Journal of Safety Research最新文献

{"title":"Analyzing the time to death of pedestrian fatalities: A copula approach","authors":"Nafis Anwari ,&nbsp;Tanmoy Bhowmik ,&nbsp;Mohamed Abdel-Aty ,&nbsp;Naveen Eluru ,&nbsp;Juneyoung Park","doi":"10.1016/j.jsr.2024.11.007","DOIUrl":"10.1016/j.jsr.2024.11.007","url":null,"abstract":"<div><div><em>Introduction:</em> The study aims to investigate the instant fatality likelihood and time to death (lag time) of pedestrian fatalities using a copula-based joint modeling framework. The upper level model investigates whether or not the pedestrian died instantly, while the lower level model investigates time to death for pedestrians who did not die instantly. <em>Method:</em> The joint model was run on a dataset of 33,615 observations obtained from the Fatality Accident Reporting System for the 2015–2019 period. The effect of roadway and traffic characteristics were investigated on time to death using six copula structures along with their parameterized versions. <em>Results:</em> Gaussian parameterized copula was found to have the best fit. Weather, Driver age groups, Drunk/ distracted/ drowsy drivers, Hit and Run, Involvement of Large Truck, VRU age group, VRU Gender, Presence of Sidewalk, Presence of Intersection, Light Condition, and Speeding were significant common factors for both sub-models. The factors found to be significant exclusively to one of the sub-models include: Area type for the Binary Logit model, and Presence of Crosswalk and Fire station nearby for the Ordered Logit model. <em>Conclusions:</em> Instant fatality likelihood increased and lag time for non-instant fatalities decreased for 16–24 year old drivers, drunk drivers, during hit and run situations, when large trucks were involved, for the elderly pedestrians, for female pedestrians, during dark conditions, and when vehicles were speeding. On the other hand, instant fatality likelihood decreased and lag time for non-instant fatalities increased in adverse weather conditions, for elderly drivers, on sidewalks, at intersections, and during daylight hours. <em>Practical applications:</em> Results can be useful to transportation policymakers and practitioners in implementing countermeasures to improve road safety. These include placing sidewalks, various types of crosswalks, traffic calming measures, and adequate artificial lighting in areas frequented by pedestrians. Alcohol and drug testing need to be enforced.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 55-67"},"PeriodicalIF":3.9,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662463","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":"Latent class analysis of autonomous vehicle crashes","authors":"Jianfeng Qiao,&nbsp;Yanan Wang,&nbsp;Zixiu Zhao,&nbsp;Dawei Chen,&nbsp;Yanping Fu,&nbsp;Jie Hou","doi":"10.1016/j.jsr.2024.11.014","DOIUrl":"10.1016/j.jsr.2024.11.014","url":null,"abstract":"<div><div><em>Introduction:</em> Since September 2014, the California Department of Motor Vehicles has requested autonomous vehicle (AV) manufacturers to report their accidents if they take field tests on public roadways in California. These collision reports are heterogeneous containing a variety of accident factors. <em>Method:</em> To describe the accident more elaborately, we add three new category variables: ‘traffic control and status,’ ‘speed/speed change,’ and ‘type of accident location,’ extracted from crash narratives. Combining with the existing variables as model inputs, we use Latent Class Analysis (LCA) to investigate the mixture types of traffic accidents. After using ‘Mplus’ (LCA tool), the data set with 308 cases has been segmented into three clusters, including ‘rear-end collisions after the speed change of AV,’ ‘sideswipe collisions at parking places,’ and ‘hit-object collisions in normal traffic road.’ <em>Results:</em> These three clusters are not highlighted in previous literature and Cluster 1 shows AV should not be designed too ethically. To follow the driving habits of traditional drivers, AVs should accelerate vehicles quickly when they start to move and delay stopping in front of stop lines, traffic lights, and yielding. The cluster-based analyses show that applying LCA as a preliminary analysis can reveal the interesting hierarchical patterns hidden in the dataset and help traffic safety researchers improve AV safety performances.