Analyzing road traffic accidents through identification and prioritization of accident-prone areas on the dembecha to injibara highway segment in amhara region, ethiopia.

Every year, millions die in road accidents globally, imposing significant economic and humanitarian costs. While road traffic accidents are a major health concern, many developing countries, including Ethiopia, struggle to address this issue effectively. Ethiopia ranks second in East Africa for severe road traffic accidents, highlighting the need for improved injury reduction strategies. This study introduces a novel approach by chronologically identifying and prioritizing accident black spots in the studied area, Ethiopia. This method provides a valuable tool for transportation authorities and traffic police to target high-risk areas for immediate intervention. Focusing on the Dembecha-Injibara highway segment, the study employs both descriptive and inferential analyses, using the Zegeer method to calculate accident rates. It also uses factors of weight contributing to road traffic accidents and their severity to rank accident-prone areas. The findings reveal that areas near Finote Selam, Banja, and Burie are highly prone to severe accidents, with specific accident frequencies and priority values identified. Recommendations are offered to address these high-risk areas and mitigate severe traffic accidents in the study region.

Implementation of stroke care & road safety in India: Lessons from Australia.

India is home to one-fifth of the world's population and is currently the fastest-growing economy. As the health industry is growing, India needs to develop robust implementation of evidence-based health care addressing the major public health issues. Two of such issues India is grappling with are the establishment of stroke care and the reduction of road accidents. Australia has achieved notable success in implementing stroke care and reducing road accidents. In stroke, Australian initiatives include dedicated stroke units, the development of clinical guidelines, the implementation of acute interventions, the establishment of a national stroke foundation, and the stroke registry. As a result, the combined, primary, and secondary prevention measures, acute treatment, and rehabilitation have reduced the total disease burden of stroke from 2003 to 2023 by 53 per cent, from 7.4 to 3.5 Disability Adjusted Life Years (DALYS) per 1,000 population, which is a 56 per cent decline in fatal burden and 23 per cent decline in non-fatal burden. For road safety, Australia implemented evidence-based practices such as education, legislation including mandatory use of seat belts, and other road safety initiatives. Data show that seat belt use reached 98 per cent in Australia in 2023. Furthermore, about 20 per cent of drivers as well as passengers who were killed in crashes in 2024 did not wear seat belts. The reduction of speed limits in built-up areas, the adoption of monitoring technology, and the clever use of infrastructure are proving to be effective in reducing fatalities and serious injuries. Australia's implementation research can provide valuable insights into the efforts of mitigating the impact of stroke and enhancing road safety in India.

Assessing public trust in road traffic injuries prevention policies in Iran: a cross-sectional study.

INTRODUCTION AND OBJECTIVE: Road Traffic Injuries (RTIs) represent the predominant cause of morbidity and mortality globally. Thus, impactful interventions and trustworthy authorities are imperative to mitigate traffic accidents. The present study seeks to assess public trust in traffic accident diminution policies and measures within the country of Iran. METHOD: This cross-sectional study utilized a questionnaire comprising 31 questions distributed across eight dimensions. The questionnaire's design was informed by a literature review and semi-structured interviews with experts specializing in traffic accidents. The target population consisted of Tabriz city residents, who were selected via convenience sampling. The collected data were then analyzed using SPSS-16 software, employing the T-test, One-way ANOVA, and Pearson correlation coefficient to present the study's results. structural equation modeling (SEM) was conducted using R 4-4-2 programming language. RESULTS: A total of 681 participants completed the questionnaire, yielding a commendable response rate of 88.6%. The majority of participants were male (60.8%), and 86.2% identified as drivers, with 61.8% having experienced driving accidents. The overall score for public trust in RTIs policies was calculated to be 46.9%. The individual scores for specific domains of trust in traffic safety were as follows: safer roads (43.1%), safe vehicle (43.3%), safety laws (48.8%), safe user (51.1%), safe technology (51.5%), road safety management (46.8%). All the latent variables except education are significantly effective on public trust. CONCLUSION: The study findings indicate relatively low levels of public trust in RTIs policies among the Iranian population. These results emphasize the need for targeted interventions to enhance public confidence in specific aspects of traffic safety. Policymakers can use these insights to implement effective measures, thus contributing to the RTIs and the promotion of road safety in the country.

