Prediction and interpretive of motor vehicle traffic crashes severity based on random forest optimized by meta-heuristic algorithm.

Providing accurate prediction of the severity of traffic collisions is vital to improve the efficiency of emergencies and reduce casualties, accordingly improving traffic safety and reducing traffic congestion. However, the issue of both the predictive accuracy of the model and the interpretability of predicted outcomes has remained a persistent challenge. We propose a Random Forest optimized by a Meta-heuristic algorithm prediction framework that integrates the spatiotemporal characteristics of crashes. Through predictive analysis of motor vehicle traffic crash data on interstate highways within the United States in 2020, we compared the accuracy of various ensemble models and single-classification prediction models. The results show that the Random Forest (RF) model optimized by the Crown Porcupine Optimizer (CPO) has the best prediction results, and the accuracy, recall, f1 score, and precision can reach more than 90 %. We found that factors such as Temperature and Weather are closely related to vehicle traffic crashes. Closely related indicators were analyzed interpretatively using a geographic information system (GIS) based on the characteristic importance ranking of the results. The framework enables more accurate prediction of motor vehicle traffic crashes and discovers the important factors leading to motor vehicle traffic crashes with an explanation. The study proposes that in some areas consideration should be given to adding measures such as nighttime lighting devices and nighttime fatigue driving alert devices to ensure safe driving. It offers references for policymakers to address traffic management and urban development issues.

Unveiling the risks of speeding behavior by investigating the dynamics of driver injury severity through advanced analytics.

Single-vehicle crashes, particularly those caused by speeding, result in a disproportionately high number of fatalities and serious injuries compared to other types of crashes involving passenger vehicles. This study aims to identify factors that contribute to driver injury severity in single-vehicle crashes using machine learning models and advanced econometric models, namely mixed logit with heterogeneity in means and variances. National Crash data from the Crash Report Sampling System (CRSS) managed by the National Highway Traffic Safety Administration (NHTSA) between 2016 and 2018 were utilized for this study. XGBoost and Random Forest models were employed to identify the most influential variables using SHAP (Shapley Additive Explanations), while a mixed logit model was utilized to model driver injury severity accounting for unobserved heterogeneity in the data collection process. The results revealed a complex interplay of various factors that contribute to driver injury severity in single-vehicle crashes. These factors included driver characteristics such as demographics (male and female drivers, age below 26 years and between 35 and 45 years), driver actions (reckless driving, driving under the influence), restraint usage (lap-shoulder belt usage and unbelted), roadway and traffic characteristics (non-interstate highways, undivided and divided roadways with positive barriers, curved roadways), environmental conditions (clear and daylight conditions), vehicle characteristics (motorcycles, displacement volumes up to 2500 cc and 5,000-10,000 cc, newer vehicles, Chevy and Ford vehicles), crash characteristics (rollover, run-off-road incidents, collisions with trees), temporal characteristics (midnight to 6 AM, 10 AM to 4 PM, 4th quarter of the analysis period: October to December, and the analysis year of 2017). The findings emphasize the significance of driving behavior and roadway design to speeding behavior. These aspects should be given high priority for driver training as well as the design and maintenance of roadways by relevant agencies.

Non-benzodiazepine Hypnotics and Police-Reported Motor Vehicle Crash Risk among Older Adults: A Sequential Target Trial Emulation.

Non-benzodiazepine hypnotics ( "Z-drugs") are prescribed for insomnia, but might increase risk of motor vehicle crash (MVC) among older adults through prolonged drowsiness and delayed reaction times. We estimated the effect of initiating Z-drug treatment on the 12-week risk of MVC in a sequential target trial emulation. After linking New Jersey driver licensing and police-reported MVC data to Medicare claims, we emulated a new target trial each week (July 1, 2007 - October 7, 2017) in which Medicare fee-for-service beneficiaries were classified as Z-drug-treated or untreated at baseline and followed for an MVC. We used inverse probability of treatment and censoring weighted pooled logistic regression models to estimate risk ratios (RR) and risk differences with 95% bootstrap confidence limits (CLs). There were 257,554 person-trials, of which 103,371 were Z-drug-treated and 154,183 untreated, giving rise to 976 and 1,249 MVCs, respectively. The intention-to-treat RR was 1.06 (95%CLs 0.95, 1.16). For the per-protocol estimand, there were 800 MVCs and 1,241 MVCs among treated and untreated person-trials, respectively, suggesting a reduced MVC risk (RR 0.83 [95%CLs 0.74, 0.92]) with sustained Z-drug treatment. Z-drugs should be prescribed to older patients judiciously but not withheld entirely over concerns about MVC risk.

