microsleeps, and fixation rate show potential as real-time indicators of driving impairment and crash risk associated with alcohol and alprazolam consumption.

European NCAP Driver State Monitoring Protocols: Prevalence of Distraction in Naturalistic Driving

OBJECTIVE

examine the prevalence of driver distraction in naturalistic driving when implementing European New Car Assessment Program (Euro NCAP)-defined distraction behaviours.

BACKGROUND

The 2023 introduction of Occupant Status monitoring (OSM) into Euro NCAP will accelerate uptake of Driver State Monitoring (DSM). Euro NCAP outlines distraction behaviours that DSM must detect to earn maximum safety points. Distraction behaviour prevalence and driver alerting and intervention frequency have yet to be examined in naturalistic driving.

METHOD

Twenty healthcare workers were provided with an instrumented vehicle for approximately two weeks. Data were continuously monitored with automotive grade DSM during daily work commutes, resulting in 168.8 hours of driver head, eye and gaze tracking.

RESULTS

Single long distraction events were the most prevalent, with .89 events/hour. Implementing different thresholds for driving-related and driving-unrelated glance regions impacts alerting rates. Lizard glances (primarily gaze movement) occurred more frequently than owl glances (primarily head movement). Visual time-sharing events occurred at a rate of .21 events/hour.

CONCLUSION

Euro NCAP-described driver distraction occurs naturalistically. Lizard glances, requiring gaze tracking, occurred in high frequency relative to owl glances, which only require head tracking, indicating that less sophisticated DSM will miss a substantial amount of distraction events.

APPLICATION

This work informs OEMs, DSM manufacturers and regulators of the expected alerting rate of Euro NCAP defined distraction behaviours. Alerting rates will vary with protocol implementation, technology capability, and HMI strategies adopted by the OEMs, in turn impacting safety outcomes, user experience and acceptance of DSM technology.

Motorcycle rider error and engagement in distracting activities: A study using the Attention-Related Driving Errors Scale (ARDES-M)

Research on motorcycle rider distraction and inattention is limited, especially when compared to that on car drivers. This study examines motorcycle rider error and its relationship with engagement in distracting activities. Additionally, it provides an adaptation of the Attention-Related Driving Errors Scale for use with motorcycle riders (ARDES-M). A sample of 418 motorcyclists responded to a web questionnaire consisting of the ARDES-M, a self-report measure of Involvement in Distracting Activities (IDA), a measure of everyday inattention (ARCES, Attention Related Cognitive Errors Scale) and socio-demographic and riding-related questions. Riding errors were associated with a higher level of engagement in distracting activities and a greater propensity for inattention in daily life. ARDES-M scores were associated with various types of self-reported traffic crashes and near-crashes, while self-reported distractions (IDA) were only linked with near-crashes. The most widely self-reported distracting activities were using map navigation, listening to the radio or to music and adjusting vehicle devices. The various uses of cell phones were relatively minor. Young riders reported higher levels of engagement in distracting activities and committing more riding errors. The ARDES-M showed good psychometric properties for the sample in Spain.

Factors reducing the detectability of train horns by road users: A laboratory study

Level crossing safety is a well-researched safety issue worldwide, but little attention has been placed on the safety benefits of using train horns when a train approaches a level crossing. Given train horns' adverse effects on the health and well-being of residents living near rail tracks, the use of train horns must be beneficial to safety. The current study sought to determine in a laboratory environment whether road users (N = 31) can detect the range of train horns observed in Australia in terms of loudness and duration, using high-definition audio recordings from railway crossings. A repeated measures design was used to evaluate the effects of key factors likely to influence the detectability of train horns, including, visual and auditory distractive tasks, hearing loss and environmental noise (crossing bells). Train horn detectability was assessed based on participants' accuracy and reaction times. Results indicated the duration of the train horn had the most influential effect on the detectability of train horns, with short-duration train horns less likely to be detected. The presence of bells at a crossing was the second most important factor that limited train horn detection. Train horn loudness also affected detectability: faint blasts were less likely to be noticed, while loudest blasts were more likely to be noticed. However, loud horns reduced the ability to detect the side from which the train was approaching and may result in longer times to detect the train, in the field. The auditory distractive task reduced the train horn detection accuracy and increased reaction time. However, the visual distractive task and medium to severe hearing loss were not found to affect train horn detection. This laboratory study is the first to provide a broad understanding of the factors that affect the detectability of Australian train horns by road users. The findings from this study provide important insights into ways to reduce the use and modify the practice to mitigate the negative effects of train horns while maintaining the safety of road users.

