Traffic Injury Prevention最新文献

{"title":"Identifying the reciprocal causation between hit-and-run behavior and crash injury severity.","authors":"Guopeng Zhang, Qianwei Xuan, Ying Cai, Xianghong Hu, Nianyi Hu, Xinguo Jiang, Xinkun Yao","doi":"10.1080/15389588.2024.2402464","DOIUrl":"https://doi.org/10.1080/15389588.2024.2402464","url":null,"abstract":"<p><strong>Objective: </strong>Hit-and-run behavior is believed to exacerbate the injury severity of traffic crashes due to the delayed emergency response for the victims. However, several previous studies indicated the opposite finding that hit-and-run crashes were associated with less severe injuries. The relevant studies mainly identified the statistical associations between hit-and-run behavior and injury severity without revealing causation between them. To this end, the study aims to explore the reciprocal causation between the two variables.</p><p><strong>Method: </strong>The two-stage probit model with endogenous regressors is employed to identify the reciprocal causation between hit-and-run behavior and crash injury severity for single- and two-vehicle crashes, respectively, with the use of crash data extracted from the Crash Report Sampling System and Fatality Analysis Reporting System (2016-2019).</p><p><strong>Results: </strong>The results indicate that 1) for both single- and two-vehicle crashes, the fleeing behavior can significantly increase the injury severity of the victims in the crashes while the severe injury of the victims has a negative impact on the propensity of such behavior, 2) the propensity of hit-and-run behavior is influenced by various instrumental variables such as driver age, gender, alcohol involvement, weekday, area type, and light condition, and 3) crash injury severity is significantly related to the victim age, gender, and vehicle damage.</p><p><strong>Conclusions: </strong>There is a reciprocal causation between hit-and-run behavior and injury severity in traffic crashes. The analytical results can provide a reasonable explanation for the counterintuitive finding on hit-an-run crashes and help mitigate the injury severity.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-8"},"PeriodicalIF":1.6,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142367360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated framework for driving risk evaluation that combines lane-changing detection and an attention-based prediction model.","authors":"Zhongxiang Feng, Xinyi Wei, Yu Bi, Dianchen Zhu, Zhipeng Huang","doi":"10.1080/15389588.2024.2399301","DOIUrl":"https://doi.org/10.1080/15389588.2024.2399301","url":null,"abstract":"<p><strong>Objective: </strong>In recent years, the increase in traffic accidents has emerged as a significant social issue that poses a serious threat to public safety. The objective of this study is to predict risky driving scenarios to improve road safety.</p><p><strong>Methods: </strong>On the basis of data collected from naturalistic driving real-vehicle experiments, a comprehensive framework for identifying and analyzing risky driving scenarios, which combines an integrated lane-changing detection model and an attention-based long short-term memory (LSTM) prediction model, is proposed. The performance of the 4 machine learning methods on the CULane data set is compared in terms of model running time and running speed as evaluation metrics, and the ultrafast network with the best performance is selected as the method for lane line detection. We compared the performance of LSTM and attention-based LSTM on the basis of the prediction accuracy, recall, precision, and F1 value and selected the better model (attention-based LSTM) for risky scenario prediction. Furthermore, Shapley additive explanation analysis (SHAP) is used to understand and interpret the prediction results of the model.</p><p><strong>Results: </strong>In terms of algorithm efficiency, the running time of the ultrafast lane detection network only requires 4.1 ms, and the average detection speed reaches 131 fps. For prediction performance, the accuracy rate of attention-based LSTM reaches 96%, the precision rate is 98%, the recall rate is 96%, and the F1 value is 97%.</p><p><strong>Conclusions: </strong>The improved attention-based LSTM model is significantly better than the LSTM model in terms of convergence speed and prediction accuracy and can accurately identify risky scenarios that occur during driving. The importance of factors varies by risky scenario. The characteristics of the yaw rate, speed stability, vehicle speed, acceleration, and lane change significantly influence the driving risk, among which lane change has the greatest impact. This study can provide real-time risky scenario prediction, warnings, and scientific decision guidance for drivers.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-9"},"PeriodicalIF":1.6,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142367357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive analysis of traumatic rupture of the aorta in road traffic crashes: incorporating epidemiological insights and K-prototype clustering.","authors":"Zhengwei Ma, Liming Zhang, Changren Qiu, Gang Xu, Ziyang Liang, Wei Wei","doi":"10.1080/15389588.2024.2398669","DOIUrl":"https://doi.org/10.1080/15389588.2024.2398669","url":null,"abstract":"<p><strong>Objectives: </strong>The identification of crash characteristics associated with traumatic rupture of the aorta (TRA) can significantly enhance countermeasures against TRA. Conventional epidemiological approaches struggle to adequately handle the substantial variability of traffic crash data. Consequently, this study aims to integrate conventional epidemiological analysis with data-driven cluster analysis to more comprehensively analyze TRA-related crash characteristics.