Comparative analysis of injury identification using KABCO and ISS in linked North Carolina trauma registry and crash data.

OBJECTIVE: The purpose of this study was to examine differences between police-reported injury severity and trauma registry data among persons with linked records in North Carolina and quantify the degree of alignment. METHODS: We analyzed linked North Carolina trauma registry and motor vehicle crash data from 2018. Injury severity identification was assessed using police-reported 5-point scale KABCO from crash data and Injury Severity Score (ISS) from trauma records. The analysis was stratified by age, sex/gender, race, ethnicity, and road users type to examine differences across groups. We calculated sensitivity, specificity, positive predictive values, and negative predictive values between police-reported injury severity and trauma registry data using ISS as the gold standard. RESULTS: A higher proportion of patients were classified as suspected minor injuries (39.0%) compared to moderate injuries in trauma registry (25.1%). Police-reported crash data underreported injury severity when compared to trauma registry data. Police-reported KABCO had a higher degree of specificity when classifying minor injuries (79.3%) but substantially underestimated seriously injured patients, with a sensitivity of 49.9%. These findings were also consistent when stratified by subpopulations. CONCLUSION: Hospital-based motor vehicle crash data are a main source of injury severity identification for road safety. Police-reported data were relatively accurate for minor injuries but not seriously injured patients. Understanding the characteristics of each data source both separately and linked will be critical for problem identification and program development to move toward a safe transportation system for all road users.

Injury and fatality risks for child pedestrians and cyclists on public roads.

BACKGROUND: Pedestrians and cyclists are often referred to as "vulnerable road users," yet most research is focused on fatal crashes. We used fatal and nonfatal crash data to examine risk factors (i.e., relationship to an intersection, urbanicity, crash circumstances, and vehicle type) for police-reported pedestrian and cyclist injuries on public roads among children aged 0-9 and aged 10-19. We also compared risk factors among these two age groups with adults aged 20-29 and aged 30-39. METHODS: Crash data were obtained for 2016-2020 from the National Highway Traffic Safety Administration's Fatality Analysis Reporting System for fatal crash injuries and Crash Report Sampling System for nonfatal crash injuries. We collected data on victim demographics, roadway, and vehicle- and driver-related factors. Descriptive analyses were conducted between and within pedestrian and cyclist victims. RESULTS: We analyzed 206,429 pedestrian injuries (36% in children aged 0-19) and 148,828 cyclist injuries (41% in children aged 0-19) from 2016 to 2020. Overall, child pedestrians had lower injury rates than adults, but children aged 10-19 had greater cycling crash rates than adults. Almost half of the pedestrian injuries in children aged 0-9 were "dart-out" injuries (43%). In the majority of the cyclist injuries, children in both age groups failed to yield to vehicles (aged 0-9 = 40% and aged 10-19 = 24%). For children and all ages included in the study, the fatality risk ratio was highest when pedestrians and cyclists were struck by larger vehicles, such as trucks and buses. Further exploration of roadway factors is presented across ages and transportation mode. CONCLUSION: Our findings on child, driver, vehicle, and roadway factors related to fatal and nonfatal pedestrian and cyclist injuries may help to tailor prevention efforts for younger and older children.

State cannabis laws and cannabis positivity among fatally injured drivers.