Severity analysis of single-vehicle left and right run-off-road crashes using a random parameter ordered logit model

OBJECTIVES

Single vehicle (SV) run-off-road crashes are a major cause of severe injury and fatality. Such crashes can result in different levels of severity depending on the direction (i.e., left or right) in which the vehicle runs off the road. This paper investigated the factors contributing to the crash severities of right run-off-road (R-ROR) and left run-off-road (L-ROR) SV crashes.

METHODS

The study used SV crash data from the City of Charlotte, North Carolina, covering 2014 to 2017. Two separate random parameter ordered logit (RPOL) models were developed to estimate the contributing factors to R-ROR and L-ROR SV crash severities. The impact of the explanatory variables on the crash severity outcomes was quantified using the models' direct pseudo-elasticities.

RESULTS

The model results showed that male drivers, Driving Under Influence (DUI), motorcycles, and dry road surfaces were significant contributing factors to R-ROR and L-ROR SV crash severities. Specifically for the R-ROR model, speeding, reckless driving, 1-2 lanes, and older drivers increased crash severity. For the L-ROR model, phone distraction, crossed centerline/median, 3-4 lanes, rain, and dark unlighted roadway increased crash severity.

CONCLUSIONS

Based on the estimated parameters for the common significant variables in the two models, it was inferred that L-ROR SV crashes are more likely to result in severe crashes compared to R-ROR SV crashes. Hence, this study contributes to the literature on ROR SV crashes by providing additional insight into contextual factors influencing ROR crash severity for more effective countermeasures.

Investigating speed-safety association: Considering the unobserved heterogeneity and human factors mediation effects

The relationship between mean speed and crash likelihood is unclear in the literature. The contradictory findings can be attributed to the masking effects of the confounding variables in this association. Moreover, the unobserved heterogeneity has almost been criticized as a reason behind the current inconclusive results. This research provides an effort to develop a model that analyzes the mean speed-crash frequency relationship by crash severity and type. Also, the confounding and mediation effects of the environment, driver, and traffic-related attributes have been considered. To this end, the loop detector and crash data were aggregated daily for rural multilane highways of Tehran province, Iran, covering two years, 2020-2021. The partial least squares path modeling (PLS-PM) was employed for crash causal analysis along with the finite mixture partial least squares (FIMIX-PLS) segmentation to account for potential unobserved heterogeneity between observations. The mean speed was negatively and positively associated with the frequency of property damage-only (PDO) and severe accidents, respectively. Moreover, driver-related variables, including tailgating, distracted driving, and speeding, played key mediation roles in associating traffic and environmental factors with the crash risk. The higher the mean speed and the lower the traffic volume, the higher odds of distracted driving. Distracted driving was, in turn, associated with the higher vulnerable road users (VRU) accidents and single-vehicle accidents, triggering a higher frequency of severe accidents. Moreover, lower mean speed and higher traffic volume were positively correlated with the percentage of tailgating violations, which, in turn, predicted multi-vehicle accidents as the main predictor of PDO crash frequency. In conclusion, the mean speed effects on the crash risk are entirely different for each crash type through distinct crash mechanisms. Hence, the distinct distribution of crash types in different datasets might have led to current inconsistent results in the literature.

Causal mediation analysis of the impacts of distracted driving on crash injury risks

Distracted driving can pose great risks to road traffic safety. Although there is a rich body of literature devoted to identifying the statistical association between distracted driving and crash risks, few are available to examine the causal effect mechanism of distracted driving. Thus, the study attempts to conduct the causal mediation analysis to reveal the impact mechanism of distracted driving on crash injury risks, in which various hazardous driving actions are used as the mediators between driver distraction and crash injuries. Sensitivity analysis is also carried out to validate the underlying assumption of causal mediation analysis. The analytic results indicate that 1) distracted driving can lead to a higher likelihood of hazardous driving actions such as failing to yield, disobeying traffic control devices, driving left of lane center, and failing to stop in assured clear distance, 2) both the driver distraction and hazardous actions are the contributory factors to the severe crash injuries, and 3) distracted driving is identified to have significant mediation effects on crash injury risks. The study confirms the causal mediation effects of distracted driving on crash injury risks, which can serve to propose specific safety countermeasures to mitigate the crash injury risks.

