Human factors in the causation of road traffic crashes

Analyzing fatal crash patterns of recidivist drivers across genders and age Groups: A hazard-based duration approach

Identifying factors that significantly affect drivers that are repeatedly involved in traffic violations or non-fatal crashes (defined here as recidivist drivers) is very important in highway safety studies. This study sought to understand the relationship between a set of variables related to previous driving violations and the duration between a previous non-fatal crash and a subsequent fatal crash, taking into account the age and gender of the driver. By identifying the characteristics of this unique driver population and the factors that influence the duration between their crash events strategies can be put in place to prevent the occurrence of future and potentially fatal crashes. To do this, a five-year (2015-2019) historical fatal crash data from the United States was used for this study. Out of 15,956 fatal crashes involving recidivist drivers obtained, preliminary analysis revealed an overrepresentation of males (about 75%). It was also found that the average duration between the two crash events was about a year and a half, with only an average of one month difference between male and female drivers. Using hazard-based duration models, factors such as number of previous crashes, previous traffic violations, primary contributing factors and some driver demographic characteristics were found to significantly be associated with the duration between the two crash events. The duration between the two events increased with driver's age for drivers who were involved in only one previous crash and the duration was shorter for those that were previously involved in multiple crashes. Previous DUI violations, license suspensions, and previous speeding violations were found to be associated with shorter durations, at varying degrees depending on the driver's age and gender. The duration was also observed to be longer if the fatal crash involved alcohol or drug use among younger drivers but shorter among middle-aged male drivers. These findings reveal interesting dynamics that may be linked to recidivist tendencies among some drivers involved in fatal crashes. The factors identified from this study could help identify crash countermeasures and programs that will help to reform such driver behaviors.

Data-driven risk analysis of nonlinear factor interactions in road safety using Bayesian networks

This paper aims to demonstrate nonlinear risk factor interactions based on a data-driven approach using a Bayesian network model, providing a road safety use case. Road safety is a critical issue worldwide, with approximately 1.3 million road traffic deaths each year (WHO). Traditional road safety risk assessment methods often analyze individual factors separately; however, these assessments fail to capture the complex dynamics of real-world analysis, in which multiple factors interact through nonlinear relationships. In this study, a novel road safety risk assessment approach that uses a Bayesian network model to explore the nonlinear relationships among road safety risk factors is developed. Through the analysis of extensive crash reports from the state of Maryland, the complex interdependencies among various risk factors and their cumulative impact on road safety are investigated. Our findings show that two combined risk factors have different effects on risk level when considered individually. Two case studies related to human state risk factors and environmental risk factors, such as driving under the influence and snowy roads, as well as fatigue and snowy roads, have an amplified effect on the risk level. The findings highlight the importance of considering nonlinear interactions among risk factors when developing effective and targeted strategies for accident prevention and road safety improvement. This research contributes to the field of road safety by presenting a new methodology for understanding and mitigating road safety hazards.

A study of the impact of traffic investment on traffic fatalities in China, 2004-2020

PURPOSE

Road traffic injuries (RTIs) have been one of the most serious public health problems in China. The purpose of this study was to investigate the extent to which traffic investment affects traffic fatalities in China as well as regional differences.

METHODS

The study analyzed the correlation between traffic investment and traffic fatalities, incorporating additional factors such as economic conditions, road infrastructure, population density, and lighting. The selected variables included the number of traffic fatalities, traffic investment, urban per capita road area, urban road length, road mileage, urban road lighting, population size, and per capita gross domestic product. Relevant data between 2004 and 2020 were collected for an analysis using a fixed effect regression model. A p < 0.05 is considered statistically significant. To reduce the heterogeneity caused by regional differences, the provinces were divided into 6 groups according to administrative districts, and the clustering standard error analysis was carried out.

RESULTS

Overall, there has been a significant improvement in road safety in China from 2004 to 2020, but some regions show an increase in traffic fatalities. The model reveals that traffic investment is significantly and positively correlated with the number of traffic fatalities. Holding all other factors constant, each 10,000 yuan increase in transport investment was associated with an average increase of 0.22 road traffic fatalities. In the analysis of regional differences, there was a significant positive correlation between traffic investment and traffic fatalities in the Northwest region and an increase of 10,000 yuan leads to an increase of 0.47. There was a significant negative correlation between road mileage, urban road lighting system, and population and traffic fatalities. For example, holding other factors constant, a 10,000 km reduction in road length would increase the number of traffic deaths by 45.56. The model results of urban per capita road area, urban road length, per capita gross domestic product, and the explained variables showed that p > 0.100, which was not statistically significant.

CONCLUSIONS

Therefore, traffic investments are essential for governments to develop measures to enhance road safety and reduce the risk of road fatalities. Adjusting traffic road investment and other covariates is conducive to improving traffic safety and reducing the risk of road fatalities. The road safety situation in different regions of China varies greatly. Local governments should consider the actual conditions to provide better road safety configuration policies.

COVID Vaccine Hesitancy and Long-Term Traffic Risks

BACKGROUND

COVID vaccine hesitancy identifies a discrepancy between personal decisions and public guidelines. We tested whether COVID vaccine hesitancy was associated with the long-term risks of a traffic crash.

METHODS

We conducted a population-based longitudinal cohort analysis of adults by determining COVID vaccination status through linkages to electronic medical records. Traffic crashes requiring emergency medical care were defined by multicenter outcome ascertainment of all hospitals throughout the region over the subsequent year.

RESULTS

We identified 11,598,549 total individuals, of whom 1,210,754 had not received a COVID vaccine. A total of 54,558 were subsequently injured in traffic crashes during the 1-year follow-up interval, equal to a risk of 4704 per million. Those who had not received a COVID vaccine had a 58% higher risk than those who had received a COVID vaccine (6983 vs 4438 per million, P < .001). The increased traffic risks among unvaccinated individuals included diverse subgroups, were accentuated for single-vehicle crashes, extended to fatal outcomes, exceeded the risks associated with sleep apnea, and persisted after adjustment for baseline characteristics. The increased risks were validated in analyses using Artificial Intelligence techniques and generally larger than the risks of other adverse events frequently ascribed to COVID vaccination.

CONCLUSIONS

COVID vaccine hesitancy is associated with significant increased long-term risks of a traffic crash. A greater awareness of traffic risks might encourage patients to take protective actions for personal safety.

A psychometric evaluation of the Life Attitudes Schedule-Short Form

The Life Attitudes Schedule-Short Form (LAS-SF) is a measure of suicide proneness featuring various conceptual models. We tested four competing LAS-SF factor structures, as well as construct validity with mental health and suicide metrics. Community dwelling adults (N = 488) completed an online cross-sectional survey. Results supported a four factor (i.e., death-related, health-related, injury-related, and self-related subscales) LAS-SF structure with one higher order factor. Death-related, injury-related, and self-related subscales demonstrated moderate positive associations with mental health and suicidal ideation. Death-related and self-related subscales showed links with suicidal ideation, as well as suicide and depression risk (controlling for other factors). This study is important in highlighting suicide proneness theory may need to be refined. LAS-SF uses include possible risk screening in clinical settings and future focus on the psychological death aspects of the LAS-SF in prospective research. Study limitations include lack of sample diversity and cross-sectional design.

Predicting and factor analysis of rider injury severity in two-wheeled motorcycle and vehicle crash accidents based on an interpretable machine learning framework

OBJECTIVE

As one of the vulnerable road users in accidents, how to improve the two-wheeled motorcyclist's driving safety and reduce accident injury is a public health issue. Accurate identification of the factors influencing the severity of accidents is an important prerequisite for mitigating injury from crashes.

