Journal of Safety Research最新文献

{"title":"A vehicle occupant injury prediction algorithm based on road crash and emergency medical data","authors":"Tetsuya Nishimoto ,&nbsp;Kazuhiro Kubota ,&nbsp;Giulio Ponte","doi":"10.1016/j.jsr.2024.09.015","DOIUrl":"10.1016/j.jsr.2024.09.015","url":null,"abstract":"<div><div><em>Introduction</em>: Advanced Automatic Collision Notification (AACN) systems are an automobile safety technology designed to reduce the number of fatalities in traffic accidents by optimizing early treatment methods. AACN systems rely on robust injury prediction algorithms, however, despite the importance of time to treatment, current injury prediction algorithms used in AACN systems do not take this critical time period time into consideration. <em>Method</em>: This study developed a vehicle occupant injury prediction algorithm by using emergency transport time in addition to mass crash data, to determine the risk of serious injury for vehicle occupants in a road crash. Two sources of de-identified data were used: The South Australian Traffic Accident Reporting System (TARS) database and the highly detailed South Australian Serious Injury Database (SID). Firstly, the TARS data, a large statistical crash dataset, was imputed into a logistic regression analysis to produce a base injury prediction algorithm. The important effect of emergency transport time on the risk of death and serious injury was then independently quantified as an odds ratio (OR) from the SID. The ORs were converted into regression coefficients and subsequently introduced into the base injury prediction algorithm to produce an enhanced injury prediction algorithm. <em>Results</em>: The ORs calculated from the SID showed that the risk of death and serious injury increased with increasing transport time: 61–90 min (OR = 1.6), 91–120 min (OR = 3.3), and &gt; 120 min (OR = 4.9), compared to a transport time of 60 min or less. An assessment of the base algorithm compared to the enhanced injury prediction algorithm through Receiver Operating Characteristic (ROC) analysis, demonstrated a prediction accuracy improvement from AUC 0.70 to AUC 0.73 when evaluating the respective algorithms. The injury prediction calculations indicate that the impact of two risk factors, transport time and age-related decline in human injury tolerance, are significant, and both have a strong influence on the increased risk of serious injury. <em>Conclusions</em>: The impact of emergency transport time on the risk of fatal and serious injuries was determined from a relatively small, but data rich SID. Subsequently this was incorporated into an injury prediction algorithm constructed from the large (TARS) statistical crash data set to produce an enhanced injury prediction algorithm. <em>Practical Application</em>: By adding the effect of transport time to enhance the basic injury prediction algorithm, an AACN that incorporates such an algorithm can be used to determine the probability of death or serious injury due to delayed treatment. Further, such a system can be used to improve policies and procedures to optimize emergency transport time.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"91 ","pages":"Pages 410-422"},"PeriodicalIF":3.9,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fatal injuries among landscaping and tree care workers: Insights from NIOSH and state-based FACE reports","authors":"Gregory D. Kearney ,&nbsp;Nancy Romano ,&nbsp;Anna Doub","doi":"10.1016/j.jsr.2024.10.005","DOIUrl":"10.1016/j.jsr.2024.10.005","url":null,"abstract":"<div><div><em>Context:</em> A comprehensive assessment of the National Institute for Occupational Safety and Health (NIOSH) and State-based Fatal Assessment and Control Evaluation (FACE) investigative reports involving landscaping and tree worker fatalities have not been fully examined. <em>Methods:</em> Narrative text from 93 FACE reports from 1987 to 2023 involving landscaping and tree care workers were reviewed, manually coded and analyzed on major variables. Univariate analyses was conducted to summarize results of decedent workers and workplace characteristics. <em>Results:</em> Among the total number of worker fatalities (n = 95), the most commonly reported incidents were, electrocutions from power lines (18.3%), falls from trees (16.1%), and incidents involving a worker being either caught, pulled, or dragged into wood-chipping machine (12.9%). More than 66.0% of fatal incidents occurred among tree care workers that had been on the job for one year or less. Among reports, 60.2% of employers lacked a written safety plan, and 34.4% did not provide job training to their workers. <em>Conclusions:</em> FACE case reports alone are not a valid measure of workplace fatalities. Nevertheless, the codification and descriptive summary of more than three decades of case reports increases understanding of circumstances and contributing risk factors associated with these tragic, and yet largely preventable incidents. A comprehensive approach is urgently needed that includes: (a) taking immediate action to reduce occupational risks while cultivating a robust safety culture across the industry, and (b) increasing research to evaluate the effectiveness of interventions and prevention measures. <em>Practical Application:</em> The interconnectedness of safety challenges requires a multi-faceted approach that includes addressing issues related to new and diverse workers, employer commitments to the implementation of safety plans, and comprehensive training and mentorship programs. Intervention strategies and implementation measures are essential to diminishing fatalities in these high-risk jobs.