</p><p><strong>Methods: </strong>A total of 350 unweighted TRA crashes were extracted from traffic crash databases including comprehensive crash details and injury descriptions. Initially, a selection was made of 11 continuous variables and 9 categorical variables, describing crash characteristics. After correlation analysis and principal component analysis were applied to the dataset, K-prototype clustering was finally conducted using 6retained categorical variables and 6 principal components derived from the continuous variables.</p><p><strong>Results: </strong>This study found significant age and gender disparities among TRA victims, with 50% falling within the age range of 25-59 years and an overwhelming majority (62.2%) being males. Side impacts emerged as the primary cause of TRA-related crashes (37.2%), followed by collisions with off-road objects (28.6%) and head-on collisions (24.8%). Cluster analyses revealed 6 distinct clusters within the TRA-related crash dataset. These clusters were characterized by factors such as vehicle model year, curb weight, collision dynamics, and seatbelt usage, providing a deeper understanding of the heterogeneity in TRA incidents and their associated factors.</p><p><strong>Conclusions: </strong>Although limitations related to available data sources and factors such as accompanying injuries and vehicle weight warrant further comprehensive investigations in the future, this study contributes valuable insights into TRA analysis to enhance understanding and prevention strategies.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-10"},"PeriodicalIF":1.6,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142367359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of a surrogate measure-based safety index for predicting injury crashes at signalized intersections.","authors":"Maryam Hasanpour, Bhagwant Persaud, Robert Mansell, Craig Milligan","doi":"10.1080/15389588.2024.2397652","DOIUrl":"https://doi.org/10.1080/15389588.2024.2397652","url":null,"abstract":"<p><strong>Objectives: </strong>The paper develops a machine learning-based safety index for classifying traffic conflicts that can be used to estimate the frequency of signalized intersection crashes, with a focus on the more severe ones that result in fatal and severe injury. The number of conflicts in different severity levels categorized by the safety index is used as an explanatory variable for developing statistical models for pro-actively estimating crashes.</p><p><strong>Methods: </strong>Video-derived conflicts in different severity levels between left-turning vehicles and opposing through vehicles, a well-recognized severe injury crash typology at signalized intersections, were identified by jointly integrating the indicators of frequency and severity, using an autoencoder neural network integration method to develop anomaly scores. Regression models were then developed to relate crashes at the same intersections to the classified conflicts based on the value of their safety indexes. Cumulative Residual plots were investigated. Finally, equations defining the boundary between consecutive anomaly score levels were developed to facilitate application in practice.</p><p><strong>Results: </strong>Regression models for total and fatal plus severe (FSI) crashes utilizing classified extreme conflicts based on anomaly scores were found to outperform the models using total conflicts. The improvement is more pronounced for FSI crashes. The results also suggest that the machine learning integration method can efficiently classify conflicts accurately according to crash severity levels since the higher anomaly score is associated with a higher crash severity level (i.e., FSI).</p><p><strong>Conclusions: </strong>The proposed framework represents a methodological advancement in traffic conflict-based estimation of crashes using a machine learning model to classify conflicts by their anomaly scores. For jurisdictions without the resources to develop such a model to classify conflicts for their own datasets, the simple equations defining the boundary between consecutive anomaly score levels could be used as an approximation.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-10"},"PeriodicalIF":1.6,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142332372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Age-related driver injury occurrence from crashes at curve-grade combined segments.","authors":"Hellen Shita, Norris Novat, Francisca Kasubi, Norran Kakama Novat, Priyanka Alluri, Valerian Kwigizile","doi":"10.1080/15389588.2024.2390093","DOIUrl":"https://doi.org/10.1080/15389588.2024.2390093","url":null,"abstract":"<p><strong>Objectives: </strong>Due to their relatively complex roadway characteristics, horizontal and vertical curve segments are associated with decreased visibility and a higher risk of rollovers. Multiple studies have identified the associated risk of young and older drivers separately in such complicated driving environments. This study investigated the relationship between driver age and injury occurrence from crashes occurring along curve-grade combined segments.