Human reliability of the intelligent construction site tower crane driver interface based on DEMATEL-ISM-BN.

With the arrival of Industry 4.0, intelligent construction sites have seen significant development in China. However, accidents involving digitized tower cranes equipped with smart systems continue to occur frequently. Among the main causes of these accidents is human unsafe behavior. To assess the human factors reliability of intelligent construction site tower cranes, it is necessary to shift the safety focus to the human-machine interface and identify patterns of human error behaviors among tower crane drivers through text mining techniques (TF-IDF-TruncatedSVD-ComplementNB). Based on the SHEL model, the behavioral factors influencing human factors reliability in the human-machine interface are categorized and a Performance Shaping Factors (PSF) system is constructed. Building on the foundation of constructing an indicator system for human factors error influence in the driver interface of intelligent construction site tower cranes, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is combined with the Interpretive Structural Modeling (ISM) to analyze the importance of various factors in causing human errors and to analyze the logical structure among these factors. Simultaneously, a Bayesian network is constructed using a multi-level hierarchical structural model, thus establishing a new evaluation method for the human-machine interface. The effectiveness of the proposed method is validated through Bayesian network causal inference based on real case studies. The results demonstrate that the evaluation process of this method aligns with the operational scenarios of tower crane drivers in intelligent construction sites. It not only allows for quantifying the likelihood of human errors but also enables the development of targeted measures for controlling unsafe behaviors among tower crane drivers in intelligent construction sites.

Development and Validation of a Novel Night-Time Hazard Visibility Test.

Purpose: Night-time driving is dangerous, with increased crash rates, particularly involving vulnerable road users. A Night-Time Hazard Visibility Test (NHVT) was developed and validated by exploring the effects of refractive and cataract blur on performance. Methods: The NHVT comprised video clips of night-time roads from the driver's perspective, including different hazards (pedestrians, cyclists, and vehicles). Participants responded when they first recognized hazards requiring them to take evasive action to avoid a collision. In experiment 1, there were 16 young visually normal drivers (mean age = 22.3, standard deviation [SD] = 2.2 years) who completed 2 NHVT sets, viewed separately through best-correction and refractive blur (+1.00 diopter sphere [DS]). In experiment 2, a refined version of the NHVT was administered to an additional 16 young visually normal drivers (mean age = 21.1, SD = 1.2 years) with best-correction and cataract blur. The order of visual conditions and NHVT sets were counterbalanced. Results: In experiment 1, refractive blur significantly reduced photopic visual acuity (VA) compared to best-corrected vision (+0.09 vs. -0.21 logMAR, P < 0.001) and delayed response times by 0.69 seconds (3.10 vs. 2.41 seconds, P < 0.001) compared to best-corrected vision. In experiment 2, cataract blur reduced VA compared to best-corrected vision (+0.03 vs. -0.17 logMAR, P < 0.001) and delayed response times by 0.63 seconds (2.92 vs. 2.29 seconds, P < 0.001). Conclusions: The NHVT was sensitive to refractive and cataract blur, providing preliminary support of its validity as a measure of night-time hazard visibility performance. Translational Relevance: The NHVT has potential as an off-road assessment of visibility for night driving and application for assessment of drivers with different refractive corrections and ocular diseases.

The Safe System Approach and technology-what works?

The Safe System Approach is an evidence-based strategy committed to eliminating fatalities and serious injuries among all road users. The Safe System Approach as developed in Sweden, acknowledges that human errors will occur, but the cost of these mistakes should not be death or serious injury. Technology is an integral component of the Safe System Approach and can address equity and reduce human error among other safety benefits. A literature review will be conducted to compare high-income countries leveraging the Safe System Approach and assess opportunities for technology interventions in low- and middle-income contexts. Evidence will be analyzed, as well as implementation considerations of the recent National adoption of the Safe System Approach in the United States. As SSA evolves in a global context, further evaluation is needed on the role of technology and how government policies can restrict or advance its implementation.

IoT-Based Assessment of a Driver's Stress Level.