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 81-90"},"PeriodicalIF":3.9,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662501","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":"Predictors of driving errors contributing to crashes in older adults across age groups, 2010 to 2020","authors":"Gilsu Pae ,&nbsp;Jonathan Davis ,&nbsp;Joseph Cavanaugh ,&nbsp;Motao Zhu ,&nbsp;Cara Hamann","doi":"10.1016/j.jsr.2024.11.010","DOIUrl":"10.1016/j.jsr.2024.11.010","url":null,"abstract":"<div><div><em>Introduction:</em> Given the largely autocentric nature of the United States, drivers continue to operate vehicles with varying levels of driving ability and self-restriction as they advance into older age. This study explores the associations of vehicle actions and traffic control devices with older drivers’ driving errors contributing to crashes, incorporating age group as effect modifiers of these relationships. <em>Method:</em> This study includes crashes reported to the Iowa Department of Transportation from 2010 to 2020. Analysis was completed for drivers involved in a crash who were aged 45 years and older (n = 254,912). Driving errors were identified based on driver contributing factors reported in the Iowa crash data. A multivariable logistic regression model was built to model predictors of driving errors, focusing on crash-related vehicle actions and traffic control devices. Additionally, interaction terms were incorporated to examine the moderating effect of age groups (45–64; 65–74; 75–84; 85+). <em>Results:</em> Driving errors increased with age, especially in the middle-old age group (75–84). A higher probability of driving errors was observed in changing lanes, merging, and turning, with right turns showing the most substantive increase in the middle-old age group compared to the other age groups. Stop and yield signs were associated with a higher probability of driving errors, increasing monotonically with age. The middle-old age group exhibited a notable increase in driving errors at uncontrolled or traffic signaled locations compared to the other age groups. <em>Conclusions:</em> The significant increase in driving errors at and beyond the middle-old age group may demonstrate higher age-related declines in safe driving compared to younger age groups. <em>Practical Applications:</em> Careful evaluations for older drivers’ fitness to drive during license renewal periods are needed once drivers reach the middle-old age. Additionally, effective combinations of advanced technologies, traffic systems, and policies are necessary to reduce the burdens associated with aging.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 40-47"},"PeriodicalIF":3.9,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662461","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":"A machine learning approach to understanding the road and traffic environments of crashes involving driver distraction and inattention (DDI) on rural multilane highways","authors":"Chenxuan Yang ,&nbsp;Jun Liu ,&nbsp;Zihe Zhang ,&nbsp;Emmanuel Kofi Adanu ,&nbsp;Praveena Penmetsa ,&nbsp;Steven Jones","doi":"10.1016/j.jsr.2024.11.011","DOIUrl":"10.1016/j.jsr.2024.11.011","url":null,"abstract":"<div><div><em>Introduction</em>: Driver distraction and inattention (DDI) are major causes of road crashes, especially on rural highways. However, not all instances of distracted or inattentive driving lead to crashes. Previous studies indicate that DDI-related driving behavior is closely associated with low-traffic and less complex driving environments. Nevertheless, it is unclear if these traffic or road environments also increase the likelihood of crashes involving DDI. <em>Method</em>: This study employed machine learning algorithms to identify the factors contributing to DDI-involved crashes on rural highways. This study applied multiple machine learning models including the Light Gradient Boosting Model (LGBM), Random Forest (RF), and Neural Network (NN) to quantify the correlations of DDI-involved crashes related to road and traffic environments. The study leveraged a statewide crash database with unique roadway data that contains variables for median type (e.g., 4-ft flush medians) and roadside access point density. To deal with the extreme imbalance of data, two sampling methods (over and under-sampling) were used to balance the data for machine learning<em>. Results</em>: Modeling results indicated that the road and traffic environments that are strongly linked to DDI-involved crashes in general overlap with the environments that lead to DDI-related driving behavior, except for the truck volumes in traffic. Crashes that involved DDI were more likely to occur in environments with non-traversable medians (compared to 4-ft flush medians), lower-volume traffic, and greater access spacing on roadsides. With regard to truck volumes, a non-linear relationship with the occurrence of DDI-involved crashes was uncovered. Traffic with about 8 to 10% of trucks is associated with the highest likelihood of DDI-involved crashes. <em>Practical Applications:</em> This study provides valuable information for drivers who need to be careful while driving in certain environments with a risk of DDI-involved crashes and for agencies who need to take actions to address the issue of DDI under such environments.