</p><p><strong>Methods: </strong>Crash data recorded in Ohio State between 2012 and 2017 were used in this study. Driver age was categorized into 3 groups: teen (age <20 years), adult (age 20-64), and older adult (age >64). Descriptive statistics were summarized using random forest, gradient boosting, and extreme gradient boosting (XGBoost) to estimate the probability of a driver incurring an injury in case of a crash at curve-grade combined segments. The area under the receiver operating characteristics curve (AUROC) was used to select the best performing model. Partial dependence plots (PDPs) were used to interpret the model results.</p><p><strong>Results: </strong>The probability of injury occurrence is different for older drivers compared to teen and adult drivers. Although teen and adult drivers showed a higher probability of sustaining injuries in crashes with an increase in the degree of curvature, older drivers were more likely to sustain injuries in roadways with higher annual average daily traffic (AADT), steeper grades, and more occupants in the vehicle. Older drivers were observed to have a higher probability of sustaining injuries during peak hours and when unrestrained compared to teen and adult drivers.</p><p><strong>Conclusions: </strong>The results emphasize the significance of tailored education and outreach countermeasures, particularly for teen and older drivers, aimed at decreasing the likelihood of injuries in such driving environments. This research adds to the expanding body of knowledge concerning the age-related occurrence of driver injuries resulting from crashes at curve-grade combined segments. The study findings provide insights into the potential over- or underrepresentation of certain age groups in analyzing crash injury occurrence. The insights gained from the machine learning analysis could also assist policymakers, transportation agencies, and traffic safety experts in developing targeted strategies to enhance road safety and protect vulnerable age groups.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-10"},"PeriodicalIF":1.6,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142332367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the effectiveness of safety countermeasures at highway-railway grade crossing based on a machine learning framework.","authors":"Mohammadali Zayandehroodi, Barat Mojaradi, Morteza Bagheri","doi":"10.1080/15389588.2024.2387713","DOIUrl":"https://doi.org/10.1080/15389588.2024.2387713","url":null,"abstract":"<p><strong>Objective: </strong>This research aims to cluster similar highway-railway grade crossings (HRGCs) to examine the safety countermeasures at HRGCs.</p><p><strong>Methods: </strong>The methodology integrates inventory and collision data from Federal Railroad Association (FRA) data set during years 2010 to 2022 . The XGBoost and random forest (RF) algorithms are employed to identify influential collision severity factors. Then, the deep latent class analysis (DLCA) method is utilized on selected inventory factors as important features to cluster similar HRGCs. Afterward, collision modification factor (CMF) and standard error (SE) measures are computed for each countermeasure through collisions within each HRGC cluster.</p><p><strong>Results: </strong>XGBoost successfully identified 20 important collision and inventory factors with importance levels exceeding 94%, such as the number of daily trains and the surface material. Then, the DCLA method achieved 4 distinct clusters optimized by high similarity within each cluster and significant independence among clusters. The effectiveness of countermeasures was computed in terms of CMF and SE. The CMF results demonstrated that bells achieved superior safety compared to other countermeasures in clusters with sharper track angles and high maximum train speeds. Implementing bells decreased collisions across Clusters 1 and 4, with reductions of 53% (CMF = 0.47) and 46% (CMF = 0.54), respectively.</p><p><strong>Conclusions: </strong>The results highlight XGBoost's capability to identify important collision and inventory factors, successfully uncovering 20 of the most important factors. The DCLA clustering method forms 4 distinct groups marked by substantial internal similarity within each cluster. This approach contributes to a clearer understanding of how each countermeasure impacts collision frequency. The findings highlight the varying effectiveness of different countermeasures across clusters, improving decision making for safety at HRGCs. The study highlights the efficacy of crossbucks in addressing safety concerns during moderate traffic conditions, particularly evident in environments with a highway speed limit between 100 and 125 mph. Additionally, bells demonstrate notable effectiveness in areas with sharper track angles.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-8"},"PeriodicalIF":1.6,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142332370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mobile phone conversation during nighttime driving: Effects on driving behavior.","authors":"Eleni Andrikopoulou, Ioanna Spyropoulou","doi":"10.1080/15389588.2024.2393228","DOIUrl":"https://doi.org/10.1080/15389588.2024.2393228","url":null,"abstract":"<p><strong>Objectives: </strong>This study aims to investigate the impact of mobile phone use (specifically, conversation), considering various use modes, on driving behavior at night. Mobile phone use is a source of driver distraction and has been associated with increased accident risk. Driving at night also entails higher accident risk and severity compared to daytime driving. Several studies have investigated the impact of mobile phone use on driving behavior; however, only a few have explored the differences between the different use modes. Most present studies involved daytime driving, although mobile phone use at night is equally if not more prevalent.