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"91 ","pages":"Pages 393-400"},"PeriodicalIF":3.9,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The aggressive driving performance caused by congestion based on behavior and EEG analysis","authors":"Shuo Zhao,&nbsp;Geqi Qi,&nbsp;Peihao Li,&nbsp;Wei Guan","doi":"10.1016/j.jsr.2024.10.004","DOIUrl":"10.1016/j.jsr.2024.10.004","url":null,"abstract":"<div><div><em>Introduction:</em> Traffic congestion is closely related to traffic accidents, as prolonged traffic congestion often results in frustration and aggressive behavior. Moreover, in daily commuting, drivers often have to pass through multiple congested road sections, and aggressive driving performance due to exiting or re-entering traffic jams has rarely been analyzed. <em>Method:</em> To fill this research gap, we designed an intermittent traffic congestion scenario using a driving simulator and employed unsupervised learning algorithms to extract high-level driving patterns gathered with EEG data to investigate the continuous effects of traffic jams, particularly when drivers exit and re-enter traffic jam conditions. <em>Results:</em> We discovered that drivers, upon exiting congested areas, engage in abrupt braking with a decrease in braking time of approximately 0.47 s and smooth lane changes with an increase in lane change time of approximately 0.5 s to maintain high-speed driving conditions. When drivers re-enter a traffic jam, they exhibit more abrupt stop-and-go behaviors to escape the traffic jam. The results of the risk assessment of driving behavior indicated that after leaving congested areas, free-flow segments have greater risk factors than other segments. Electroencephalogram (EEG) data were analyzed to identify instances of mind-wandering when a driver transitions into free-flowing segments, followed by a substantial increase in brain activity upon re-entry into congested traffic conditions. <em>Practical Applications:</em> The research outcomes suggest that optimizing the road segments after congestion, using appropriate entertainment systems to reduce driver stress, and implementing adaptive traffic signals to achieve smooth transitions during intermittent congestion can reduce aggressive driving behavior and enhance traffic safety.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"91 ","pages":"Pages 381-392"},"PeriodicalIF":3.9,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rates and ratios of fatal and nonfatal drowning attended by ambulance in New South Wales, Australia between 2010 and 2021","authors":"Edwina Mead ,&nbsp;Chen-Chun Shu ,&nbsp;Pooria Sarrami ,&nbsp;Rona Macniven ,&nbsp;Michael Dinh ,&nbsp;Hatem Alkhouri ,&nbsp;Lovana Daniel ,&nbsp;Amy E. Peden","doi":"10.1016/j.jsr.2024.09.019","DOIUrl":"10.1016/j.jsr.2024.09.019","url":null,"abstract":"<div><div><em>Introduction</em>: Drowning is a preventable cause of mortality, with 279 unintentional drowning deaths per year in Australia. Despite larger estimated numbers, less is known about nonfatal drowning compared to fatalities. This study aimed to examine the burden of fatal and nonfatal drowning in the Australian state of New South Wales using pre-hospital case capture. <em>Methods:</em> A cross-sectional analysis of individuals attended by an ambulance in NSW for drowning between 2010 and 2021 was conducted. Ambulance data (paper-based and electronic medical records) were linked to emergency department and death registry. Ratios of fatal to nonfatal drowning were constructed overall, by sex, age, and remoteness of incident and residential locations. <em>Results:</em> 3,973 ambulance-attended drowning patients were identified (an annual rate of 4.16/100,000 persons). Six percent (6.1%; n = 243) died within 30 days, 82.7% (n = 201) of which died on the day of incident, including at the scene. Mean survival time for those who died between 2 and 30 days was 4.6 days. The overall ratio of fatal to nonfatal incidents was 1:15. Ratios were highest for 10–19 year-olds (1:77), females (1:22), and in metropolitan incident (1:20) and residential (1:23) locations. Across the study drowning declined by 14 incidents and 0.18 fatalities per year. <em>Discussion:</em> Temporal trends indicate declining drowning incidents and fatalities. However, this study highlights significant numbers of nonfatal incidents among those traditionally seen as lower risk, such as adolescents and females, necessitating a widened focus on improving water safety among these groups. <em>Conclusions:</em> Nonfatal drowning results in significant, yet preventable health system burden in New South Wales. <em>Practical Applications:</em> This study highlights the importance of documenting the full burden of drowning, including health system impacts of a preventable cause of injury and death. Such data may be used to encourage further investment in primary prevention efforts.