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 14-26"},"PeriodicalIF":3.9,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662459","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":"Safety assurance for automated systems in transport: A collective case study of real-world fatal crashes","authors":"Stuart Ballingall,&nbsp;Majid Sarvi,&nbsp;Peter Sweatman","doi":"10.1016/j.jsr.2024.11.008","DOIUrl":"10.1016/j.jsr.2024.11.008","url":null,"abstract":"<div><div><em>Introduction</em>: Traditional vehicle safety assurance frameworks are challenged by Automated Driving Systems (ADSs) that enable dynamic driving tasks to be performed without active involvement of a human driver. Further, an ADS’s driving functionality can be changed during in-service operation, using software updates developed using Machine Learning (ML). Learnings from real-world cases will be a key input to reforming current regulatory frameworks to assure ADS safety. However, ADSs are yet to be deployed in mass volumes, and limited data are available regarding their in-service safety performance. <em>Method:</em> To overcome these limitations, a collective case study was undertaken, drawing upon three relevant real-world cases involving automated control systems that were a causative factor in major transport safety incidents. <em>Results:</em> A range of findings were identified, which informed recommendations for reform. The study found some assurance processes, decisions and oversight were not commensurate with risk or safety integrity levels, including a lack of independence with reviews and approvals for safety–critical system components. Two cases were also impacted by conflict or bias with regulatory approvals. Other commonalities included a lack of safeguards to ensure systems were not operated outside their design domain, and a lack of system redundancy to ensure safe operation if a system component fails. Further, the identification and validation of system responses to scenarios that could be encountered within design domain boundaries was lacking. For the two cases in which safety–critical functionality was developed using ML, it’s concerning no regulator reports provided detailed findings regarding the role of ML models, algorithms, or training data.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 27-39"},"PeriodicalIF":3.9,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662460","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":"Experimental and finite element analysis of rear impacts on bicycles with child seats","authors":"Takaaki Terashima ,&nbsp;Ryuga Miyata ,&nbsp;Koji Mizuno","doi":"10.1016/j.jsr.2024.10.008","DOIUrl":"10.1016/j.jsr.2024.10.008","url":null,"abstract":"<div><div><em>Introduction:</em> In Japan, bicycles equipped with child seats have become popular in urban areas as a convenient means of transportation for preschool children. As such, it is necessary to conduct more studies and evaluations to prevent crashes and/or mitigate injuries of children in child-carrying bicycles. This study primarily aims to comprehend the kinematic behavior and injury risks to a child seated in a child seat attached to a bicycle when it is struck from the rear by a car. <em>Method:</em> First, collision tests were conducted to investigate the effects of bicycle tire sizes where a car collides against a bicycle with a rear-mounted child seat. The Hybrid III 3-year-old was seated in the child seat behind the Hybrid III 5F, representing a bicycle rider. Second, a finite element (FE) analysis was conducted for the same collision configurations as the tests. The FE analysis using Hybrid III dummy and THUMS models was employed, and the time frame was calculated from the moment the car began making contact with the bicycle to when the child collided with the adult. <em>Results:</em> The 26-inch tire bicycle lifted its front wheel upward, while the 20-inch tire bicycle pushed forward without lifting. The risk of injury to the child’s head was in the order of ground impact, adult rider impact, and vehicle hood impact. The FE analysis confirmed that both the child passenger and an adult rider could sustain injuries when contacting with each other. <em>Conclusions:</em> Our current study has demonstrated that the kinematic behavior of the bicycle and potential injuries to the child passenger and adult rider differed between bicycles with 26 and 20-inch tire sizes. <em>Practical Applications:</em> The findings are useful in the selection of bicycles suitable for child seats and in the design of child seats tailored to bicycles with different tire sizes.