Driver Monitoring Systems (DMSs) play a key role in preventing hazardous events (e.g., road accidents) by providing prompt assistance when anomalies are detected while driving. Different factors, such as traffic and road conditions, might alter the psycho-physiological status of a driver by increasing stress and workload levels. This motivates the development of advanced monitoring architectures taking into account psycho-physiological aspects. In this work, we propose a novel in-vehicle Internet of Things (IoT)-oriented monitoring system to assess the stress status of the driver. In detail, the system leverages heterogeneous components and techniques to collect driver (and, possibly, vehicle) data, aiming at estimating the driver's arousal level, i.e., their psycho-physiological response to driving tasks. In particular, a wearable sensorized bodice and a thermal camera are employed to extract physiological parameters of interest (namely, the heart rate and skin temperature of the subject), which are processed and analyzed with innovative algorithms. Finally, experimental results are obtained both in simulated and real driving scenarios, demonstrating the adaptability and efficacy of the proposed system.

Turn Around, Don't Drown: A Systematic Review of Risk Factors for Motor Vehicle-Related Drowning in Floods and its Preventive Strategies.

OBJECTIVE: Exposure to flood, one of the most widespread disasters caused by natural hazards, increases the risk of drowning. Driving through flooded waterways is a cause of death due to flood-related drowning, especially in flood-prone areas. This study aimed at identifying the risk factors for motor vehicle-related drowning in floods and its prevention strategies. METHODS: International and national databases (WOS, PubMed, Scopus, Google Scholar, Magiran, and SID) were searched in the time span from 2000 to 2022. The studies investigating the risk factors relevant to land motor vehicle-related drowning in floods and its prevention strategies were included and analyzed using thematic content analysis. RESULTS: In 14 eligible studies, risk factors for land motor vehicle-related drowning in floods were identified and categorized in 3 subthemes: driver (3 categories: socio-demographic characteristics, knowledge and attitude, and beliefs); technology (1 category: land motor vehicles); and environment (2 categories: physical and socio-economic environment). Physical and structural measures (1 category: road safety improvement) and nonstructural measures (4 categories: research and education and raising awareness, risk management, promoting social-cognitive beliefs, and reconstruction and improvement of legal infrastructure) were proposed as drowning prevention strategies. CONCLUSIONS: The knowledge, attitude, and belief of the driver; the vehicle; and the environment were the most important risk factors of driving through flooded waterways. These factors should be considered when designing programs and physical and structural strategies for future interventions to curb this dangerous and potentially fatal driving behavior.

Meta-analysis of the safety effect of electronic stability control.

OBJECTIVE: Electronic Stability Control (ESC) is a standard feature on most modern cars, due to its reported efficiency to reduce the number of crashes of several types. However, empirical studies of safety effects of ESC for passenger vehicles have not considered some methodological problems that might have inflated the effects. This includes self-selection of drivers who buy/use ESC and behavioral adaptation to the system over long time periods, but also the dominant method of induced exposure. This study aimed to investigate whether such methodological problems might have influenced the results. METHOD: A meta-analysis was undertaken to investigate whether there are systematic differences between published studies. Moderators tested included when the study was undertaken, the type of vehicle studied, the percent ESC in the sample, size of sample, the length of the study, whether matched or un-matched vehicles were studied, whether induced exposure was used, and two variants of types of crashes used as controls. RESULTS: The effects found ranged from 38% to 75% reduction of crashes for the main targets of singles, running off road and rollover crashes. However, these effects were heterogeneous, and differed depending on the methods used. Most importantly, information that could have allowed more precise analyses of the moderators were missing in most publications. CONCLUSIONS: Although average effects were large and in agreement with previous meta-analyses, heterogeneity of the data was large, and lack of information about important moderators means that firm conclusions about what kind of mechanisms were influencing the effects cannot be drawn. The available data on ESC efficiency are not unanimous, and further investigations into the effects of ESC on safety using different methodologies are warranted.

Internet of things based smart framework for the safe driving experience of two wheelers.