METHODS

Based on a vehicle and a two-wheeled motorcycle crash accident data from the China in-depth accident study database (CIDAS), this study uses the performance evaluation indicators of accuracy, precision, recall, F1-score, AUC, and the ROC curve. The classification and prediction performances of the six machine learning methods on the dataset are compared, and the LightGBM algorithm with the best performance is selected to model the accident injury severity of the motorcyclists. The SHAP method is used to extend the interpretability of the LightGBM model results. Based on the SHAP method, the importance, main effect, and the interaction effect of factors under each accident injury severity are quantitatively analyzed.

RESULTS

The model prediction accuracy is 92.6%, the F1-Score is 92.8%, and the AUC value is 0.986. The importance of factors varies with the accident injury severity of motorcyclists. The kilometers traveled per year by the driver, the throwing distance of the motorcyclist, and the road speed limit are the three most important factors. The motorcyclist is more likely to suffer fatal injuries when the throwing distance is >1,000 cm.

CONCLUSIONS

The prediction model of driver injury severity based on LightGBM algorithm has a good prediction performance. It can be used to analyze the influence factors of injury severity in two-wheeled motorcyclist accident by combining the model with SHAP method. These results could help the traffic management department to take measures to reduce accident injury of motorcyclists.

Air pollution and motor vehicle collisions in New York city

Road traffic accidents are a pervasive feature of everyday life, killing 36,500 people, injuring 4.5 million and, overall, generating costs to the American society of $340 billion in 2019. Understanding the underlying factors can improve the design of prevention strategies. We use all road traffic collisions in New York City between 2013 and 2021 (N = 1,269,600) and match each individual collision to the nearest weather and air pollution station. Our study uses highly disaggregated data using an hourly frequency of collisions at a fine spatial level incorporating various air pollutants and weather factors. We employ an instrumental variable approach using temperature inversions to provide exogenous variation in air pollution addressing endogeneity and measurement error concerns. We find that higher concentrations of carbon monoxide (CO) and sulfur dioxide (SO2) increase the number of collisions but leave the severity (persons injured or killed) unaffected. Part of this can be explained by the effect of air pollutants on aggressive behavior: CO (p < .05) and SO2 (p < .01) increase the number of collisions caused by aggressive driving. Interestingly, this channel is only present in male drivers. Our results provide additional evidence that air pollution not only adversely affects health, but also has "non-health" related effects which are costly for the society.

Blink-related EEG activity measures cognitive load during proactive and reactive driving

Assessing drivers' cognitive load is crucial for driving safety in challenging situations. This research employed the occurrence of drivers' natural eye blinks as cues in continuously recorded EEG data to assess the cognitive workload while reactive or proactive driving. Twenty-eight participants performed either a lane-keeping task with varying levels of crosswind (reactive) or curve road (proactive). The blink event-related potentials (bERPs) and spectral perturbations (bERSPs) were analyzed to assess cognitive load variations. The study found that task load during reactive driving did not significantly impact bERPs or bERSPs, possibly due to enduring alertness for vehicle control. The proactive driving revealed significant differences in the occipital N1 component with task load, indicating the necessity to adapt the attentional resources allocation based on road demands. Also, increased steering complexity led to decreased frontal N2, parietal P3, occipital P2 amplitudes, and alpha power, requiring more cognitive resources for processing relevant information. Interestingly, the proactive and reactive driving scenarios demonstrated a significant interaction at the parietal P2 and occipital N1 for three difficulty levels. The study reveals that EEG measures related to natural eye blink behavior provide insights into the effect of cognitive load on different driving tasks, with implications for driver safety.

Examining enforcement coverage for speeding and red-light offenses across various populations and driver characteristics

Over the years empirical evidence has shown that traffic enforcement reduces traffic violations, crashes, and casualties. However, less attention has been paid to enforcement coverage across different populations and driver characteristics. The current study develops and explores a method for estimating police enforcement coverage, by comparing the share of drivers across several characteristics who received tickets from automatic speed and red-light cameras - as an objective estimate of offenses committed - to the share of drivers who received tickets through manual police enforcement. Using data from all speeding and red-light tickets issued to Israelis over a period of one and a half years, we found under-enforcement by police officers for female drivers, two-wheeled vehicle drivers (for speeding), and drivers with previous tickets. We found over-enforcement for younger drivers, truck drivers, and two-wheeled vehicle drivers (for red-light offenses). The findings suggest that the method developed in the research is able to identify groups of drivers who are over- or under-enforced. Police authorities can use this information to create evidence-based enforcement policies.

Effect of oral clonidine on pain reduction in patients with opioid use disorder in the emergency department: A randomized clinical trial

AIMS

Pain can create physical and psychosocial discomfort. Pain management of patients with opioid misuse history can be challenging, in part due to their tolerance to opioids. Clonidine is an alpha-2 agonist that has been used for the reduction of anxiety and pain. The aim of this study was to investigate the effect of oral clonidine on pain outcomes in patients with a history of opioid use disorder presenting with orthopaedic fractures in the emergency room.

METHODS

In this blinded clinical trial in the emergency department, 70 opioid-dependent patients with orthopaedic fractures were divided into a control group of 35 and an intervention group of 35 subjects. Initially, 0.2 mg of oral clonidine was given to the intervention group and the control group received placebo tablets. Pain levels were recorded based on the Numerical Rating Scale rating before intervention, at 30 min, 1 h after intervention and at disposition from the emergency room (3-6 h after intervention). The total morphine requirement was also recorded.

RESULTS

The pain score of the clonidine group was significantly lower than that of the control group at 1 h and at disposition time. The amount of morphine required was significantly reduced in the clonidine group (P < 0.05). Oral clonidine had no significant effect on pulse rate. Oral clonidine was more effective for pain reduction in lower limb injuries.

CONCLUSION

Oral clonidine significantly reduced pain and the need for morphine in opioid-dependent patients with orthopaedic fractures.

Assessment of increased knowledge about traffic accidents prevention, one month after a presentation included in the program "it can happen to you" of AESLEME

Road traffic accidents are a real pandemic and incur expenses amounting to 1-2% of every country's GDP. AESLEME (Association for the Study of Spinal Cord Injuries) has celebrated its 30th anniversary here in Spain. AESLEME's instructors are health workers and people with spinal cord injuries caused by road accidents: their presentations-teaching road safety and sharing information on irreversible injuries-are enhanced by personal stories that help schoolchildren to acquire knowledge on this matter.

STUDY DESIGN

Pre and post-quasi-experimental study.

OBJECTIVE

To assess the increase in knowledge about road safety following a school-based road safety campaign.

METHODS

Two multiple-choice tests were given to each of the 8106 students taking part, who were 12-14 years old. Of the four possible answers, only one of them was correct. The first multiple-choice test was taken before the presentation and the second was taken one month later.

RESULTS

After assessing the answers, there was a change in the tendency of the number of correct before/after answers for the multiple-choice test, and the number of correct ones rose one month after the presentation. This increase is statistically significant (p < 0.01) and represents a national increase of 61% in the probability of correct answers, although this varies from 8% to 278% depending on the region.

CONCLUSIONS

The assessment, involving over 8000 people, showed that there has been an improvement in road safety knowledge thanks to education provided by AESLEME's instructors, and a statistically significant increase was obtained throughout Spain and different regions.