Correlations among self-report, static image, and video-based hazard perception assessments: The validity of a new Lithuanian hazard prediction test

Scholars suggest that improving hazard perception (HP) skills among different road users can lead to safer driving. To this end, a small number of countries have developed a national hazard perception test for their licensing procedures. The purpose of the current research is to develop and validate a new video-based hazard prediction test for the Lithuanian driving context. Eighty-eight drivers participated in the study. Initially, 25 naturalistic clips were included and edited into a hazard prediction test following the "what happens next?" paradigm. The overall score of the test was higher for experienced drivers when compared with novices. On further inspection the overall group difference was driven by 12 clips. The Lithuanian Hazard Perception test HPT-LIT featuring static images (Tūskė et al., 2019) and Hazard Perception Questionnaire developed by White et al. (2011) were used to validate the new test. This new version of the test (12 clips, LHP₁₂) showed acceptable psychometric properties. Drivers with less driving experience demonstrated poorer hazard prediction skills than experienced drivers. The results revealed a negative correlation between hazard prediction scores on LHP₁₂ and crash involvement in the novice driver group, as well as a positive correlation between prediction scores and self-reported hazard prediction skills. It was concluded that the new Lithuanian hazard test shows potential for future research.

Driving Behavior Recognition Algorithm Combining Attention Mechanism and Lightweight Network

In actual driving scenes, recognizing and preventing drivers’ non-standard driving behavior is helpful in reducing traffic accidents. To resolve the problems of various driving behaviors, a large range of action, and the low recognition accuracy of traditional detection methods, in this paper, a driving behavior recognition algorithm was proposed that combines an attention mechanism and lightweight network. The attention module was integrated into the YOLOV4 model after improving the feature extraction network, and the structure of the attention module was also improved. According to the 20,000 images of the Kaggle dataset, 10 typical driving behaviors were analyzed, processed, and recognized. The comparison and ablation experimental results showed that the fusion of an improved attention mechanism and lightweight network model had good performance in accuracy, model size, and FLOPs.

OD-XAI: Explainable AI-Based Semantic Object Detection for Autonomous Vehicles

In recent years, artificial intelligence (AI) has become one of the most prominent fields in autonomous vehicles (AVs). With the help of AI, the stress levels of drivers have been reduced, as most of the work is executed by the AV itself. With the increasing complexity of models, explainable artificial intelligence (XAI) techniques work as handy tools that allow naive people and developers to understand the intricate workings of deep learning models. These techniques can be paralleled to AI to increase their interpretability. One essential task of AVs is to be able to follow the road. This paper attempts to justify how AVs can detect and segment the road on which they are moving using deep learning (DL) models. We trained and compared three semantic segmentation architectures for the task of pixel-wise road detection. Max IoU scores of 0.9459 and 0.9621 were obtained on the train and test set. Such DL algorithms are called “black box models” as they are hard to interpret due to their highly complex structures. Integrating XAI enables us to interpret and comprehend the predictions of these abstract models. We applied various XAI methods and generated explanations for the proposed segmentation model for road detection in AVs.

Road Safety Policy in Addis Ababa: A Vision Zero Perspective

In this article, the Addis Ababa city road safety policies are examined and analysed based on the Vision Zero approach to road safety work. Three major policy documents are explored and assessed in terms of how they compare with Vision Zero policy in Sweden, concerning how road safety problems are conceptualised, the responsibility ascriptions promoted, the nature of goal setting concerning road safety objectives, and the specific road safety interventions promoted. It is concluded that there is a big difference between the Swedish Vision Zero approach to road safety work and the Addis Ababa road safety approach in terms of how road safety problems are framed and how responsibility ascriptions are made. In Addis Ababa, policy documents primarily frame road safety problems as individual road user problems and, hence, the responsibility for traffic safety is mainly left to the individual road users. The responsibility extended to other system components such as the vehicles, road design, and the operation of the traffic is growing but still very limited. It is argued that in order to find and secure long-term solutions for traffic safety in the city, a paradigm shift is needed, both regarding what are perceived to be the main causes of road safety problems in the city and who should be responsible for ensuring that road fatalities and injuries are prevented.