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"91 ","pages":"Pages 437-446"},"PeriodicalIF":3.9,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142653095","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":"A large language model framework to uncover underreporting in traffic crashes","authors":"Cristian Arteaga,&nbsp;JeeWoong Park","doi":"10.1016/j.jsr.2024.11.009","DOIUrl":"10.1016/j.jsr.2024.11.009","url":null,"abstract":"<div><div><em>Introduction:</em> Crash reports support the development of traffic safety countermeasures, but these reports often suffer from underreporting of crucial crash factors due to miscoded entries during data collection. To rectify these issues, the current practice relies on manual information rectification, which is time consuming and error prone, especially with large data volumes. To address these hurdles, we develop a framework to analyze traffic crash narratives and uncover underreported crash factors by capitalizing on the capabilities of Large Language Models (LLM). <em>Method:</em> The framework integrates procedures for prompt definition, selection of LLM generation parameters, output parsing, and underreporting determination. For evaluation, we present a case study on identification of underreported alcohol involvement in traffic crashes. We investigate the framework’s identification accuracy in relation to different underlying LLMs (i.e., ChatGPT, Flan-UL2, and Llama-2), prompt framings (i.e., explicit vs. implicit matching), and generation parameters (i.e., sampling temperature and nucleus probability). Our validation dataset consists of 500 crash reports from the State of Massachusetts. <em>Results:</em> Analysis results demonstrate that the developed framework achieves a recall and precision of up to 1.0 and 0.93, respectively, indicating a successful retrieval of underreported instances. These findings indicate that the developed framework addresses a critical gap in the existing traffic safety analysis workflow by enabling safety analysts to uncover underreporting in crash data efficiently and accurately, without the need for extensive expertise in natural language processing. <em>Practical Applications:</em> Thus, the developed approach offers unprecedented opportunities to maximize the quality and comprehensiveness of traffic crash records, paving the way for more effective countermeasure development.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 1-13"},"PeriodicalIF":3.9,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662539","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":"Drivers’ long-term crash risks associated with being ticketed for speeding","authors":"Darren Walton ,&nbsp;Ross Hendy","doi":"10.1016/j.jsr.2024.10.009","DOIUrl":"10.1016/j.jsr.2024.10.009","url":null,"abstract":"<div><div><em>Introduction</em>: This research analyzes the relationship between police-issued tickets for speeding and the crash risk of those drivers, in New Zealand, between 2015–2019. <em>Method</em>: The main data are constructed through data-matching license details of crash outcomes with all officer-issued tickets for speeding between 2015–2016 (N = 534,935). The sub-group of drivers that accumulate tickets is compared to a coarsened exact matched set of drivers of the same age. <em>Results:</em> There is a strong relationship between the number of tickets a person has in a two-year period (2015–16) and the likelihood of a crash outcome (2017–2019). However, the accumulation of tickets is not the best predictor of crash likelihood. A combination of the excess in speed <em>and</em> the accumulation of tickets increases the relative odds of a subsequent crash. These results are discussed considering the threshold at which New Zealand criminalizes alcohol-relating offending (notionally 4.2 times the base rate crash risk). The same rate of elevated crash risk exists when a driver has one ticket for being 10 km/h over the speed limit and has another speeding ticket within two years.