Sharing roads with automated vehicles: A questionnaire investigation from drivers', cyclists' and pedestrians' perspectives

Despite the promised benefits, the introduction of Automated Vehicles (AVs) on roads will be confronted by many challenges, including public readiness to use those vehicles and share the roads with them. The risk profile of road users is a key determinant of their safety on roads. However, the relation of such risk profiles to road users' perception of AVs is less known. This study aims to address the above research gap by conducting a cross-sectional survey to investigate the acceptance of Fully Automated Vehicles (FAVs) among different non-AV-user groups (i.e., pedestrians, cyclists, and conventional vehicle drivers). A total of 1205 road users in Queensland (Australia) took part in the study, comprising 456 pedestrians, 339 cyclists, and 410 drivers. The Theory of Planned Behaviour (TPB) is used as the theoretical model to examine road users' intention towards sharing roads with FAVs. The risk profile of the participants derives from established behavioural scales and individual characteristics are also included in the acceptance model. The study results show that pedestrians reported lowest intention in terms of sharing roads with FAVs among the three groups. Drivers and cyclists in a lower risk profile group were more likely to report higher intention to share roads with FAVs than those in a higher risk profile group. As age increased, pedestrians were less likely to accept sharing roads with FAVs. Drivers who had more exposure time on roads were more likely to accept sharing roads with FAVs. Male drivers reported higher intention towards sharing roads than female drivers. Overall, the study provides new insights into public perceptions of FAVs, specifically from the non-AV-user perspective. It sheds light on the obstacles that future AVs may encounter and the types of road users that AV manufacturers and policymakers should consider closely. Specifically, groups such as older pedestrians and road users who engage in more risky behaviours might resist or delay the integration of AVs.

Investigating the role of health belief model on seat belt use for front seat passengers on urban and rural roads

What makes a vehicle user buckle up? Considering the strong effect of seat belt use in reducing injuries and fatalities in a vehicle crash, we investigated the role of the health belief model on seat belt use among front-seat passengers on urban and rural roads. A questionnaire based on the theory components was randomly distributed in public areas of Sari, Iran. Structural equation model was used to test the study hypotheses. The results revealed that anticipated severity and perceived susceptibility directly affected seat belt use on urban roads, whereas perceived barriers had a reverse effect on seat belt use on urban roads. Perceived barriers with an indirect and perceived susceptibility with a direct effect, played an essential role in explaining seat belts use on rural roads. Outcomes of this study extend the knowledge of seat belts use behavior among front seat passengers by introducing new factors of potential influence, which could lead to practical solutions aimed to enhance seat belts utilization among these vehicle users and decrease the rate of injuries and fatalities in road crashes.

Exploring pathways from driving errors and violations to crashes: The role of instability in driving

This study explores how different driving errors, violations, and roadway environments contribute to safety-critical events through instability in driving speed. We harness a subsample (N = 9239) of the naturalistic driving study (NDS) data collected through the Second Strategic Highway Research Program (SHRP2). From a methodological standpoint, we use the safe systems approach relying on path analysis to jointly model outcomes. This accounts for the potential correlation between unobserved factors associated with both instability in driving speed and epoch (video stream) outcomes, i.e., baseline or event-free driving, near-crashes, and crashes. Tobit and ordered Probit regressions are estimated to model the coefficient of variation (COV) of speed and epoch outcomes, respectively. Results from the Tobit model indicate that driving errors and violations are associated with instability in the driving speed of the subject driver (COV of speed). The Probit model reveals that driving errors, violations, and instability in driving speed are associated with higher chances of crashes and near-crashes. Our key finding is that driving errors and violations not only induce event risk directly but also indirectly through instability in driving speed. For instance, recognition errors associate with higher crash risk by 6.78 % but this error is accompanied by instability in driving speed, which further increases event risk by 4.73 %, bringing the total increase in risk to 11.51 %. Moreover, significant correlations were found between unobserved factors reflected in the error terms of the two models. Ignoring such correlations can lead to inefficient parameter estimates. Based on the findings, practical implications are discussed, which can lead to effective countermeasures that effectively reduce crash risk.

Correlation Analysis of In-Vehicle Sensors Data and Driver Signals in Identifying Driving and Driver Behaviors

Today's cars have dozens of sensors to monitor vehicle performance through different systems, most of which communicate via vehicular networks (CAN). Many of these sensors can be used for applications other than the original ones, such as improving the driver experience or creating new safety tools. An example is monitoring variables that describe the driver's behavior. Interactions with the pedals, speed, and steering wheel, among other signals, carry driving characteristics. However, not always all variables related to these interactions are available in all vehicles; for example, the excursion of the brake pedal. Using an acquisition module, data from the in-vehicle sensors were obtained from the CAN bus, the brake pedal (externally instrumented), and the driver's signals (instrumented with an inertial sensor and electromyography of their leg), to observe the driver and car information and evaluate the correlation hypothesis between these data, as well as the importance of the brake pedal signal not usually available in all car models. Different sets of sensors were evaluated to analyze the performance of three classifiers when analyzing the driver's driving mode. It was found that there are superior results in classifying identity or behavior when driver signals are included. When the vehicle and driver attributes were used, hits above 0.93 were obtained in the identification of behavior and 0.96 in the identification of the driver; without driver signals, accuracy was more significant than 0.80 in identifying behavior. The results show a good correlation between vehicle data and data obtained from the driver, suggesting that further studies may be promising to improve the accuracy of rates based exclusively on vehicle characteristics, both for behavior identification and driver identification, thus allowing practical applications in embedded systems for local signaling and/or storing information about the driving mode, which is important for logistics companies.

La relación de los factores de personalidad y la conducción agresiva: los Cinco Grandes y la Tríada Oscura

El objetivo de esta investigación fue analizar el papel que los factores de personalidad pertenecientes a los Cinco Grandes y la Tríada Oscura —maquiavelismo, narcisismo y psicopatía— tienen en la conducción agresiva. La muestra se compuso de 318 estudiantes universitarios con permiso de conducir, quienes contestaron a una batería de pruebas que evaluaba los factores de personalidad de los Cinco Grandes (tipi), la Tríada Oscura (dd) y la conducción agresiva (das y dax). Los análisis de regresión jerárquica controlando las variables de edad y sexo, respaldan la utilidad predictiva de los factores de personalidad de los Cinco Grandes y la Tríada Oscura. Los resultados mostraron cómo la afabilidad, la estabilidad emocional y la apertura a la experiencia predicen de manera significativa diversos aspectos de la conducción agresiva. El maquiavelismo y el narcisismo son predictores significativos de diversas formas de expresión de la ira en la conducción, una vez controladoslos efectos de los Cinco Grandes.

Safe route-finding: A review of literature and future directions

While road navigation systems seek to determine the shortest routes between a given set of origin and destination points, there are certain situations in which the fastest route increases the risk of being involved in road crashes. This implies the necessity of integrating safe route-finding into road navigation systems. This study is designed to synthesize the literature on safe route-finding and identify the gaps in the literature for future research. Specifically, a scoping literature review methodology is applied to understand how safety is incorporated in route-finding, even beyond motor vehicle navigation systems. Three databases (Scopus, Web of Science, and IEEE Xplore) are explored, and controlling for inclusion criteria, 40 studies are included in this review. The findings of this review indicated five areas through which safety was considered in route-finding: motor vehicle navigation, public safety, public health, pedestrian and cyclist navigation, and hazardous material transportation. The measurement of safety was found challenging with inconsistencies in safety quantification approaches. The safe route-finding algorithms were investigated based on their predictive/reactive, static/dynamic, and centralized/decentralized characteristics. Based on the critical review of the safe route-finding algorithms, availability of real-time data sources, accurate real-time and disaggregated crash risk prediction models, trade-off between time and safety in road navigation tools, and centralized safe route-finding are highlighted as the requirements and challenges in considering safety in road navigation systems. This study outlines a research agenda to address the identified challenges in safe route-finding.