Hit-and-run: a Swedish nationwide cohort study of serious transport accidents and convictions due to traffic offenses in obsessive-compulsive disorder

PURPOSE

Individuals with obsessive-compulsive disorder (OCD) often report driving-related obsessions, such as fears of causing accidents, but the risk of transport accidents in OCD is unknown. We investigated whether individuals with OCD have an increased risk of serious transport accidents and convictions due to traffic offenses and explored the role of psychiatric comorbidities.

METHODS

We included all individuals ≥ 18 years living in Sweden between 1997 and 2013 (N = 5,760,734). A total of 23,126 individuals had a diagnosis of OCD in the National Patient Register. We also identified 16,607 families with full siblings discordant for OCD. Cox proportional hazards regression models estimated hazard ratios (HRs) with 95% confidence intervals (CIs) for the risk of three outcomes in individuals with OCD, compared to unexposed individuals and their unexposed full siblings: injuries or deaths due to transport accidents, injuries or deaths due to motor vehicle accidents, and convictions related to traffic offenses. Psychiatric comorbidities were systematically adjusted for.

RESULTS

Women, but not men, with OCD had a marginally increased risk of serious transport accidents (adjusted HR = 1.20 [95% CI 1.13-1.28]) and motor vehicle accidents (adjusted HR = 1.20 [95% CI 1.09-1.31]), compared to unaffected individuals. Neither women nor men with OCD had a significantly increased risk of convictions. The sibling comparisons showed no significant associations. When psychiatric comorbidities were adjusted for, several observed associations became non-significant or inversed (HRs and 95% CIs below one).

CONCLUSION

The risks of serious transport accidents and driving-related criminal convictions in OCD are negligible and heavily influenced by psychiatric comorbidity.

Effects of weather and moon phases on emergency medical use after fall injury: A population-based nationwide study

Background

Previous studies reported that changes in weather and phases of moon are associated with medical emergencies and injuries. However, such studies were limited to hospital or community level without explaining the combined effects of weather and moon phases. We investigated whether changes in weather and moon phases affected emergency department (ED) visits due to fall injuries (FIs) based on nationwide emergency patient registry data.

Methods

Nationwide daily data of ED visits after FI were collected from 11 provinces (7 metropolitan cities and 4 rural provinces) in Korea between January 2014 and December 2018. The daily number of FIs was standardized into FI per million population (FPP) in each province. A multivariate regression analysis was conducted to elucidate the relationship between weather factors and moon phases with respect to daily FPP in each province. The correlation between weather factors and FI severity was also analyzed.

Results

The study analyzed 666,912 patients (418,135 in metropolitan and 248,777 in rural areas) who visited EDs on weekdays. No regional difference was found in age or gender distribution between the two areas. Precipitation, minimum temperature and wind speed showed a significant association with FI in metropolitan areas. In addition, sunshine duration was also substantial risk factors for FI in rural areas. The incidence of FIs was increased on full moon days than on other days in rural areas. Injury severity was associated with weather factors such as minimum temperature, wind speed, and cloud cover.

Conclusion

Weather changes such as precipitation, minimum temperature, and wind speed are associated with FI in metropolitan and rural areas. In addition, sunshine duration and full moon are significantly associated with FI incidence only in rural areas. Weather factors are associated with FI severity.

Influence of built environment and roadway characteristics on the frequency of vehicle crashes caused by driver inattention: A comparison between rural roads and urban roads

INTRODUCTION

With prevalent and increased attention to driver inattention (DI) behavior, this research provides a comprehensive investigation of the influence of built environment and roadway characteristics on the DI-related vehicle crash frequency per year. Specifically, a comparative analysis between DI-related crash frequency in rural road segments and urban road segments is conducted.