BACKGROUND: As of November 8, 2023, 24 states and the District of Columbia have legalized cannabis for both recreational and medical use (RMCL-states), 14 states have legalized cannabis for medical use only (MCL-states) and 12 states have no comprehensive cannabis legislation (NoCL-states). As more states legalize cannabis for recreational use, it is critical to understand the impact of such policies on driving safety. METHODS: Using the 2019 and 2020 Fatality Analysis Reporting System data, we performed multivariable logistic regression modeling to explore the association between state level legalization status and cannabis positivity using toxicological testing data for 14,079 fatally injured drivers. We performed a sensitivity analysis by including multiply imputed toxicological testing data for the 14,876 eligible drivers with missing toxicological testing data. RESULTS: Overall, 4702 (33.4%) of the 14,079 fatally injured drivers tested positive for cannabis use. The prevalence of cannabis positivity was 30.7% in NoCL-states, 32.8% in MCL-states, and 38.2% in RMCL-states (p < 0.001). Compared to drivers fatally injured in NoCL-states, the adjusted odds ratios of testing positive for cannabis were 1.09 (95% confidence interval: 0.99, 1.19) for those fatally injured in MCL-states and 1.54 (95% confidence interval: 1.34, 1.77) for those fatally injured in RMCL-states. Sensitivity analysis yielded similar results. CONCLUSIONS: Over one-third of fatally injured drivers tested positive for cannabis use. Drivers fatally injured in states with laws permitting recreational use of cannabis were significantly more likely to test positive for cannabis use than those in states without such laws. State medical cannabis laws had little impact on the odds of cannabis positivity among fatally injured drivers.

Alcohol-related non-fatal motor vehicle crash injury in the US from 2019 to 2022.

Background: Information on recent alcohol-related non-fatal motor vehicle crash (MVC) injuries is limited.Objectives: To analyze alcohol-related non-fatal MVC injuries, 2019-2022, considering COVID-19 and Stay-at-Home policies.Methods: State-level counts of alcohol-related non-fatal MVC injuries (involving individuals age 15+) from Emergency Medical Services data in 18 US states, chosen for comprehensive coverage, were analyzed for the annual rate. The total non-fatal MVC injury count in each state served as the denominator. We used analysis of variance to evaluate annual rate changes from 2019 to 2022 and used robust Poisson regression to compare annual mean rates to the 2019 baseline, pre-pandemic, excluding Quarter 1 due to COVID-19's onset in Quarter 2. Additional Poisson models compared rate changes by 2020 Stay-at-Home policies.Results: Data from 18 states were utilized (N = 1,487,626, 49.5% male). When evaluating rate changes of alcohol-related non-fatal MVC injuries from period 1 (Q2-4 2019) through period 4 (Q2-4 2022), the rate significantly increased from period 1 (2019) to period 2 (2020) by 0.024 (p = .003), then decreased from period 2 to period 4 (2022) by 0.016 (p = .04). Compared to the baseline (period 1), the rate in period 2 was 1.27 times higher. States with a 2020 Stay-at-Home policy, compared to those without, had a 30% lower rate (p = .05) of alcohol-related non-fatal MVC injuries. States with partial and mandatory Stay-at-Home policies had a 5.2% (p = .01) and 10.5% (p < .001) annual rate decrease, respectively.Conclusion: Alcohol-related non-fatal MVC injury rates increased initially (2019-2020) but decreased thereafter (2020-2022). Stay-at-home policies effectively reduced these rates.

Investigating the severity of single-vehicle truck crashes under different crash types using mixed logit models.

INTRODUCTION: Almost 90% of fatal road crashes occur in developing countries. Among these countries, Iran has a noticeable fatal crash rate of 21.47 deaths per 100,000 persons. Improving the safety of trucks is of particular importance in Iran where road freight is used to transport almost 90% of the commodities. Researchers have suggested dichotomizing crashes into single- and multi-vehicle categories and found that when this is performed vast differences can be identified between the mechanisms behind these categories of crashes, particularly when investigating truck crashes. METHOD: This study investigated single-vehicle truck crashes in Khorasan Razavi province in Iran from 2013 to 2021. Likelihood ratio tests were employed to show that separate models are statistically valid for different crash types. Subsequently, three mixed logit crash-type models were developed to investigate 5,703 single-vehicle truck crashes. RESULTS: Four significant variables were exclusive to collisions with an object (brake failure, ABS, primary roads, and rainy or snowy weather), five significant variables were associated with run-off-road crashes (driving a loaded truck, speed limit (>60 km/h), paved shoulders, driving uphill, and inability to control the truck), and three significant variables were associated with overturn crashes (overloaded truck, curved roads, and changing direction suddenly). In all crash types, both fastening the seatbelt and speeding were found to be significant factors. CONCLUSION: The research highlights the need to analyze single-vehicle truck crashes using distinct crash type models and highlights the unique contributing factors of three common single-vehicle crash types. PRACTICAL APPLICATIONS: The study presents recommendations for policy to address key crash risks for trucks in Iran, including education and training to improve driver experience, compliance with seat belt usage, enforcement of speeding, and vehicle technologies to monitor drivers.