Computer vision-based approach to detect fatigue driving and face mask for edge computing device

The fatality of road accidents in this era is alarming. According to WHO, approximately 1.30 million people die each year in road accidents. Road accidents result in significant socioeconomic losses for people, their families, and the country. The integration of modern technologies into automobiles can help to reduce the number of people killed or injured in road accidents. Most of the study and police reports claim that fatigued driving is one of the deadliest factors behind many road accidents. This paper presents a complete embedded system to detect fatigue driving using deep learning, computer vision, and heart rate monitoring with Nvidia Jetson Nano developer kit, Arduino Uno, and AD8232 heart rate module. The proposed system can monitor the driver's real-time situations, then analyze the situation to detect any fatigue conditions and act accordingly. The onboard camera module constantly monitors the driver. The frames are retrieved and analyzed by the core system that uses deep learning and computer vision techniques to verify the situation with Nvidia Jetson Nano. The driver's states are identified using eye and mouth localization approaches from 68 distinct facial landmarks. Experimentally driven threshold data is employed to classify the states. The onboard heart rate module constantly measures the heart rates and detects any fluctuation in BPM related to the drowsiness. This system uses a convolutional neural network-based deep learning framework to include additional face mask detection to cope with the current pandemic situation. The heart rate module works parallelly where the other modules work in a conditional sequential manner to ensure uninterrupted detection. It will detect any sign of drowsiness in real-time and generate the alarm. The system successfully passed the initial lab tests and some actual situation experiments with 97.44% accuracy in fatigue detection and 97.90% accuracy in face mask identification. The automatic device was able to analyze different situations of drivers (different distances of driver from the camera, various lighting conditions, wearing eyeglasses, oblique projection) more precisely and generate an alarm before the accident happened.

An exploratory study of drivers' EEG response during emergent collision avoidance

INTRODUCTION

EEG (electroencephalogram) has been applied as a valuable measure to estimate drivers' mental status and cognitive workload during driving tasks. However, most previous studies have focused on the EEG features at particular driver status, such as fatigue or distraction, with less attention paid to EEG response in emergent and safety-critical situations. This study aims to investigate the underlying patterns of different EEG components during an emergent collision avoidance process.

METHOD

A driving simulator experiment was conducted with 38 participants (19 females and 19 males). The scenario included a roadside pedestrian who suddenly crossed the road when the driver approached. The participants' EEG data were collected during the pedestrian-collision avoidance process. The log-transformed power and power ratio of four typical EEG components (i.e., delta, theta, alpha and beta) were extracted from four collision avoidance stages: Stage 1-normal driving stage, Stage 2-hazard perception stage, Stage 3-evasive action stage, and Stage 4-post-hazard stage.

RESULTS

The activities of all four EEG bands changed consistently during the collision avoidance process, with the power increased significantly from Stage 1 to Stage 4. Drivers who collided with the pedestrian and drivers who avoided the collision successfully did not show a significant difference in EEG activity across the stages. Male drivers had a higher delta power ratio and lower alpha power ratio than females in both hazard perception and evasive action stages.

CONCLUSIONS

Enhanced activities of different EEG bands could be concurrent at emergent and safety-critical situations. Female drivers were more mentally aroused than male drivers during the collision avoidance process.

PRACTICAL APPLICATIONS

The study generates more understanding of drivers' neurophysiological response in an emergent and safety-critical collision avoidance event. Driver state monitoring and warning systems that aim to assist drivers in impending collisions may utilize the patterns of EEG activity identified in the collision avoidance process.

The Analysis of Classification and Spatiotemporal Distribution Characteristics of Ride-Hailing Driver's Driving Style: A Case Study in China

Monitoring the driving styles of ride-hailing drivers is helpful for providing targeted training for drivers and improving the safety of the service. However, previous studies have lacked analyses of the temporal variation as well as spatial variation characteristics of driving styles. Understanding the variations can also help authorities formulate driver management policies. In this study, trajectory data are used to analyze driving styles in various temporal and spatial scenarios involving 34,167 drivers. The k-means method is used to cluster sample drivers. In terms of driving style time-varying, we found that only 31.79% of drivers could maintain a stable driving style throughout the day. Spatially, we divided the research area into two parts, namely, road segments and intersections, to analyze the spatial driving characteristics of drivers with different styles. The speed distribution, the acceleration and deceleration distributions are analyzed, results indicated that aggressive drivers display more aggressive driving styles in road segments, and conservative drivers exhibit more conservative driving styles at intersections. The findings of this study provide an understanding of temporal and spatial driving behavior factors for ride-hailing drivers and offer valuable contributions to ride-hailing driver training and road safety management.

The Role of Crosswalks in the Smart City Concept Implementation from the “iGen” Perspective

In this article, the authors assumed that the “iGeneration” is the leading driving force for the SMART orientation of modern cities. Dynamic and multidirectional technical and technological processes introduce a new level of changes in urban space, adapting it to the present and future requirements of its inhabitants in a sustainable manner. An important infrastructure element of the urban space is the crosswalk, being an inseparable element of everyday life in the city. As part of a clear emphasis on the issue of vulnerable road users’ protection, the aim of the article is to examine the perception of users regarding crosswalks in Poland, based on the example of Szczecin. The main aim of the article is to identify the dimensions of crosswalk perception. The specific objectives include the determination of the state of knowledge about the essence and typology of crosswalks and the identification of good practices in their designation. Literature analysis, questionnaire research, and a case study were used. In the adopted research methodology, the use of the questionnaire made it possible to identify key intersections (Five Stars), each different in their form, and to learn about the perception dimensions of this element of urban space. In the context of the Smart City concept implementation, the perception of crosswalks by young city residents, i.e., the “iGeneration”, was examined. The obtained results allowed to perform a systematic analysis that focuses on individual behavioral aspects and subjectivism of the assessment of crosswalks in comparison with the commonly dominant architectural, engineering, and legal perspectives. The research allowed to assess the topology of intersections as well as the indication of safety improvement recommendation lists, taking into account intergenerational optics.

Ride-hailing services: Competition or complement to public transport to reduce accident rates. The case of Madrid

Introduction

The transport and mobility sector is experiencing profound transformations. These changes are mainly due to: environmental awareness, the increase in the population of large urban areas and the size of cities, the aging of the population and the emergence of relevant technological innovations that have changed consumption habits, such as electronic commerce or the sharing economy. The introduction of new services such as Uber or Cabify is transforming urban and metropolitan mobility, which has to adapt to this new scenario and the very concept of mobility.

Objective

Thus, the purpose of this study was to evaluate whether ride-hailing platforms substitute or complement public transport to reduce accident rates, considering the two basic transport zones of Madrid: “The Central Almond” and the periphery.

Methods

The data were collected from the 21 districts of Madrid for the period 2013–2019, and they were analyzed by a Random Effects Negative Binominal model.

Results

The results obtained in this study suggest that since the arrival of Uber and Cabify to the municipality of Madrid the number of fatalities and serious injuries in traffic accidents has been reduced. Traffic accidents on weekends and holidays, with at least one serious injury or death, have also been reduced. However, the number of minor injuries has increased in the central districts of Madrid.

Conclusion

Overall, what was found in this study supports the hypothesis that these services replace the urban buses. However, these services improve the supply to users with greater difficulties to access taxis or public transport, constituting an alternative mode of transport for high-risk drivers. Therefore, such findings may be quite useful for policy makers to better define regulatory policies for these services.

A Fuzzy-Logic Approach Based on Driver Decision-Making Behavior Modeling and Simulation

The present study proposes a decision-making model based on different models of driver behavior, aiming to ensure integration between road safety and crash reduction based on an examination of speed limitations under weather conditions. The present study investigated differences in road safety attitude, driver behavior, and weather conditions I-69 in Flint, Genesee County, Michigan, using the fuzzy logic approach. A questionnaire-based survey was conducted among a sample of Singaporean (n = 100) professional drivers. Safety level was assessed in relation to speed limits to determine whether the proposed speed limit contributed to a risky or safe situation. The experimental results show that the speed limits investigated on different roads/in different weather were based on the participants’ responses. The participants could increase or keep their current speed limit or reduce their speed limit a little or significantly. The study results were used to determine the speed limits needed on different roads/in different weather to reduce the number of crashes and to implement safe driving conditions based on the weather. Changing the speed limit from 80 mph to 70 mph reduced the number of crashes occurring under wet road conditions. According to the results of the fuzzy logic study algorithm, a driver’s emotions can predict outputs. For this study, the fuzzy logic algorithm evaluated drivers’ emotions according to the relation between the weather/road condition and the speed limit. The fuzzy logic would contribute to assessing a powerful feature of human control. The fuzzy logic algorithm can explain smooth relationships between the input and output. The input–output relationship estimated by fuzzy logic was used to understand differences in drivers’ feelings in varying road/weather conditions at different speed limits.