METHOD

Utilizing DI-related crash data collected from North Carolina for the period 2013-2017, three types of models: (1) Poisson/negative binomial (NB) model, (2) Poisson hurdle (HP) model/negative binomial hurdle (HNB) model, and (3) random intercepts Poisson hurdle (RIHP) model/random intercepts negative binomial hurdle (RIHNB) model, are applied to handle excessive zeros and unobserved heterogeneity in the dataset.

RESULTS

The results show that RIHP and RIHNB models distinctly outperform other models in terms of goodness-of-fit. The presence of commercial areas is found to increase the probability and frequency of DI-related crashes in both rural and urban regions. Roadway characteristics (such as non-freeways, segments with multiple lanes, and traffic signals) are positively associated with increased DI-related crash counts, whereas state-secondary routes and speed limits (higher than 35 mph) are associated with decreased DI-related crash counts in rural and urban regions. Besides, horizontal curved and longitudinal bottomed segments and segments with double yellow lines/no passing zones are likely to have fewer DI-related crashes in urban areas. Medians in rural road segments are found to be effective to reduce DI-related crashes. Practical Applications: These findings provide a valuable understanding of the DI-related crash frequency for transportation agencies to propose effective countermeasures and safety treatments (e.g., dispatching more police enforcement or surveillance cameras in commercial areas, and setting more medians in rural roads) to mitigate the negative consequences of DI behavior.

European NCAP Program Developments to Address Driver Distraction, Drowsiness and Sudden Sickness

Driver distraction and drowsiness remain significant contributors to death and serious injury on our roads and are long standing issues in road safety strategies around the world. With developments in automotive technology, including driver monitoring, there are now more options available for automotive manufactures to mitigate risks associated with driver state. Such developments in Occupant Status Monitoring (OSM) are being incorporated into the European New Car Assessment Programme (Euro NCAP) Safety Assist protocols. The requirements for OSM technologies are discussed along two dimensions: detection difficulty and behavioral complexity. More capable solutions will be able to provide higher levels of system availability, being the proportion of time a system could provide protection to the driver, and will be able to capture a greater proportion of complex real-word driver behavior. The testing approach could initially propose testing using both a dossier of evidence provided by the Original Equipment Manufacturer (OEM) alongside selected use of track testing. More capable systems will not rely only on warning strategies but will also include intervention strategies when a driver is not attentive. The roadmap for future OSM protocol development could consider a range of known and emerging safety risks including driving while intoxicated by alcohol or drugs, cognitive distraction, and the driver engagement requirements for supervision and take-over performance with assisted and automated driving features.

Job Insecurity during an Economic Crisis: the Psychological Consequences of Widespread Corporate Cost-Cutting Announcements

Economic crises, such as the one induced by the COVID-19 pandemic, and resulting widespread corporate cost-cutting, drastically alter the nature of work. Job insecurity represents a critical intermediate between the economic ramifications of an economic crisis and work and stress outcomes, however, the underlying cognitive consequences of job insecurity and how to buffer those effects are not well understood. We examine how corporate cost-cutting announcements indirectly relate to employees’ attention through their relationship with employee job insecurity and investigate supervisor support as a potential buffer of these relationships. We used multi-source data to test our research model, combining data on cost-cutting announcements (budget cuts, layoffs, and furloughs) in news articles for 165 organizations with survey data from 421 full-time employees from these organizations between March 26, 2020 and April 8, 2020. Cost-cutting announcements are positively related to job insecurity, which is related to employee’s attention with supervisor support mitigating the effects of job insecurity on attention. Grounded in self-regulation theories, we contribute to and extend the theoretical understanding of the organizational context for job insecurity and cognitive outcomes. We discuss the implications for organizations to manage and prepare for future economic crises, specifically on organizational communication and supervisor interventions.