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"91 ","pages":"Pages 373-380"},"PeriodicalIF":3.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The maximum potential benefits of safety systems on light van crashes in the United States","authors":"Aimee E. Cox,&nbsp;Jessica B. Cicchino","doi":"10.1016/j.jsr.2024.09.021","DOIUrl":"10.1016/j.jsr.2024.09.021","url":null,"abstract":"<div><div><em>Introduction:</em> The retail landscape has shifted from brick-and-mortar sales to e-commerce, which surged during the COVID-19 pandemic. Light vans are popular vehicles to meet the rising home delivery demands. Two New Car Assessment Programs developed van ratings programs based on their equipment of safety features. This study was designed to estimate the maximum potential benefits that safety technologies could provide light vans based on their historical involvement in relevant crash scenarios. <em>Methods:</em> We used U.S. crash data from 2016—2021 to estimate the average annual total (police-reported), injury, and fatal crashes involving light vans. We determined the proportion of total crashes where front crash prevention, lane departure prevention, blind spot detection, and intelligent speed assistance systems might help the driver prevent crashes or mitigate their severity. We determined the proportions of injury and fatal crashes that resulted in an injury to someone not traveling in the light van. <em>Results:</em> Of the systems studied, front crash prevention that detects vehicles, pedestrians, and cyclists was relevant to largest percentage of light van crashes and could prevent as many as 17% of their involvements, 14% of their injury crashes, and 19% of their fatal crashes. Combined, the four systems have the potential to reduce up to 26% of light van crashes, 22% of their injury crashes, and 36% of their fatal crashes. Sixty-two percent of injury crashes and 56% of fatal crashes relevant to these technologies resulted in injuries or fatalities to occupants of other vehicles or other road users. <em>Conclusions:</em> Light vans are a growing market that can benefit from safety technology, especially when considering their impact on others with whom they share the road. <em>Practical Applications:</em> People and businesses in the market for a light van should seek these systems. Aftermarket products can be installed on light vans not equipped with them.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"91 ","pages":"Pages 366-372"},"PeriodicalIF":3.9,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-label material and human risk factors recognition model for construction site safety management","authors":"Jeongeun Park ,&nbsp;Sojeong Seong ,&nbsp;Soyeon Park ,&nbsp;Minchae Kim ,&nbsp;Ha Young Kim","doi":"10.1016/j.jsr.2024.10.002","DOIUrl":"10.1016/j.jsr.2024.10.002","url":null,"abstract":"<div><div><em>Introduction:</em> Construction sites are prone to numerous safety risk factors, but safety managers have difficulty managing these risk factors for practical reasons. Moreover, manually identifying multiple risk factors visually is challenging. Therefore, this study aims to propose a deep learning model–based multi-label risk factor recognition (MRFR) framework that automatically recognizes multiple potential material and human risk factors at construction sites. The research answers the following questions: How can a deep learning model be developed and optimized to recognize and classify multiple material and human risk factors automatically and concurrently at construction sites, and how can the decision-making process of the model be understood and improved for practical application in preemptive safety management? <em>Methods:</em> Data comprising 14,605 instances of eight types of material and human risk factors were collected from construction sites. Multiple risk factors can occur concurrently; thus, an optimal model for multi-label recognition of possible risk factors was developed. <em>Results:</em> The MRFR framework combines material and human risk factors into a single label while achieving satisfactory performance with an F1 score of 0.9981 and a Hamming loss of 0.0008. The causes of mispredictions by MRFR were analyzed by interpreting the decision basis of the model using visualization. Conclusion: This study found that the model must have sufficient capacity to detect multiple risk factors. Performance degradation in MRFR is primarily due to difficulties recognizing visual ambiguities and a tendency to focus on nearby objects when perspective is involved. <em>Practical applications:</em> This study contributes to safety management knowledge by developing a model to recognize multi-label material and human risk factors. Furthermore, the results can be used as guidelines for data collection methods and model improvement in the future. The MRFR framework can be used as an algorithm to recognize risk factors preemptively and automatically at real-world construction sites.