Modeling Motorcyclists’ Aggressive Driving Behavior Using Computational and Statistical Analysis of Real-Time Driving Data to Improve Road Safety and Reduce Accidents

Driving behavior is considered one of the most important factors in all road crashes, accounting for 40% of all fatal and serious accidents. Moreover, aggressive driving is the leading cause of traffic accidents that jeopardize human life and property. By evaluating data collected by various collection devices, it is possible to detect dangerous and aggressive driving, which is a huge step toward altering the situation. The utilization of driving data, which has arisen as a new tool for assessing the style of driving, has lately moved the concentration of aggressive recognition research. The goal of this study is to detect dangerous and aggressive driving profiles utilizing data gathered from motorcyclists and smartphone APPs that run on the Android operating system. A two-stage method is used: first, determine driver profile thresholds (rules), then differentiate between non-aggressive and aggressive driving and show the harmful conduct for producing the needed outcome. The data were collected from motorcycles using -Speedometer GPS-, an application based on the Android system, supplemented with spatiotemporal information. After the completion of data collection, preprocessing of the raw data was conducted to make them ready for use. The next steps were extracting the relevant features and developing the classification model, which consists of the transformation of patterns into features that are considered a compressed representation. Lastly, this study discovered a collection of key characteristics which might be used to categorize driving behavior as aggressive, normal, or dangerous. The results also revealed major safety issues related to driving behavior while riding a motorcycle, providing valuable insight into improving road safety and reducing accidents.

Associations between Post-Traumatic stress disorder symptoms and automobile driving behaviors: A review of the literature

Human factors are responsible for most motor vehicle accidents that occur on the road. Recent work suggests that symptoms of posttraumatic stress disorder (PTSD) are linked to reduced driving safety, yet none have provided a comprehensive review of this small, emerging literature. The present review identified twenty-two studies reporting associations between PTSD and driving behaviors. Among these, longitudinal designs (k = 3) and studies using objective driving performance measures (e.g., simulators) (k = 2) were rare. Most studies (k = 18) relied on brief screener measures of PTSD status/symptoms or a prior chart diagnosis, while few used a standardized structured interview measure to determine PTSD status (k = 4), and only a small number of studies assessed PTSD symptom clusters (k = 7). PTSD was most frequently associated with increased rates of hostile driving behaviors (e.g., cutting off others), unintentional driving errors (e.g., lapses in attention) and negative thoughts and emotions experienced behind the wheel. Findings regarding risk of motor vehicle accident and driving-related legal issues were variable, however relatively few studies (k = 5) explored these constructs. Future directions are discussed, including the need for work focused on concurrent PTSD symptom/driving-related changes, more comprehensive PTSD and driving assessment, and consideration of the contributions of comorbid traumatic brain injury history and other neurological and psychiatric conditions on driving outcomes.

Different brain activation patterns in the prefrontal area between self-paced and high-speed driving tasks

The purpose of this study was to investigate the effects on prefrontal cortex brain activity when participants attempted to stop a car accurately at a stop line when driving at different speeds using functional near-infrared spectroscopy (fNIRS). Twenty healthy subjects with driving experience drove their own cars for a distance of 60 m five times each at their own pace or as fast as possible. The variation in the distance between the stop line and the car was not significantly different between the self-paced and high-speed tasks. However, oxygenated hemoglobin concentration in the prefrontal cortex was significantly higher in the high-speed task than in the self-paced task. These findings suggest that driving at high speed requires more divided attention than driving at self-paced speed, even though the participants were able to stop the car at the same distance from the target. This study shows the advantages and usefulness of fNIRS .

The Indicator of GDI Engine Operating Mode and Its Influence on Eco-Driving

Elements of car construction, especially the information available on a dashboard, can stimulate the way of driving. The experiment described in the paper aimed to examine how the information provided by the indicator, which informs about the operational mode of a gasoline direct injection (GDI) engine, can contribute to eco-driving and the possible learning of acceleration pedal operation by a driver. The analysis of the fuel injection process affected by driver behaviour was an essential part of the experiment. The experiment was divided into two parts. The first one (nine tests) consisted of driving without access to the indicator information. In the second part, the information on the mode of the engine run was available for the driver. The results confirmed that the information about the type of fuel mixture used for the supply of the GDI engine facilitates an economical driving style (about 10% fuel savings) and motivates the driver to engage in eco-driving.

Study on the Maximum Safe Instantaneous Input of the Steering Wheel against Rollover for Trucks on Horizontal Curves

Truck rollover crashes on horizontal curves have been recognized as one of the most serious types of crashes. Driver’s instantaneous emergency steering maneuvers (DIESM) play an important role in truck rollover crashes, but have not received much attention. In the present study, the radius of curvature of the actual vehicle travel path (AVTP) under DIESM was calculated based on the transient bicycle model. Rollover margins were used to evaluate the truck-rollover potential under DIESM. To calculate rollover margins, the lateral acceleration under DIESM was calculated based on the radius of the curvature of the AVTP. A rollover threshold formula was introduced to calculate vehicle’s rollover thresholds by distinguishing two turning conditions. According to rollover margins, the maximum safe instantaneous input of the steering wheel against rollover for trucks was obtained. Moreover, theoretical results were verified by computer simulation. Results showed: (1) The maximum safe instantaneous inputs of the steering wheel were 259°, 212°, 182°, 162°and 147°, respectively, at speeds of 60 km/h, 70 km/h, 80 km, 90 km and 100 km when the superelevation rate was 0, and (2) superelevation significantly affected truck-rollover potential; the worst turning condition was turning from the inside to the outside of the curve. Due to the consideration of the wheelbase, the centroid position, the tire’s cornering stiffness and the suspension roll gain, the prediction results were more accurate.

A Recognition Method of Aggressive Driving Behavior Based on Ensemble Learning

Aggressive driving behavior (ADB) is one of the main causes of traffic accidents. The accurate recognition of ADB is the premise to timely and effectively conduct warning or intervention to the driver. There are some disadvantages, such as high miss rate and low accuracy, in the previous data-driven recognition methods of ADB, which are caused by the problems such as the improper processing of the dataset with imbalanced class distribution and one single classifier utilized. Aiming to deal with these disadvantages, an ensemble learning-based recognition method of ADB is proposed in this paper. First, the majority class in the dataset is grouped employing the self-organizing map (SOM) and then are combined with the minority class to construct multiple class balance datasets. Second, three deep learning methods, including convolutional neural networks (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU), are employed to build the base classifiers for the class balance datasets. Finally, the ensemble classifiers are combined by the base classifiers according to 10 different rules, and then trained and verified using a multi-source naturalistic driving dataset acquired by the integrated experiment vehicle. The results suggest that in terms of the recognition of ADB, the ensemble learning method proposed in this research achieves better performance in accuracy, recall, and F₁-score than the aforementioned typical deep learning methods. Among the ensemble classifiers, the one based on the LSTM and the Product Rule has the optimal performance, and the other one based on the LSTM and the Sum Rule has the suboptimal performance.