An evaluation and comparison of commercial driver sleepiness detection technology: a rapid review

Objective. Sleepiness-related motor vehicle crashes, caused by lack of sleep or driving during night-time hours, often result in serious injury or fatality. Sleepiness detection technology is rapidly emerging as a sleepiness risk mitigation strategy for drivers. Continuous monitoring technologies assess and alert to driver sleepiness in real-time, while fit for duty technologies provide a single assessment of sleepiness state. The aim of this rapid review was to evaluate and compare sleepiness detection technologies in relation to specifications, cost, target consumer group and validity.Approach. We evaluated a range of sleepiness detection technologies suitable for consumer groups ranging from regular drivers in private vehicles through to work-related drivers within large businesses.Main results. Continuous monitoring technologies typically ranged between $100 and $3000 AUD and had ongoing monthly costs for telematics functionality and manager alerts. Fit for duty technologies had either a one-off purchase cost or a monthly subscription cost. Of concern, the majority of commercial continuous monitoring technologies lacked scientific validation. While some technologies had promising findings in terms of their ability to detect and reduce driver sleepiness, further validation work is required. Field studies that evaluate the sensitivity and specificity of technology alerts under conditions that are regularly experienced by drivers are necessary. Additionally, there is a need for longitudinal naturalistic driving studies to determine whether sleepiness detection technologies actually reduce sleepiness-related crashes or near-crashes.Significance. There is an abundance of sleepiness detection technologies on the market, but a majority lacked validation. There is a need for these technologies and their validation to be regulated by a driver safety body. Otherwise, consumers will base their technology choices on cost and features, rather than the ability to save lives.

Assessment of the Influence of Technology-Based Distracted Driving on Drivers' Infractions and Their Subsequent Impact on Traffic Accidents Severity

Multitasking while driving negatively affects driving performance and threatens people’s lives every day. Moreover, technology-based distractions are among the top driving distractions that are proven to divert the driver’s attention away from the road and compromise their safety. This study employs recent data on road traffic accidents that occurred in Spain and uses a machine-learning algorithm to analyze, in the first place, the influence of technology-based distracted driving on drivers’ infractions considering the gender and age of the drivers and the zone and the type of vehicle. It assesses, in the second place, the impact of drivers’ infractions on the severity of traffic accidents. Findings show that (i) technology-based distractions are likely to increase the probability of committing aberrant infractions and speed infractions; (ii) technology-based distracted young drivers are more likely to speed and commit aberrant infractions; (iii) distracted motorcycles and squad riders are found more likely to speed; (iv) the probability of committing infractions by distracted drivers increases on streets and highways; and, finally, (v) drivers’ infractions lead to serious injuries.

The use of mobile phone while driving: Behavior and determinant analysis in one of the largest metropolitan area of Italy

The use of mobile phones while driving is one of the main causes of road accidents and it is a phenomenon in continuous growth. The key aim of this study is to analyse simultaneously knowledge, attitudes, and behavior toward the use of mobile phones while driving in one of the largest and populous metropolitan areas of Italy, Naples. The data acquired from 774 questionnaires - administered to subjects evenly divided by gender and with an average age of 39 years - revealed that 69 % have used their mobile phone while driving at least once in their lifetime. Among those who used the phone, 63.6 % use it to make phone calls while 75.2 % only to answer them; 49.1 % read messages and only 33.3 % write them. It is also notable that 34.1 % do not stop to answer a call and only 10 % do not value the use of headsets while driving as fundamental. The results indicate that cell phone usage while driving is common in the study population, despite many having university-level education and satisfactory risks awareness. The multiple linear regression analysis shows how knowledge is not correlated to the behavior held. On the contrary, attitudes are strongly correlated to knowledge and behavior, meaning that good attitudes bring forth positive behavior. According to the collected data and statistical analysis, it is possible to identify factors that can greatly affect the use of mobile phone while driving and establish targeted prevention programs.