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"91 ","pages":"Pages 354-365"},"PeriodicalIF":3.9,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Kinematic characterization of micro-mobility vehicles during evasive maneuvers","authors":"Paolo Terranova ,&nbsp;Shu-Yuan Liu ,&nbsp;Sparsh Jain ,&nbsp;Johan Engström ,&nbsp;Miguel A. Perez","doi":"10.1016/j.jsr.2024.09.020","DOIUrl":"10.1016/j.jsr.2024.09.020","url":null,"abstract":"<div><div><em>Introduction</em>: Over the last decade, the increasing popularity of Micromobility Vehicles (MMVs) has led to profound changes in personal mobility, raising concerns about road safety and public health. Therefore, the effective characterization of their kinematic performances and safety boundaries is becoming crucial. Hence, this study aims to: (1) characterize the MMVs kinematic behaviors during emergency maneuvers; (2) examine how various power sources affect their performances; and (3) assess the suitability of a piecewise linear model for modeling their trajectories. <em>Method</em>: We conducted a test track experiment involving 40 frequent riders performing emergency braking and swerving maneuvers on different electric MMVs, their traditional counterparts, and behaving as running pedestrians. A second experiment determined the swerving boundaries of different devices estimating their minimum radius of curvature. <em>Results</em>: Electric MMVs displayed superior braking capabilities compared to their traditional counterparts, while the opposite was observed in terms of swerving performances. Performances significantly varied across MMV-types, with handlebar-based devices (bicycles and scooters) consistently outperforming the handlebar-less MMVs (skateboards and onewheel). The piecewise linear models used for braking profiles well fitted most MMV trajectories, except for skateboards and pedestrians due their foot-ground interaction. <em>Conclusions</em>: This research highlights the influence of MMVs-specific characteristics on their maneuverability, underscoring that steering or braking effectiveness in collisions may vary depending on device type and power source. Piecewise linear models effectively generated parameterized functions for modeling braking trajectories, despite further improvements are suggested given the inapplicability of the single brake-ramp assumption to all the MMVs. <em>Practical applications</em>: The identified similarities and distinctions between MMVs could offer insights to traffic regulators and may assist MMV designers and manufacturers in enhancing the devices users’ safety. The piecewise model results allow traffic events reconstructions and simulations, enabling intelligent driving system to predict MMV riders' evasive actions in critical situations.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"91 ","pages":"Pages 342-353"},"PeriodicalIF":3.9,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vehicle-pedestrian interaction analysis for evaluating pedestrian crossing safety at uncontrolled crosswalks − a geospatial approach using multimodal all-traffic trajectories","authors":"Fei Guan ,&nbsp;Trevor Whitley ,&nbsp;Hao Xu ,&nbsp;Ziru Wang ,&nbsp;Zhihui Chen ,&nbsp;Tianwen Hui ,&nbsp;Yuan Tian","doi":"10.1016/j.jsr.2024.09.005","DOIUrl":"10.1016/j.jsr.2024.09.005","url":null,"abstract":"<div><div><em>Introduction</em>: Pedestrian crossing safety has gained increased attention due to the high rate of pedestrian fatalities and injuries, especially at uncontrolled crosswalks. <em>Method</em>: In this study, we proposed a novel GIS-based method for detecting motorist yield behaviors using multi-modal trajectory data collected from LiDAR (Light Detection and Ranging) sensors at uncontrolled crosswalks. The approach classifies diverse types of motorist-pedestrian interactions and calculates motorist compliance rates, enabling us to assess the safety performance of different geometric crossing treatments. The method was applied to four uncontrolled crosswalks in midtown Reno, NV to analyze the impact of different crossing treatments, including curb extensions, pedestrian refuge islands, and Danish Offset, on motorist yield rates. <em>Results</em>: The findings indicated that refuge islands significantly improve driver yield rates, with further improvement observed when implementing Danish Offset designs. Among the four sites, the highest motorist yield rate (78.0%) was observed at Taylor (Danish Offset), followed by St. Lawrence (refuge island) with 71.9%. Martin and LaRue (curb extension only) exhibited lower yield rates of 57.9% and 61.3%, respectively. <em>Practical applications</em>: This study emphasized the importance of considering different directions when evaluating pedestrian safety at crosswalks, an aspect currently not considered in the latest Highway Capacity Manual (HCM). This research also provides valuable insights into applying multimodal all-road-user geospatial trajectory data for initiative-taking traffic safety performance evaluation of pedestrian crossing facilities at uncontrolled crosswalks and can guide future efforts in improving pedestrian safety.