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"91 ","pages":"Pages 431-436"},"PeriodicalIF":3.9,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142653096","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":"Factors influencing behavioral intentions to use conditionally automated vehicles","authors":"Sjaan Koppel ,&nbsp;David B. Logan ,&nbsp;Xin Zou ,&nbsp;Fareed Kaviani ,&nbsp;Hayley McDonald ,&nbsp;Joseph F. Hair Jr ,&nbsp;Renée M. St. Louis ,&nbsp;Lisa J. Molnar ,&nbsp;Judith L. Charlton","doi":"10.1016/j.jsr.2024.10.006","DOIUrl":"10.1016/j.jsr.2024.10.006","url":null,"abstract":"<div><div><em>Background:</em> This study explored factors influencing the acceptance of conditionally automated vehicles among Australian drivers by extending the Technology Acceptance Model with the Technology Readiness Index. <em>Method:</em> Data from an online survey of 844 participants were analyzed using partial least squares structural equation modeling (PLS-SEM). <em>Results:</em> Perceived usefulness had the strongest direct effect on behavioral intention (0.469, p &lt; 0.001), followed by attitude (0.318, p &lt; 0.001). Innovativeness positively influenced behavioral intention (0.183, p &lt; 0.001), while insecurity had a negative impact (−0.071, p &lt; 0.01). Optimism and discomfort were not significant. Perceived usefulness also had significant indirect effects through attitude (0.156, p &lt; 0.001) and trust (0.072, p &lt; 0.001). Perceived ease of use indirectly influenced behavioral intention through perceived usefulness (0.306, p &lt; 0.001), attitude (0.102, p &lt; 0.001), trust (0.047, p &lt; 0.001), and their combinations. Trust indirectly affected behavioral intention via attitude (0.130, p &lt; 0.001). Perceived security and privacy risks had indirect negative effects through trust and attitude (−0.035, p &lt; 0.001; −0.005, p &lt; 0.05). <em>Conclusion:</em> These results suggest that fostering acceptance among less tech-savvy individuals may help promote positive attitudes, increase conditionally automated vehicle adoption, and potentially enhance road safety. <em>Practical implications:</em> These findings suggest a need for targeted programs to enhance perceived usefulness and trust while addressing security and privacy concerns, ultimately contributing to safer road systems through the adoption of conditionally automated vehicles.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"91 ","pages":"Pages 423-430"},"PeriodicalIF":3.9,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532197","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":"Recent Latino immigrants to Miami-Dade County, Florida: Impaired driving behaviors during the initial years after immigration and the pandemic lockdown","authors":"Eduardo Romano ,&nbsp;Mariana Sanchez","doi":"10.1016/j.jsr.2024.09.009","DOIUrl":"10.1016/j.jsr.2024.09.009","url":null,"abstract":"<div><div><em>Introduction</em>: Typically, recent Latino immigrants (RLIs) experience a decline in driving while impaired (DWI) rates soon after immigration, largely due to limited access to vehicles. Such a transitional period offers a window of opportunity for intervention for RLIs at risk of engaging in DWI and riding with an impaired driver (RWID). This manuscript examines the rates of DWI, RWID, and driving while impaired by drugs (DWID) among RLIs upon arrival to Miami/Dade County (MDC), Florida. <em>Methods:</em> Collected between 2018 and 2021, data originates from a longitudinal study examining self-reported drinking and driving trajectories among 540 RLIs to MDC. At baseline retrospective pre-immigration data were obtained simultaneously with first-year post-immigration data. Two follow-up surveys conducted one year apart (N=531 and N=522), collect data on RLIs initial 3 years in the United States. <em>Results:</em> Pre- to post-immigration trajectories for mean number of drinks per month (d/m) revealed a “U-shaped” curve: 18.3 d/m, 13.9 d/m, 10.4 d/m, 12.9 d/m, and 16.4 d/m, from pre-immigration (T0), first year (T1), second year before COVID (T2-BC) and during the pandemic lockdown (T2-DC), and third year in the United States (T3). The use of illicit drugs showed a constant decline, from 14.6% at T0 to 2.1% at T3. The prevalence of DWI at T1 was significantly lower compared to rates in the country of origin (T0) and continued declining through T3. DWID rates remained low across the assessment period. RWID was significantly more prevalent than DWI across all study time points. C<em>onclusions:</em> Although the relatively low prevalence of DWI, drug use, and DWID among the RLIs during their initial years in the United States is encouraging, the surge in alcohol use at T3 warns about the need for interventions to prevent increases in DWI. <em>Practical applications:</em> Findings from the present study point to an opportunity to develop early interventions to prevent the escalation of impaired driving among RLIs to MDC.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"91 ","pages":"Pages 401-409"},"PeriodicalIF":3.9,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532195","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}