A systematic review of the impact of brain tumours on risk of motor vehicle crashes.

PURPOSE: Brain tumours are associated with neurocognitive impairments that are important for safe driving. Driving is vital to maintaining patient autonomy, despite this there is limited research on driving capacity amongst patients with brain tumours. The purpose of this review is to examine MVC risk in patients with brain tumours to inform development of clearer driving guidelines. METHODS: A systematic review was performed using Medline and EMBASE. Observational studies were included. The outcome of interest was MVC or measured risk of MVC in patients with benign or malignant brain tumours. Descriptive analysis and synthesis without meta-analysis were used to summarise findings. A narrative review of driving guidelines from Australia, United Kingdom and Canada was completed. RESULTS: Three studies were included in this review. One cohort study, one cross-sectional study and one case-control study were included (19,135 participants) across United States and Finland. One study evaluated the incidence of MVC in brain tumour patients, revealing no difference in MVC rates. Two studies measured MVC risk using driving simulation and cognitive testing. Patients found at higher risk of MVC had greater degrees of memory and visual attention impairments. However, predictive patient and tumour characteristics of MVC risk were heterogeneous across studies. Overall, driving guidelines had clear recommendations on selected conditions like seizures but were vague surrounding neurocognitive deficits. CONCLUSION: Limited data exists regarding driving behaviour and MVC incidence in brain tumour patients. Existing guidelines inadequately address neurocognitive complexities in this group. Future studies evaluating real-world data is required to inform development of more applicable driving guidelines. SYSTEMATIC REVIEW REGISTRATION NUMBER: PROSPERO 2023 CRD42023434608.

A systematic review and meta-analysis of data linkage between motor vehicle crash and hospital-based datasets.

Motor vehicle crash data linkage has emerged as a vital tool to better understand the injury outcomes and the factors contributing to crashes. This systematic review and meta-analysis aims to explore the existing knowledge on data linkage between motor vehicle crashes and hospital-based datasets, summarize and highlight the findings of previous studies, and identify gaps in research. A comprehensive and systematic search of the literature yielded 54 studies for a qualitative analysis, and 35 of which were also considered for a quantitative meta-analysis. Findings highlight a range of viable methodologies for linking datasets, including manual, deterministic, probabilistic, and integrative methods. Designing a linkage method that integrates different algorithms and techniques is more likely to result in higher match rate and fewer errors. Examining the results of the meta-analysis reveals that a wide range of linkage rates were reported. There are several factors beyond the approach that affect the linkage rate including the size and coverage of both datasets and the linkage variables. Gender, age, crash type, and roadway geometry at the crash site were likely to be associated with a record's presence in a linked dataset. Linkage rate alone is not the only important metric and when linkage rate is used as a metric in research, both police and hospital rates should be reported. This study also highlights the importance of examining and accounting for population and bias introduced by linking two datasets.

Impact of alcohol driving-while-impaired license suspension duration on future alcohol-related license events and motor vehicle crash involvement in North Carolina, 2007 to 2016.