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"91 ","pages":"Pages 326-341"},"PeriodicalIF":3.9,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of integrated takeover request warning with personal portable device on takeover time and post-takeover performance in level 3 driving automation","authors":"Niloy Talukder ,&nbsp;Chris Lee ,&nbsp;Yong Hoon Kim ,&nbsp;Balakumar Balasingam ,&nbsp;Francesco Biondi ,&nbsp;Aditya Subramani Murugan ,&nbsp;Eunsik Kim","doi":"10.1016/j.jsr.2024.09.016","DOIUrl":"10.1016/j.jsr.2024.09.016","url":null,"abstract":"<div><div><em>Introduction</em>: Level 3 driving automation defined by the Society of Automotive Engineers (SAE) requests human drivers to drive manually when the vehicle cannot perform the driving task. In this regard, researchers have studied the integrated takeover request (TOR) which provides visual and auditory TOR warning in both vehicle interface (e.g., dashboard, windshield (head-up display) and personal portable device (PPD) (e.g., cell phone, tablet). However, these studies neither used auditory TOR warning in PPD nor examined the effect of use of headphone on takeover. Thus, this study evaluates the effects of the integrated TOR with the use of headphones on the takeover time and the post-takeover performance. <em>Method</em>: The behavior of 60 drivers was observed in the driving simulator experiment. During the experiment, the drivers watched a video on a tablet in automated driving, received the TOR warning, and manually drove in the lane change and pullover scenarios. The survey was also conducted to ask drivers’ experience and preference for TOR warning. <em>Results</em>: The integrated TOR significantly reduced the takeover time compared to the conventional TOR which provides the TOR warning in vehicle interface only. The integrated TOR also improved the post-takeover performance as indicated by more stable steering operation and safer driving behavior after TOR warning. However, the use of headphones did not significantly reduce the takeover time or improve the post-takeover performance for the integrated TOR. The participants generally perceived higher subjective comfort and safety level with the integrated TOR than the conventional TOR. <em>Conclusions</em>: The integrated TOR with auditory warning in PPD can significantly reduce the takeover time and improve the post-takeover performance in both urgent and less urgent conditions. <em>Practical applications</em>: The integrated TOR with auditory warning in PPD can be applied to SAE Level 3 driving automation for safe transition from automated to manual driving.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"91 ","pages":"Pages 314-325"},"PeriodicalIF":3.9,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determination of rates of occurrence for hydroplaning events with naturalistic driving data","authors":"Christina Witcher ,&nbsp;Daniel Christ ,&nbsp;Jeremy Sudweeks ,&nbsp;Charles Layman ,&nbsp;Miguel Perez","doi":"10.1016/j.jsr.2024.09.018","DOIUrl":"10.1016/j.jsr.2024.09.018","url":null,"abstract":"<div><div><em>Introduction:</em> The degree to which hydroplaning occurs in real-world conditions is not entirely known. Naturalistic driving data can be helpful in addressing some of the limitations of existing data sources related to the incidence of hydroplaning. <em>Method:</em> Data from the Second Strategic Highway Research Program Naturalistic Driving Study were leveraged to estimate the incidence of hydroplaning. Two hydroplaning detection algorithms were used for candidate hydroplaning event generation. Hard braking events were also identified and analyzed as normative comparisons to the hydroplaning events. <em>Results:</em> A total of 1,141 hydroplaning events were found in the naturalistic driving data and utilized for analysis, including 650 hydroplaning events that were unnoticeable by the driver based on lack of observable reactions, 13 events that were deemed to be of “critical” severity, and only 3 events that resulted in crash events during more than 30 million miles of driving. Hard braking events occurred nearly four times more often, and at comparatively lower speeds, than hydroplaning events. Observable driver reactions also differed between event types. For example, more drivers changed their posture after a hydroplaning event than after a hard braking event and drivers maintained both hands on the wheel at higher rates after experiencing a hydroplaning event than after a hard braking event. Suspension of secondary tasks during hard braking and hydroplaning events was also observed. <em>Conclusions:</em> Overall, these findings suggest that drivers perceive hydroplaning events as more harmful than hard braking events, despite the large discrepancy and incompatibility in how often these driving situations lead to vehicular crashes. <em>Practical application:</em> The findings of this research will provide vehicle and tire designers with empirical data that quantifies the important tradeoffs they must make in balancing vehicle and tire performance in wet and dry environments, and in tradeoffs related to tire wear performance and grip.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"91 ","pages":"Pages 303-313"},"PeriodicalIF":3.9,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}