Several parameters affect our brain's neuronal system and can be identified by analyzing electroencephalogram (EEG) signals. One of the parameters is alcoholism, which affects the pattern of our EEG signals. By analyzing these EEG signals, one can derive information regarding the alcoholic or normal stage of an individual. Many road accident cases around the world, including drinking and driving scenarios, which result in loss of life, have been reported. Another reason for such incidents is that riders avoid wearing helmets while driving two-wheelers. Many road accident cases involving two-wheelers, including drinking, driving, overspeeding, and nonwearing helmets, have been reported. Therefore, to solve such issues, the present work highlights the features of an intelligent model that can predict the alcoholism level of the subject, wearing of a helmet, vehicle speed, location, etc. The system is designed with the latest technologies and is smart enough to make decisions. The system is based on multilayer perceptron, histogram of oriented gradients (HoG) feature extraction, and random forest to make decisions in real time. The accuracy of the proposed method is approximately 95%, which will reduce the fatality rate due to road accidents. The system is tested under different working environments, i.e., indoor and outdoor, and satisfactory outcomes are observed.

Investigating the immediate and mid-term effect of a gamified e-learning platform for the enhancement of traffic knowledge and skills among Vietnamese adolescents operating powered two-wheelers.

INTRODUCTION: Traffic crashes caused by adolescents are being assessed as particularly serious and a common concern of society as a whole. Improving traffic knowledge and skills is crucial in reducing adolescent traffic crashes. OBJECTIVES: This study aimed to investigate the effects of a gamified e-learning platform on traffic knowledge and skills among adolescents (aged 15-18) in Vietnam. METHOD: Using a pretest-posttest design, this quasi-experimental study, included 350 participants within the intervention group and 350 participants within the control group. All participants were selected from three high schools in Ho Chi Minh City in Vietnam. Intervention group participants got a gamified traffic safety learning experience with a gamified e-learning platform, while control group participants received general traffic safety education through conventional methods using short videos. The effect was measured via tests focusing on traffic knowledge and skills. Data were subsequently collected from both groups before (i.e., pretest) and immediately after (i.e., posttest 1) following the education. In addition, within the intervention group also a second posttest (i.e., posttest 2) was conducted six months after following the education. RESULTS: A significant increase in scores on posttest 1 compared to the pretest was found in the intervention group but not in the control group. Also, among the intervention group, the scores in posttest 2 were significantly better than those in the pretest, however, there was no difference in scores between posttest 1 and posttest 2. CONCLUSION: The results of this study indicated that the gamified e-learning platform cannot only improve participants' knowledge of traffic safety but also help participants retain such knowledge for at least six months. PRACTICAL IMPLICATIONS: The study findings can reinforce the important role of traffic safety education in improving adolescent traffic knowledge and skills.

A qualitative study on apparent and latent contributing factors to driving errors in Iran.

Road traffic crashes (RTCs) disproportionately impact low and middle-income countries (LMICs). Current interventions in LMICs primarily target road user behavior, neglecting systemic issues and casting drivers as mere contributors. Given Iran's unique cultural, financial, and social intricacies, this study aims to explore the latent causes of RTCs, prioritizing drivers' experiences and encompassing insights from various traffic system components. Applying a qualitative approach, data were collected through 46 semi-structured interviews with 38 participants, including drivers and experts from traffic-related organizations. Content analysis identified ten themes as contributing to driving errors. Direct factors included fatigue influenced by economic and occupational conditions, distraction from internal and external sources, reckless behaviors influenced by traffic culture and environment, and Inadequate driving skills due to training. Macro-scale challenges in road infrastructure, vehicle quality, education, and accident investigations were highlighted. The lack of a centralized traffic safety authority and a predominant focus on penalizing drivers overshadowed systemic issues. The study offers valuable insights into the complex interplay of factors contributing to driving errors in LMICs, advocating for a paradigm shift towards holistic, systemic interventions beyond individual driver behavior and challenging the conventional blame-centric view associated with driving errors.

Multi-Index Driver Drowsiness Detection Method Based on Driver's Facial Recognition Using Haar Features and Histograms of Oriented Gradients.