</p><p><strong>Method: </strong>A driving simulator experiment was designed in which 55 participants drove under nighttime simulator conditions, in different road environments (urban and rural) and under different types of distraction: no distraction, handheld, wired earphone, and speaker mode. The drives were performed during late afternoon and evening hours to resemble nighttime conditions both in the simulator and in the actual environment. Participants also completed a questionnaire for collection of different types of data.</p><p><strong>Results: </strong>Results highlight the effect of mobile phone use on driving behavior, through specific indicators. Mobile phone use resulted in reduced 85th percentile driving speed and 85th percentile acceleration and increased reaction time and lateral deviation. However, safer stopping distance was observed in rural roads. Parameters relative to mobile phone use familiarity and exposure were found to mitigate mobile phone use effects.</p><p><strong>Conclusions: </strong>Mobile phones affect driving behavior at night in a similar manner to that noted in several different studies considering daytime driving. The hands-free regulation should be revisited, because driver distraction also occurred under this particular use mode. Further research is required considering mobile phone use familiarity and exposure and effects of mobile phone use, because the latter is reduced with an increase in the former. Stopping distance, an understudied but more immediate surrogate measure of road safety, was increased with mobile phone use, mainly as a result of the risk compensation behavior that drivers adopt, indicating that more research is required in this field.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-10"},"PeriodicalIF":1.6,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142332373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An evaluation of front seat distance from rear facing child restraint systems in prevention of injury in frontal crash tests.","authors":"Matthew R Maltese, Maya DiFrischia, Jonathan Judge","doi":"10.1080/15389588.2024.2391453","DOIUrl":"https://doi.org/10.1080/15389588.2024.2391453","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Objectives: &lt;/strong&gt;Elevated head injury incidence in infants compared to toddlers involved as occupants in motor vehicle crashes has been demonstrated in multiple population-representative crash databases. Further, experimental studies have revealed a potential injury mechanism &lt;i&gt;via&lt;/i&gt; impact between a rear-facing, CRS-restrained child and the back of the vehicle seat or console on the row in front of the CRS. Subsequently, experimental studies have suggested that bracing the CRS against the seat immediately in front of the CRS could mitigate head injury, but also indicated that more research was necessary. Thus, we investigated the effect of bracing against the front seat, as well as distance from the front seat with rear-facing infant carriers and rear-facing convertibles, with a focus on changes to measured head, neck and chest injury metrics in rear facing CRSs. Further, we examined the effect of using the infant carrier with and without a base on these injury metrics.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;34 frontal sled tests at 30 or 35 mph were conducted using a simulated rear-row vehicle seat and structure representing the front seatback. A Q1.5 anthropomorphic test device (ATD) was placed in a single make/model LATCH-affixed rear-facing convertible or single make/model infant carrier; infant carrier without base was affixed with lap and shoulder belt. To evaluate the effect of bracing and distance, tests were conducted with a 300, 140, 70, or 15 mm gap between the CRS seatback and the front seatback, or a touching (0 mm) or braced (-20 mm) condition. Bayesian regression models quantified the effects of various predictors and model uncertainty.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;For tests with the convertible CRS, no head contact was observed between the head and the front vehicle seatback. For the infant carrier, head contact occurred at both 70 and 140 mm distances but not the other distances. On average, the -20, 0, or 15 mm distances yielded a 60% reduction in head injury criterion with 15 millisecond window (HIC15), and a 60% to 80% reduction in neck tension, compared to the 70 and 140 mm distances; chest acceleration also decreased for the convertible seat only. In the case of both carriers and convertibles, each mm of distance the CRS moves away from the front seatback up to 70 mm, adds 5.3 HIC15 points (95% Credible Interval (CrI):[4.6, 6.2]), and 3.5 Newtons (95% CrI: [2.2, 4.8]) of neck tension, on average.