Risk-taking behaviors of e-scooter users: A survey in Paris

Risk-taking behavior is often held responsible for increased crash involvement. We designed and undertook a face-to-face road survey (N = 459) in order to explore incident involvement history, driving attitudes and perceived risk among e-scooter users is Paris, France. Three risk factors were specifically explored: (i) riding after having consumed alcohol, (ii) riding after having consumed drugs, and (iii) using the smartphone while riding. The relationship between these factors and user attributes (such as age and gender) and travel behavior (such as frequency of e-scooter usage and trip duration) was examined using logit and mixed logit specifications and a structural equation model. Empirical evidence suggests that it is more likely for young and male riders to develop risky behaviors. Longer trip durations seem to be associated with risk-taking behaviors.

Crash proneness? Predictors of repeat crashes in older drivers

INTRODUCTION

Older drivers are believed to be prone to crashes due to age-related deterioration of their driving abilities. Currently, little is known about the characteristics of repeat crashers and the factors that predict subsequent crashes among these older drivers.

METHOD

A dataset containing the records of crash events that occurred between January 2014 and November 2019 was provided by the Department of Transport and Main Roads (DTMR) in Queensland, Australia. This dataset included 16,973 records of older drivers involved in a single crash and 222 cases in multiple crashes, comprising a total of 17,195 cases. Descriptive and inferential analyses were performed to understand the characteristics of repeat crashers. Survival analysis techniques were used to determine risk factors predictive of subsequent crashes.

RESULTS

Nearly half (46%) of the repeat crashers were culpable for both of their crashes. Their average age was significantly older than those who were culpable for none or one of their crashes. For older male drivers, riding a motorcycle or driving a heavy vehicle were significant risk factors for having a subsequent crash. The risk for female at-fault drivers being involved in a subsequent crash was 4.53 times greater than those not at-fault. Older female drivers involved in crashes caused by slowing or stopping also presented a higher risk of being involved in subsequent crashes.

CONCLUSIONS

This study identified risk factors for older drivers being involved in repeat crashes; distinctive gender differences in the risk for involvement in repeat crashes were found. Practical Applications: To reduce the likelihood of older drivers being involved in subsequent crashes, attention should be directed towards elders living in major cities, male motorcycle riders and heavy vehicle drivers, and at-fault female drivers.

Driving Behavior Based Relative Risk Evaluation Using a Nonparametric Optimization Method

Evaluating risks when driving is a valuable method by which to make people better understand their driving behavior, and also provides the basis for improving driving performance. In many existing risk evaluation studies, however, most of the time only the occurrence frequency of risky driving events is considered in the time dimension and fixed weights allocation is adopted when constructing a risk evaluation model. In this study, we develop a driving behavior-based relative risk evaluation model using a nonparametric optimization method, in which both the frequency and the severity level of different risky driving behaviors are taken into account, and the concept of relative risk instead of absolute risk is proposed. In the case study, based on the data from a naturalistic driving experiment, various risky driving behaviors are identified, and the proposed model is applied to assess the overall risk related to the distance travelled by an individual driver during a specific driving segment, relative to other drivers on other segments, and it is further compared with an absolute risk evaluation. The results show that the proposed model is superior in avoiding the absolute risk quantification of all kinds of risky driving behaviors, and meanwhile, a prior knowledge on the contribution of different risky driving behaviors to the overall risk is not required. Such a model has a wide range of application scenarios, and is valuable for feedback research relating to safe driving, for a personalized insurance assessment based on drivers' behavior, and for the safety evaluation of professional drivers such as ride-hailing drivers.

The Effects of Driving Experience on the P300 Event-Related Potential during the Perception of Traffic Scenes

The incidence of human-error-related traffic collisions is markedly reduced among drivers who have few years of driving experience compared with those with little driving experience or fewer driving opportunities, even if they have a driver's license. This study analyzes the effect of driving experience on the perception of the traffic scenes through electroencephalograms (EEGs). Primarily, we focused on visual attention during driving, the essential visual function in the visual search and human gaze, and evaluated the P300, which is involved in attention, to explore the effect of driving experience on the visual attention of traffic scenes, not for improving visual ability. In the results, the P300 response was observed in both experienced and beginner drivers when they paid visual attention to the visual target. Furthermore, the latency for the peak amplitude of the P300 response among experienced drivers was markedly faster than that in beginner drivers, suggesting that the P300 latency is a piece of crucial information for driving experience on visual attention.

Examining risky riding behavior in India using Motorcycle rider behavior questionnaire

Powered two-wheelers (PTW) constitute the most vulnerable type of road user, primarily due to their lack of protection compared to other motorized vehicles, which can lead to a severe accident in the event of crashes. A notable increase in the percentage of PTW accidents resulting in fatalities has raised a serious need for further research in understanding riding behavior. The Motorcycle rider behavior questionnaire (MRBQ) based studies have shown promising results by using MRBQ to relate riding behavior with crash risk. Despite numerous studies using the MRBQ technique and inconsistency in derived inferences across the studies highlighted the need to revise MRBQ and carry out predictive validity for capturing the correct riding behavior of Indian riders. Therefore, this research modified the previously available questionnaire by considering the focus group's opinion, consisting of twenty professional riders, two transportation experts, and two traffic police officers. Additionally, the predictive validity check of MRBQ was carried out using a sample of Indian riders consisting of 392 participants. The exploratory factor analysis of the MRBQ revealed a 32 item version of the questionnaire divided into a four-factor structure (traffic errors, control errors, speed violations, and stunts). The present research highlighted some critical dissimilarities between PTW riders of India and other countries. The low mean score (based on the Likert scale) of the items under the four-factor structure indicated overall a safe PTW rider behavior of Indian riders. Among the four factors, speed violation showed the highest mean score and stunts showed the least mean score indicating frequent and infrequent aberrant ridding behaviors, respectively. A known group construct validity check revealed that gender had a significant and age an insignificant effect on the reporting of aberrant riding behaviors. Furthermore, a negative binomial regression analysis revealed that traffic error had the highest incidence rate ratio, confirming it to be the most significant predictor of crash risk for Indian riders. Finally, the study briefly discussed counter-measure strategies targeting specific riding behavior.

Urban population size and road traffic collisions in Europe

Millions of road traffic collisions take place every year, leading to significant knock-on effects. Many of these traffic collisions take place in urban areas, where traffic levels can be elevated. Yet, little is known about the extent to which urban population size impacts road traffic collision rates. Here, we use urban scaling models to analyse geographic and road traffic collision data from over 300 European urban areas in order to study this issue. Our results show that there is no significant change in the number of road traffic collisions per person for urban areas of different sizes. However, we find individual urban locations with traffic collision rates which are remarkably high. These findings have the potential to inform policies for the allocation of resources to prevent road traffic collisions across the different cities.

Research on the speed thresholds of trucks in a sharp turn based on dynamic rollover risk levels

Truck rollover is a problem that seriously endangers the safety of human life. Under special conditions, when the driver takes a sharp turn, the truck is most prone to rollover. Speed seriously affects the driving stability of the truck in a sharp turn, but the calculation of the safe speed is not accurate enough at present. The aim of this paper is to develop a more accurate safe speed calculation method to avoid the truck rollover in a sharp turn. Firstly, the calculation formula of the rollover threshold was derived based on a theoretical model, then, the simulation tests were carried out. We selected a 4-axle truck with a total weight of 30t as the subject, simulated the dynamic process of the truck rollover in a sharp turn with TruckSim, evaluated the dynamic rollover risk levels of the truck during this process, and verified the accuracy of the simulation results by results of the theoretical model. Finally, by analyzing the steering principle of the vehicle, the safe speed threshold and the limit speed threshold of the truck in a sharp turn were calculated according to the lateral acceleration corresponding to the rollover risk levels. The results show that no matter what the loading condition of the truck is, when the rollover margin is reduced to about 0.15g, the truck just reaches the risk level of critical rollover; the result provides an accurate algorithm for speed thresholds of the truck when turning radius is less than 250 m. The research provides a calculation method for safe speed of trucks from a dynamic perspective. The research results can be applied to the speed warning system of trucks, which can make drivers better control the rollover risk of trucks in the process of driving and improve driving safety.