It is estimated that 10% to 20% of road accidents are related to fatigue, with accidents caused by drowsiness up to twice as deadly as those caused by other factors. In order to reduce these numbers, strategies such as advertising campaigns, the implementation of driving recorders in vehicles used for road transport of goods and passengers, or the use of drowsiness detection systems in cars have been implemented. Within the scope of the latter area, the technologies used are diverse. They can be based on the measurement of signals such as steering wheel movement, vehicle position on the road, or driver monitoring. Driver monitoring is a technology that has been exploited little so far and can be implemented in many different approaches. This work addresses the evaluation of a multidimensional drowsiness index based on the recording of facial expressions, gaze direction, and head position and studies the feasibility of its implementation in a low-cost electronic package. Specifically, the aim is to determine the driver's state by monitoring their facial expressions, such as the frequency of blinking, yawning, eye-opening, gaze direction, and head position. For this purpose, an algorithm capable of detecting drowsiness has been developed. Two approaches are compared: Facial recognition based on Haar features and facial recognition based on Histograms of Oriented Gradients (HOG). The implementation has been carried out on a Raspberry Pi, a low-cost device that allows the creation of a prototype that can detect drowsiness and interact with peripherals such as cameras or speakers. The results show that the proposed multi-index methodology performs better in detecting drowsiness than algorithms based on one-index detection.

The tales of two cities: use of evidence for introducing 20 miles per hour speed limits in Edinburgh and Belfast (United Kingdom).

BACKGROUND: In 2016, large-scale 20 miles per hour speed limits were introduced in the United Kingdom cities of Edinburgh and Belfast. This paper investigates the role that scientific evidence played in the policy decisions to implement lower speed limits in the two cities. METHODS: Using a qualitative case study design, we undertook content analysis of a range of documents to explore and describe the evolution of the two schemes and the ways in which evidence informed decision-making. In total, we identified 16 documents for Edinburgh, published between 2006 and 2016, and 19 documents for Belfast, published between 2002 and 2016. FINDINGS: In both cities, evidence on speed, collisions and casualties was important for initiating discussions on large-scale 20 mph policies. However, the narrative shifted over time to the idea that 20 mph would contribute to a wider range of aspirations, none of which were firmly grounded in evidence, but may have helped to neutralize opposing discourses. DISCUSSION AND CONCLUSIONS: The relationship between evidence and decision-making in Edinburgh and Belfast was neither simple nor linear. Widening of the narrative appears to have helped to frame the idea in such a way that it had broad acceptability, without which there would have been no implementation, and probably a lot more push back from vested interests and communities than there was.

Auditing learner driver information about floodwaters: An environmental scan of government issued resources in Australia.

INTRODUCTION: Vehicles driving, or being swept, into floodwaters is a leading cause of flood-related death. Establishing safe behaviors among learner drivers may reduce risk throughout their driving lifetime. METHODS: An environmental scan of publicly available government issued learner and driver handbooks across the eight Australian jurisdictions was conducted to identify information provided regarding floodwaters. Search terms included 'flood,' 'rain,' 'water,' and 'wet.' A visual audit of flood-related signage was also conducted. RESULTS: Twelve documents, across eight jurisdictions, were analyzed. Four jurisdictions' documents provided no information on flooding. Of the four jurisdictions that provided information, content varied. This included highlighting risks and discouraging entering floodwaters in a vehicle, including penalties associated with travel on closed roads, to advising depth and current checks if crossing a flooded roadway, with recommendations based on vehicle size (preference given to bigger vehicles, i.e., 4wds). Information on flood-related signage was found in one jurisdiction. DISCUSSION: Learner and driver handbooks represent a missed opportunity to provide flood safety information. Currently, information is not provided in all jurisdictions, despite flood-related vehicle drowning deaths of drivers and passengers being a national issue. Where information is presented, it is limited, often lacks practical guidance on how to assess water depth, current, and road base stability, and could better use evidence regarding the psychological factors underpinning, and behavioral prompts for performing, or avoiding, risky driving behavior during floods. CONCLUSIONS: The provision and content of information in learner driver and driver handbooks must be improved, particularly within the context of increasing flooding and extreme weather associated with the effects of climate change. PRACTICAL APPLICATIONS: We encourage all jurisdictions to provide practical information that draws on evidence-based risk factors and empirically established psychological factors for behavioral change to help establish safe driver behaviors around floods in the formative years of learning to drive.