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Placing a rear facing CRS, both convertibles and infant carriers, against or close to the seatback of the seat immediately in front of the CRS reduces head and tensile neck injury criteria in ATDs. The amount of gap between the front seat and the rear facing CRS is strongly and positively correlated with HIC for both convertibles and infant carriers. RF infant carriers with and without a base yield comparable injury metrics and kinematics when touching or nearly touching the back of","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-9"},"PeriodicalIF":1.6,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142332368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing highway Loop Safety Level through proactive risk-based assessment of geometric configuration using lateral acceleration.","authors":"Omid Rahmani, Hosein Ghasemzadeh Tehrani, Iman Aghayan","doi":"10.1080/15389588.2024.2394110","DOIUrl":"https://doi.org/10.1080/15389588.2024.2394110","url":null,"abstract":"<p><strong>Objective: </strong>Loop ramps are complex due to their combination of horizontal curves and vertical alignments. Analyzing driving behavior and measuring safety levels can provide insights for designers, helping to improve the performance and alignment of design assumptions with actual driving behavior on loops. Therefore, the primary objective of this research is to explore the safety, performance and geometric configuration of the main body and general shape of free-flow loop ramps in the free-following mode.</p><p><strong>Methods: </strong>The study uses data from a UAV to investigate vehicle demand behavior. Maximum lateral acceleration (<i>a</i>_y,i) in loops is used as a Surrogate Safety Measure (SSM), along with a new parameter, the a/b ratio, to determine the general shape of loop bodies. The study presents the Loop Safety Level (LSL), an approach for proactive risk analysis and ranking that relies on threshold lateral acceleration (<i>a</i>_t), 85th percentile maximum lateral acceleration (<math><mrow><mi>a</mi></mrow></math>_y,max,85%), and crash analysis.</p><p><strong>Results: </strong>A higher LSL value points to a more critical safety concern regarding the loop's shape in relation to lateral acceleration caused by driving behaviors. Comparing crash statistics with lateral acceleration results enables the LSL to provide appropriate safety ratings and diagnose loop segment safety. A prediction model for maximum lateral acceleration, based on loop geometry, demonstrates a good fit (R²=0.88) between observed and predicted data.</p><p><strong>Conclusions: </strong>The study enhances understanding of safety considerations in geometric configuration and general shape enhancement of loops during the design process.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-11"},"PeriodicalIF":1.6,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142332369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the impact of cannabis use on freeway driving performance and practices: A comparative analysis with placebo and alcohol-influenced driving.","authors":"Timothy Brown, Cole Kruse, Rose Schmitt, Gary Gaffney, Gary Milavetz","doi":"10.1080/15389588.2024.2393215","DOIUrl":"https://doi.org/10.1080/15389588.2024.2393215","url":null,"abstract":"<p><strong>Objective: </strong>The objectives of this study were 1) to identify the effects cannabis has on driving performance and individual motor practices when on the freeway compared to placebo and 2) to bring context to the effects of cannabis on driving by comparing effect sizes to those of alcohol.</p><p><strong>Methods: </strong>Data for analysis was collected from a study of fifty-three participants with a history of tetrahydrocannabinol (THC) cannabis use who completed three visits in randomized order (placebo (0% THC), 6.18% THC, and 10.5% THC). Data for the alcohol analysis was from a subset of eighteen of these participants with a history of recent alcohol use that completed a fourth alcohol visit that targeted a .05 g/210L breath alcohol content (BrAC) during the drive. Comparisons were made using an analysis of variance approach with the SAS General Linear Models Procedure. Cohen's <i>d</i> effect sizes were calculated for the cannabis and alcohol conditions relative to placebo for both the full sample and alcohol subset.</p><p><strong>Results: </strong>Standard deviation of lane position (SDLP) for cannabis significantly increased compared to placebo and the effect size was comparable to that of alcohol at .05 BrAC. Lane departures for cannabis significantly increased relative to placebo as did the time out of the lane. Cannabis use resulted in an increased amount of time at 10% or more below the speed limit for the 6.18% THC condition. Relative to alcohol, cannabis produced more time at slower speeds and less time at speeds more than 10% above the speed limit.</p><p><strong>Conclusions: </strong>Multiple factors of lateral and longitudinal vehicle control on the freeway showed statistical significance. Drivers under the influence of cannabis exhibited higher rates of driving errors but also showed more cautious behaviors such as generally lower speeds on the freeway. Compared with alcohol, effect sizes varied. For longitudinal control, there were larger effect sizes for alcohol with speed effects in opposite directions, but relatively equivalent effect sizes for lateral control and driving errors associated with lane keeping.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-10"},"PeriodicalIF":1.6,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142301094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}