BACKGROUND/PURPOSE: License suspensions are a strategy to address alcohol-impaired driving behavior and recidivism following an alcohol driving while impaired (alcohol-DWI) conviction. Little is known about the specific impacts of conviction-related suspensions on safety outcomes and given recent fluctuations in alcohol-impaired driving behavior, crashes, and suspension trends, updated and focused assessments of this intervention are necessary. This study aimed to 1) examine the association between type of recent alcohol-DWI suspension and having a secondary alcohol-related license outcome and/or future crash event in North Carolina (NC) between 2007 and 2016; and 2) assess potential modification of these associations by race/ethnicity. METHODS: We used linked NC licensing data, NC crash data, and county-level contextual data from a variety of data sources. We compared individuals ages 21 to 64 who sustained initial (1-year) versus repeat (4-year) suspensions for alcohol-related license and crash involvement outcomes. We estimated unadjusted and adjusted hazard ratios (aHRs) using Cox proportional hazards models and produced Kaplan-Meier (KM) survival curves using a three-year follow-up period. After observing statistically significant modification by race/ethnicity, we calculated stratified aHRs for each outcome (Black and White subgroups only, as other subgroups had low numbers of outcomes). RESULTS: 122,002 individuals sustained at least one alcohol-DWI conviction suspension (117,244 initial, 4,758 repeat). Adjusted KM survival curves indicated that within three years of the index suspension, the predicted risks of having a license outcome and crash outcome were about 8 % and 15 %, respectively, among individuals with an initial suspension and 5 % and 10 %, respectively, among individuals with a repeat suspension. After adjusting for potential confounding, we found that compared to those with an initial suspension, those with repeat suspensions had a lower incidence of future license (aHR: 0.49; 95 % CI: 0.42, 0.57) and crash outcomes (aHR: 0.67; 95 % CI: 0.60, 0.75). Among Black individuals, license outcome incidence was 162 % lower among repeat versus initial index suspension groups (aHR: 0.38; 95 % CI: 0.26, 0.55), while for White individuals, the incidence was 87 % lower (aHR: 0.54; 95 % CI: 0.45, 0.64). Similarly, crash incidence for repeat versus initial suspensions among Black individuals was 56 % lower (aHR: 0.64; 95 % CI: 0.50, 0.83), while only 39 % lower among White individuals (aHR: 0.72; 95 % CI: 0.63, 0.81). CONCLUSIONS: Decreased incidence of both license and crash outcomes were observed among repeat versus initial index suspensions. The magnitude of these differences varied by race/ethnicity, with larger decreases in incidence among Black compared to White individuals. Future research should examine the underlying mechanisms leading to alcohol-impaired driving behavior, convictions, recidivism, and crashes from a holistic social-ecological perspective so that interventions are designed to both improve road safety and maximize other critical public health outcomes, such as access to essential needs and services (e.g., healthcare and employment).

A Lightweight Pre-Crash Occupant Injury Prediction Model Distills Knowledge From Its Post-Crash Counterpart.

Accurate occupant injury prediction in near-collision scenarios is vital in guiding intelligent vehicles to find the optimal collision condition with minimal injury risks. Existing studies focused on boosting prediction performance by introducing deep-learning models but encountered computational burdens due to the inherent high model complexity. To better balance these two traditionally contradictory factors, this study proposed a training method for pre-crash injury prediction models, namely, knowledge distillation (KD)-based training. This method was inspired by the idea of knowledge distillation, an emerging model compression method. Technically, we first trained a high-accuracy injury prediction model using informative post-crash sequence inputs (i.e., vehicle crash pulses) and a relatively complex network architecture as an experienced "teacher". Following this, a lightweight pre-crash injury prediction model ("student") learned both from the ground truth in output layers (i.e., conventional prediction loss) and its teacher in intermediate layers (i.e., distillation loss). In such a step-by-step teaching framework, the pre-crash model significantly improved the prediction accuracy of occupant's head abbreviated injury scale (AIS) (i.e., from 77.2% to 83.2%) without sacrificing computational efficiency. Multiple validation experiments proved the effectiveness of the proposed KD-based training framework. This study is expected to provide reference to balancing prediction accuracy and computational efficiency of pre-crash injury prediction models, promoting the further safety improvement of next-generation intelligent vehicles.

Alternative approaches to modeling heterogeneity to analyze injury severity sustained by motorcyclists in two-vehicle crashes.