Exploring the perceived effectiveness and strictness of penalties for traffic offences: The influence of prior punishment experience and evaluation perspective (personal vs. general).

INTRODUCTION: Previous research has identified the perception of penalties as one of the most important deterrents to road traffic offenses. This study investigated whether the perceived effectiveness and the perceived strictness of penalties for different traffic offenses are associated with prior punishment experience and evaluation perspective (personal - if participants were being punished themselves, vs. general - for punishing all drivers). METHOD: A convenience sample of 1,374 Lithuanian drivers participated in the survey (56.3% males; aged 18-77 years). Among them, 801 participants had no penalties for traffic offenses, 333 reported monetary fines in the last year, and 240 reported having their drivers license suspended at least once in their driving career. A scale with 10 specific penalties for traffic offenses was developed to measure the perceived effectiveness and strictness of penalties. Half of the participants evaluated the penalties as if they were personally sanctioned for each traffic offense (personal perspective), while the other half assessed the effectiveness and strictness of the penalties for drivers in general. RESULTS: The results indicated that the perceived effectiveness of penalties was related to the evaluation perspective, being higher for changing one's own behavior than for changing behavior in general. However, the perceived effectiveness of penalties was not related to prior punishment experience. Males and drivers who had experienced their license being suspended reported the highest perceived strictness of the penalties, whereas females and drivers with no prior punishment experience perceived penalties as the least strict. PRACTICAL APPLICATIONS: These findings suggest the need for developing new ways of communicating penalties to different groups of drivers. Nevertheless, this research was exploratory in nature and further research is warranted.

Multigroup invariance of measure for angry drivers (MAD) scale using a representative sample of drivers in Australia.

INTRODUCTION: Driver anger and aggression have been linked to crash involvement and injury outcomes. Improved road safety outcomes may be achieved through understanding the causes of driver anger, and interventions designed to reduce this anger or prevent it from becoming aggression. Scales to measure anger propensities will be an important tool in this work. The measure for angry drivers (MAD; Stephens et al., 2019) is a contemporary scale designed to measure tendencies for anger across three types of driving scenarios: perceived danger from others, travel delays, and hostility or aggression from other drivers. METHOD: This study aimed to validate MAD using a representative sample of Australian drivers, stratified across age, gender, and location. Participants completed a 10-minute online survey that included MAD, sought demographic information (age, gender, driving purpose, crash history), as well as the frequency of aggressive driving. Multigroup confirmatory factor analyses (MGCFA) assessed how stable the structure of the MAD was across drivers of different ages, gender, purposes for driving and those who do or do not display anger aggressively. MAD was invariant across all groups, showing that all drivers interpreted and responded to MAD in the same way. RESULTS: A comparison of latent means showed anger tendencies were higher for men compared to women, for younger drivers compared to older drivers, and for those who drive mainly for work compared to those who mainly drive for other reasons. When controlling for driver factors, driving anger was associated with increased odds of being aggressive while driving. PRACTICAL APPLICATIONS: Overall, this study demonstrated that MAD is an appropriate scale to measure anger tendencies and can be used to support interventions, and evaluation of interventions, to reduce anger and aggressive driving.

Understanding aberrant driving intentions based on the Theory of Planned Behavior: Literature review and Meta-Analysis.

INTRODUCTION: Despite deployed efforts to establish strict road safety standards, human factors is still the leading cause of road crashes. To identify determinants of driver's behavior, TPB (Theory of Planned Behavior) is widely used as a prominent theory of behavior change. However, the existence of different aberrant driving behaviors (decision errors, recognition errors, violations, and physical condition related errors) and several studies using TPB to understand driving behavior, makes it important to conduct a literature review and a meta-analysis of existing studies to use their results in effective driving behavior change interventions. METHOD: The selection process provided 125 relevant studies that were published between 1991 and 2022, and that used TPB for the understanding of aberrant driving behavior. Five fundamental research questions were defined to identify information to be discovered from the literature review and from the meta-analysis. RESULTS: In addition to the standard TPB constructs (attitudes, subjective norms, and perceived behavioral control), past behavior, moral norms, and descriptive norms were used in studies for a more comprehensive understanding of aberrant driving intention. This analysis demonstrated a significant correlation between aberrant driving intentions and past behavior. Also, moral norms construct was correlated with violations and recognition errors, whereas descriptive norms construct was correlated just with recognition errors. CONCLUSIONS: The results of this study highlight the strength of TPB in the prediction of aberrant driving intention and its potential effectiveness to guide interventions aimed at changing aberrant driving behaviors. The study contributes to the comprehension of the relevant psychological factors influencing the engagement of drivers in each category of aberrant driving behaviors. PRACTICAL APPLICATIONS: Researchers can use the results of this study to select the relevant psychological factors adapted to their interventions of driving behavior change. The results of the meta-analysis can also be used in the prediction of driver's intentions.