Towards a Sustainable City for Cyclists: Promoting Safety through a Mobile Sensing Application

Riding a bicycle is a great manner to contribute to the preservation of our ecosystem. Cycling helps to reduce air pollution and traffic congestion, and so, it is one of the simplest ways to lower the environmental footprint of people. However, the cohabitation of cars and vulnerable road users, such as bikes, scooters, or pedestrians, is prone to cause accidents with serious consequences. In this context, technological solutions are sought that enable the generation of alerts to prevent these accidents, thereby promoting a safer city for these road users, and a cleaner environment. Alert systems based on smartphones can alleviate these situations since nearly all people carry such a device while traveling. In this work, we test the suitability of a smartphone based alert system, determining the most adequate communications architecture. Two protocols have been designed to send position and alert messages to/from a centralized server over 4G cellular networks. One of the protocols is implemented using a REST architecture on top of the HTTP protocol, and the other one is implemented over the UDP protocol. We show that the proposed alarm system is feasible regarding communication response time, and we conclude that the application should be implemented over the UDP protocol, as response times are about three times better than for the REST implementation. We tested the applications in real deployments, finding that drivers are warned of the presence of bicycles when closer than 150 m, having enough time to pay attention to the situation and drive more carefully to avoid a collision.

How are distractibility and hazard prediction in driving related? Role of driving experience as moderating factor

Distraction constitute one of the 'five fatal' behaviours that contribute to road trauma, and some people may be more susceptible to it than others. It is also known that a greater ability to predict danger is related to a lower probability of suffering accidents. It could be hypothesised that drivers with a higher tendency to distraction are worse at predicting traffic hazards, but to what extent might driving experience serve to mitigate this tendency to distraction? The current study collected self-reported attentional errors from drivers by using the Attention-Related Driving Errors Scale (ARDES-Spain) in order to examine whether novice drivers suffered from inattention more than experienced drivers. The results demonstrated that novice drivers scored more highly on ARDES than experienced drivers. ARDES scores were then related to performance in a Hazard Prediction test, where participants had to report what hazard was about to happen in a series of video clips that occlude just as the hazard begins to develop. While experienced drivers were better at the Hazard Prediction test than novice drivers, those participants who reported fewer attention errors were also better able to detect the upcoming hazard following occlusion. In addition, our results demonstrate a relationship between self-reported attentional errors and the ability to predict upcoming hazards on the road, with driving experience having a moderating role. In the case of novice drivers, as their scores in the Manoeuvring Errors ARDES factor increase, their ability in Hazard Prediction diminishes, while for experienced drivers the increase is not significant. Guidance on how to improve training for drivers in order to mitigate the effects of inattention on driving safety can be addressed.

Driver distraction and inattention in fatal and injury crashes: Findings from in-depth road crash data

Objective: This study investigated the contribution of driver distraction and inattention within fatal and injury crashes using recent in-depth road crash investigation data. To assist in developing system-based solutions, this study also examined the wider context in which inattention-related crashes occurred and the characteristics of inattentive drivers. Methods: The sample included in-depth crash data from 186 fatal and injury crashes in South Australia investigated from 2014 to 2018. Crash case notes were reviewed to determine whether there was evidence that attentional failures contributed to the crash. Using an adapted taxonomy of inattention, 5 subtypes of driver inattention were defined: Misprioritized attention, neglected attention, cursory attention, diverted attention (distraction), and unspecified inattention. The characteristics of inattention crashes were also compared with those for non-inattention-related crashes. Results: Of the 160 crashes for which there was sufficient information to determine whether inattention was a factor, 31.3% showed evidence of driver inattention contributing to the crash. The most common subtypes of inattention were distraction (13.8% of all crashes) and driver misprioritized attention (8.1%). The distraction-related crashes included a variety of different distractions, the majority of which were not technology based (e.g., passenger interaction, searching for/adjusting objects, emotional stress, other road users), with those located in-vehicle the most prevalent. Distraction from mobile phone use was identified in 2.5% of all crashes. The majority of distractions were cognitive (64%) and voluntary (77%) in nature. Inattention crashes were most likely to involve right turn/angle or rear-end crash types and occur at intersections, in metropolitan areas, and in lower speed zones. Conclusions: The findings established that almost a third of fatal and injury crashes involved driver inattention and distraction and many of these could have been prevented. System-wide solutions that could mitigate or prevent distraction crashes include intervening vehicle safety technologies, infrastructure solutions to promote a forgiving road environment, blocking capabilities within technologies to prevent communications while driving, and interventions communicating the risks associated with inattention. Of significance, this study also demonstrated the importance of in-depth data for understanding the contribution of distraction and inattention errors in crash causation.