A path model approach to safety compliance and personal factors among electrical workers in India

This paper reports the findings from a safety research survey conducted among the employees of a large public sector electrical utility in the state of Kerala, India. Response of 3017 employees was collected by one-to-one interaction using the developed instrument. Personal factors like self-esteem, job stress, personal stress, social supports, and fatigue of the targeted population were measured. Personal safety climate factors of the utility were accessed by modifying the existing safety climate scales. Statistical analysis confirmed the reliability and validity of the factors in the study. A significant path model of personal and safety climate factors was developed. Seven research hypotheses were validated by using statistical analysis. The results of the study highlighted the need for safety participation, safety knowledge, safety training, and interventions to reduce personal issues in the workplace. These findings provide valuable insights to safety professionals for implementing novel methods to ensure workplace safety.

Analyzing traffic violations among motorcyclists using structural equation modeling

In developing countries, the motorcycle is one of the main modes of transportation, especially in the downtown area. Motorcyclists' safety is a common issue since they have more vulnerability for severe crashes. Thus, it is necessary to decrease the probability of encountering high-risk situations. The main reason for motorcycle crashes is human errors in the form of traffic violations. This article aims to identify the role of seven factors on traffic violations, including motorcyclist characteristics, road conditions, environmental circumstances, riding skills, cycling statuses, motorcycle age and previous negative experiences such as fines and accidents. The structural equation modeling was applied to evaluate the impact of these factors pertinent to motorcyclists' crashes. To assess these factors, 600 motorcyclists have been interviewed in Kerman, Iran. The results indicated that motorcyclists' characteristics are the most effective factor in violation commitment. Since most motorcyclists are young, with low income and education, it is necessary to pay more attention to their training and education before giving a cycling license. In addition, those with more previous crashes and violations are more susceptible to committing violations. This relates to the lack of enough control and enforcement in developing cities; also, it shows that the current traffic fines are not deterrent enough. Finally, considering the results of this research can help to minimize traffic violations among motorcyclists, which is a step towards safer roads.

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.

Built environment, driving errors and violations, and crashes in naturalistic driving environment

Driving errors and violations are highly relevant to the safe systems approach as human errors tend to be a predominant cause of crash occurrence. In this study, we harness highly detailed pre-crash Naturalistic Driving Study (NDS) data 1) to understand errors and violations in crash, near-crash, and baseline (no event) driving situations, and 2) to explore pathways that lead to crashes in diverse built environments by applying rigorous modeling techniques. The "locality" factor in the NDS data provides information on various types of roadway and environmental surroundings that could influence traffic flow when a precipitating event is observed. Coded by the data reductionists, this variable is used to quantify the associations of diverse environments with crash outcomes both directly and indirectly through mediating driving errors and violations. While the most prevalent errors in crashes were recognition errors such as failing to recognize a situation (39 %) and decision errors such as not braking to avoid a hazard (34 %), performance errors such as poor lateral or longitudinal control or weak judgement (8 %) were most strongly correlated with crash occurrence. Path analysis uncovered direct and indirect relationships between key built-environment factors, errors and violations, and crash propensity. Possibly due to their complexity for drivers, urban environments are associated with higher chances of crashes (by 6.44 %). They can also induce more recognition errors which correlate with an even higher chances of crashes (by 2.16 % with the "total effect" amounting to 8.60 %). Similar statistically significant mediating contributions of recognition errors and decision errors near school zones, business or industrial areas, and moderate residential areas were also observed. From practical applications standpoint, multiple vehicle technologies (e.g., collision warning systems, cruise control, and lane tracking system) and built-environment (roadway) changes have the potential to reduce driving errors and violations which are discussed in the paper.

Identifying Interactive Factors That May Increase Crash Risk between Young Drivers and Trucks: A Narrative Review

Sharing the road with trucks is associated with increased risk of serious injury and death for passenger vehicle drivers. However, the onus for minimising risk lies not just with truck drivers; other drivers must understand the unique performance limitations of trucks associated with stopping distances, blind spots, and turning manoeuverability, so they can suitably act and react around trucks. Given the paucity of research aimed at understanding the specific crash risk vulnerability of young drivers around trucks, the authors employ a narrative review methodology that brings together evidence from both truck and young driver road safety research domains, as well as data regarding known crash risks for each driving cohort, to gain a comprehensive understanding of what young drivers are likely to know about heavy vehicle performance limitations, where there may be gaps in their understanding, and how this could potentially increase crash risk. We then review literature regarding the human factors affecting young drivers to understand how perceptual immaturity and engagement in risky driving behaviours are likely to compound risk regarding both the frequency and severity of collision between trucks and young drivers. Finally, we review current targeted educational initiatives and suggest that simply raising awareness of truck limitations is insufficient. We propose that further research is needed to ensure initiatives aimed at increasing young driver awareness of trucks and truck safety are evidence-based, undergo rigorous evaluation, and are delivered in a way that aims to (i) increase young driver risk perception skills, and (ii) reduce risky driving behaviour around trucks.

No effect of the Penalty Point System on road traffic accident mortality among men with a high socioeconomic status in Spain

The purpose of this study was to assess the effect of the Penalty Point System (PPS) on road traffic accident mortality by gender and socioeconomic status. We conducted a nationwide prospective study covering adult people living in Spain on November 2001. They were followed up until 30 Nov 2007 to determine vital status and cause of death. An interrupted time-series analysis was used to assess whether PPS (explanatory variable) had both immediate and long-term effect on the rates of road traffic accident mortality (RTAMs) separately by gender. Subjects were classified by socioeconomic status (low and high) using two indicators: educational attainment (up to lower secondary education; upper secondary education or more) and occupation (manual and non-manual workers). We performed several segmented Poisson regression models, controlling for trend, seasonality, 2004 road safety measures and fuel consumption as proxy for traffic exposure. Among men, we found a decrease on the RTAMs immediately after PPS in those with low educational level (16.2 %, IC95 %: 6.1 %-25.2 %) and manual workers (16.3 %, IC95 %: 2.8 %-27.8 %), and a non-significant increase among those with high education level and non-manual workers (6.2 % and 1.8 %). Among women, there were no significant differences in the immediate effect of PPS by socioeconomic status. We did not identify significant trend changes between pre-PPS and post-PPS periods in any socioeconomic group. In a context of downward trend of traffic mortality, the PPS implementation led to an immediate reduction on death rates only among men with a low socioeconomic status.

Applications of brain imaging methods in driving behaviour research

Applications of neuroimaging methods have substantially contributed to the scientific understanding of human factors during driving by providing a deeper insight into the neuro-cognitive aspects of driver brain. This has been achieved by conducting simulated (and occasionally, field) driving experiments while collecting driver brain signals of various types. Here, this sector of studies is comprehensively reviewed at both macro and micro scales. At the macro scale, bibliometric aspects of these studies are analysed. At the micro scale, different themes of neuroimaging driving behaviour research are identified and the findings within each theme are synthesised. The surveyed literature has reported on applications of four major brain imaging methods. These include Functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS) and Magnetoencephalography (MEG), with the first two being the most common methods in this domain. While collecting driver fMRI signal has been particularly instrumental in studying neural correlates of intoxicated driving (e.g. alcohol or cannabis) or distracted driving, the EEG method has been predominantly utilised in relation to the efforts aiming at development of automatic fatigue/drowsiness detection systems, a topic to which the literature on neuro-ergonomics of driving particularly has shown a spike of interest within the last few years. The survey also reveals that topics such as driver brain activity in semi-automated settings or neural activity of drivers with brain injuries or chronic neurological conditions have by contrast been investigated to a very limited extent. Potential topics in driving behaviour research are identified that could benefit from the adoption of neuroimaging methods in future studies. In terms of practicality, while fMRI and MEG experiments have proven rather invasive and technologically challenging for adoption in driving behaviour research, EEG and fNIRS applications have been more diverse. They have even been tested beyond simulated driving settings, in field driving experiments. Advantages and limitations of each of these four neuroimaging methods in the context of driving behaviour experiments are outlined in the paper.