Development of an algorithm for analysis of routes: Case studies using novice and older drivers.

INTRODUCTION: This study addresses the lack of methods to quantify driver familiarity with roadways, which poses a higher risk of crashes. METHOD: We present a new approach to assessing driving route diversity and familiarity using data from the DrivingApp, a smartphone-based research tool that collects trip-level information, including driving exposure and global positioning system (GPS) data, from young novice drivers (15-19 years old) to older drivers (67-78 years old). Using these data, we developed a GPS data-based algorithm to analyze the uniqueness of driving routes. The algorithm creates same route trip (SRT) arrays by comparing each trip of an identified user, employing statistically determined thresholds for GPS coordinate proximity and trip overlap. The optimal thresholds were established using a General Linear Model (GLM) to examine distance, and repeated observations. The Adjusted Breadth-First Search method is applied to the SRT arrays to prevent double counting or trip omission. The resulting list is classified as geographically distinct routes, or unique routes (URs). RESULTS: Manual comparison of algorithm output with geographical maps yielded an overall precision of 0.93 and accuracy of 0.91. The algorithm produces two main outputs: a measure of driving diversity (number of URs) and a measure of route-based familiarity derived from the Rescorla-Wagner model. To evaluate the utility of these measures, a Gaussian mixture model clustering algorithm was used on the young novice driver dataset, revealing two distinct groups: the low-frequency driving group with lower route familiarity when having higher route diversity, whereas the high-frequency driving group with the opposite pattern. In the older driver group, there was a significant correlation found between the number of URs and Geriatric Depression Score, or walking gait speed. PRACTICAL APPLICATIONS: These findings suggest that route diversity and familiarity could complement existing measures to understand driving safety and how driving behavior is related to physical and psychological outcomes.

Examining strategies, policies, and guidance for addressing wrong-way driving in the United States: A comprehensive national survey.

INTRODUCTION: This paper presents a comprehensive investigation into the current and emerging solutions, policies, and guidance employed by various agencies to mitigate wrong-way driving (WWD) activities in the United States. The study utilized a two-pronged approach, involving an online survey and follow-up phone interviews with respondents from state transportation agencies, tollway authorities, and law enforcement. METHODS: The initial step involved conducting an online survey to gather general insights about the existing strategies and practices used to combat WWD. The survey questionnaire, consisting of 12 questions, covered topics such as mitigation strategies/policies, guidance for selecting countermeasures, and topics/needs for national handbook. The survey was emailed to traffic and safety engineers from all 50 state transportation agencies and 59 tollway authorities across the nation. As the second step, follow-up phone interviews were conducted with respondents identified from the online survey. The interviews delved deeper into specific aspects such as crash/incident data collection methods, identification of crash-prone locations, countermeasure selection and implementation, experience with Intelligent Transportation Systems (ITS) applications, and future initiatives. RESULTS: The findings from the survey and interviews indicated an increasing awareness and adoption of best practices to combat WWD. Various states have implemented new policies and advanced technologies to deter WWD incidents. The insights gathered from the survey and interviews with different agencies are invaluable in shaping safe system approaches and guidelines for the national handbook on WWD solutions. PRACTICAL APPLICATIONS: Overall, this study sheds light on the efforts and progress made by state transportation agencies, tollway authorities, and law enforcement in addressing the critical issue of WWD. By gathering valuable lessons and practices from the various agencies, this research lays the groundwork for developing national guidelines to reduce WWD crashes and incidents on divided highways.