Traffic climate, driver behaviour and dangerous driving among taxi drivers

Traffic accidents are considered a major public health problem in many countries. The aim of this study was to explore the relationships between traffic climate factors (i.e., external affective demands, functionality and internal requirements), driver behaviours, dangerous driving behaviours and traffic accident involvement among taxi drivers. A total of 450 male taxi drivers participated in the study. The traffic climate scale (TCS), the driver behaviour questionnaire (DBQ), the positive driver behaviours scale (PDBS) and the Dula dangerous driving index (DDDI) were used to measure driving behaviours and traffic conditions. The results showed that there was a significant negative correlation between functionality (of the TCS) and the number of accident involvement. Further analysis demonstrated that the effect of risky driving (of the DDDI) on accident involvement was significant. Taken together, these findings suggest that functional traffic systems and driving environments play important roles in traffic accident involvement.

Fuzzy analysis of students' perception of traffic safety in school environments: the case of a small Brazilian city

Thousands of lives are lost every year due to traffic accidents worldwide, and youths are the most affected. The goal of this paper is to analyze the differences in young students' perceptions about traffic safety in school surroundings in order to help in the formulation of public policies and the development of infrastructure to make school travels safer. A questionnaire was used to obtain the perception of high school students from institutions with different urban characteristics. Data modelling with fuzzy logic and statistical analysis of variance indicated that students' perceptions are influenced by the different realities these youths are exposed to daily, such as school socioeconomic category, transport mode, urban environment and gender. Traffic engineering measures, public policies and road safety education action, inciting active mobility, can be validated and supported by these results. This road safety analysis may also be a participative alternative for locations with low data access.

A taxonomy of driving errors and violations: Evidence from the naturalistic driving study

Driving errors and violations are identified as contributing factors in most crash events. To examine the role of human factors and improve crash investigations, a systematic taxonomy of driver errors and violations (TDEV) is developed. The TDEV classifies driver errors and violations based on their occurrence during the theoretically based perception-reaction process and analyzes their contributions in safety critical events. To empirically explore errors and violations, made by drivers of instrumented vehicles, in diverse built environments, this study harnesses unique and highly detailed pre-crash sensor data collected in the Naturalistic Driving Study (NDS), containing 673 crashes, 1,331 near-crashes and 7,589 baselines (no-event). Human factors are categorized into recognition errors, decision errors, performance errors, and errors due to the drivers' physical condition or their lack of contextual experience/familiarity, and intentional violations. In the NDS data, built environments (measured by roadway localities) are classified based on roadway functional classification and land uses, e.g., residential areas, school zones, and church zones. Based on the crash percentage to baseline percentage in a specific locality, interstates and open country/open residential (rural and semi-rural settings) may pose lower risks, while urban, business/industrial, and school zone locations showed higher crash risk. Human errors and violations by instrumented vehicle drivers contributed to 93% of the observed crashes, while roadway factors contributed to 17%, vehicle factors contributed in 1%, and 4% of crashes contained unknown factors. The most common human errors were recognition and decision errors, which occurred in 39% and 34% of crashes, respectively. These two error types occurred more frequently (each contributing to nearly 39% of crashes) in business or industrial land use environments (but not in dense urban localities). The findings of this study reveal continued prevalence of human factors in crashes. The distribution of driving errors and violations across different roadway environments can aid in the implementation of driver assistance systems and place-based interventions that can potentially reduce these driving errors and violations.

Regulating Road Rage

Road rage has been a problem since the advent of cars. Given the ubiquity of road rage, and its potentially devastating consequences, understanding road rage and developing interventions to curb it are important priorities. Emerging theoretical and empirical advances in the study of emotion and emotion regulation have provided new insights into why people develop road rage and how it can be prevented and treated. In the current article, we suggest an integrative conceptual framework for understanding road rage, based upon a psychological analysis of emotion and emotion regulation. We begin by defining road rage and other key constructs. We then consider the interplay between road rage generation and road rage regulation. Using an emotion regulation framework, we describe key points at which emotion-regulation difficulties can lead to road rage, followed by strategies that may alleviate these difficulties. We suggest that this framework usefully organizes existing research on road rage, while exposing key directions for future research.

Analyzing driver behavior under naturalistic driving conditions: A review

For a decade, researchers working in the area of road safety have started exploring the use of driving behavior data for a better understanding of the causes related to road accidents. A review of the literature reveals the excellent potential of naturalistic driving studies carried out by collecting vehicle performance data and driver behavior data during normal, impaired, and safety-critical situations. An in-depth understanding of driver behavior helps analyze and implement pre-crash safety measures - the development of enforcement policies, infrastructure design, and intelligent vehicle safety systems. The present paper attempts to review the naturalistic driving studies that have been undertaken so far. The paper begins with an overview of different methods for collecting unobtrusive driver behavior data during their day to day trip, followed by a discussion of various factors affecting driving behavior and their influence on vehicle performance parameters. The paper also discusses the strategies mentioned in the literature for improving driving behavior using naturalistic driving studies to enhance road safety. Some of the major findings of this review suggest that i) driver behavior is a major cause in the majority of the road accidents ii) drivers generally reduce their speed and increases headway as a compensatory measure to reduce the workload imposed during distracting activity and adverse weather conditions iii) mobile phone has emerged as a potential device for collecting naturalistic driving data and, iv) improvement in driving behavior can be achieved by providing feedback to the drivers about their driving behavior. This can be done by implementing usage-based insurance schemes such as pay as you drive (PAYD), pay how you drive (PHYD), and manage how you drive (MHYD). While a considerable amount of research has been done to analyze driving behavior under naturalistic conditions, some areas which are yet to be explored are highlighted in the present paper.

Socioeconomic Status, Health and Lifestyle Settings as Psychosocial Risk Factors for Road Crashes in Young People: Assessing the Colombian Case

The social determinants of health influence both psychosocial risks and protective factors, especially in high-demanding contexts, such as the mobility of drivers and non-drivers. Recent evidence suggests that exploring socioeconomic status (SES), health and lifestyle-related factors might contribute to a better understanding of road traffic crashes (RTCs). Thus, the aim of this study was to construct indices for the assessment of crash rates and mobility patterns among young Colombians who live in the central region of the country. The specific objectives were developing SES, health and lifestyle indices, and assessing the self-reported RTCs and mobility features depending on these indices. A sample of 561 subjects participated in this cross-sectional study. Through a reduction approach of Principal Component Analysis (PCA), three indices were constructed. Mean and frequency differences were contrasted for the self-reported mobility, crash rates, age, and gender. As a result, SES, health and lifestyle indices explained between 56.3–67.9% of the total variance. Drivers and pedestrians who suffered crashes had higher SES. A healthier lifestyle is associated with cycling, but also with suffering more bike crashes; drivers and those reporting traffic crashes have shown greater psychosocial and lifestyle-related risk factors. Regarding gender differences, men are more likely to engage in road activities, as well as to suffer more RTCs. On the other hand, women present lower healthy lifestyle-related indices and a less active implication in mobility. Protective factors such as a high SES and a healthier lifestyle are associated with RTCs suffered by young Colombian road users. Given the differences found in this regard, a gender perspective for understanding RTCs and mobility is highly suggestible, considering that socio-economic gaps seem to differentially affect mobility and crash-related patterns.