The presence of unobserved factors in the motorcycle involved two-vehicle crashes (MV) data could lead to heterogenous associations between observed factors and injury severity sustained by motorcyclists. Capturing such heterogeneities necessitates distinct methodological approaches, of which random and scale heterogeneity models are paramount. Herein, we undertake an empirical evaluation of random and scale heterogeneity models, exploring their efficacy in delineating the influence of external determinants on the degree of injury severity in crashes. Within the effects of scale heterogeneity, this study delves into two dominant models: the scaled multinomial logit model (S-MNL) and its generalized counterpart, the G-MNL, which encompasses both the S-MNL and the random parameters multinomial logit model (RPL). While the random heterogeneity domain is represented by the random parameters multinomial logit and an upgraded variant - the random parameters multinomial logit model with heterogeneity in means and variances (RPLHMV). Motorcycle involved two-vehicle crashes data were extracted from the UK STATS19 dataset from 2016 to 2020. Likelihood ratio tests are computed to assess the temporal variability of the significant factors. The test result demonstrates the temporal variations over a five-year study period. Some very important differences started to show up across the years based on the model estimation results: that the RPLHMV model statistically outperforms the G-MNL model in the 2016, 2018, and 2019 models, while the S-MNL model is statistically superior in the 2017 and 2020 years. These important findings suggest that the origin of heterogeneity in explaining factor weights can be captured by scale effects, not just random heterogeneity. In addition, the model results further show that motorcyclists' injury severities are significantly affected by motorcycle-related characteristics; there is the added factor of external influences, such as non-motorcycle drivers (males, young drivers, and elderly drivers) and vehicles (the moving status, age, and types of vehicles) that collide with motorcycles. The results of this paper are anticipated to help policymakers develop effective strategies to mitigate motorcycle involved two-vehicle crashes by implementing appropriate measures.

Injury severity analysis of single-vehicle and two-vehicle crashes with electric scooters: A random parameters approach with heterogeneity in means and variances.

In recent years, the electric scooter has become one of the most popular means of transportation on short trips. Due to the lag in the formulation of transportation policies and regulations, coupled with the increasing number of electric scooter crashes, there has been growing concern about the safety of pedestrians and electric scooter riders. For the first time in the extant literature, this study aims to analyze injury severity of electric scooter crashes by unobserved heterogeneity modeling approaches. A random parameters approach with heterogeneity in means and variances is utilized to examine the factors influencing injury severity, using data collected from the STATS19 road safety database. Electric scooter crashes are classified as single-vehicle crashes and two-vehicle crashes, with injury severity categorized into two groups: fatalities or serious injuries, and slight injuries. The model estimation was conducted by considering several variables including roadway, environment, temporality, vehicle, and rider characteristics, as well as second-party vehicle and driver characteristics and manners of collision specific to two-vehicle crashes. The results of the model estimation reveal that certain factors had relatively stable effects with the varying degree of crash injury severity outcomes in both single-vehicle crashes and two-vehicle crashes. These factors include nighttime incidents, weekdays, male riders, and an increase in rider age, all of which are associated with more severe injury outcomes. Moreover, the random parameters logit model with heterogeneity in means and variances is more flexible in accounting for unobserved heterogeneity and exhibits better goodness of fit. This study improves the understanding of electric scooter safety, and the finding can better inform public policy regarding electric scooter use to improve road safety and reduce injury severity of electric scooter crashes.

Migraine headaches are associated with motor vehicle crashes and driving habits among older drivers: Prospective cohort study.

BACKGROUND: Migraine headache is common in older adults, often causing symptoms that may affect driving safety. This study examined associations of migraine with motor vehicle crashes (MVCs) and driving habits in older drivers and assessed modification of associations by medication use. METHODS: In a multi-site, prospective cohort study of active drivers aged 65-79 (53% female), we assessed prevalent migraine (i.e., ever had migraine, reported at enrollment), incident migraine (diagnosis first reported at a follow-up visit), and medications typically used for migraine prophylaxis and treatment. During 2-year follow-up, we recorded self-reported MVCs and measured driving habits using in-vehicle GPS devices. Associations of prevalent migraine with driving outcomes were estimated in multivariable mixed models. Using a matched design, associations of incident migraine with MVCs in the subsequent year were estimated with conditional logistic regression. Interactions between migraine and medications were tested in all models. RESULTS: Of 2589 drivers, 324 (12.5%) reported prevalent migraine and 34 (1.3%) incident migraine. Interactions between migraine and medications were not statistically significant in any models. Prevalent migraine was not associated with MVCs in the subsequent 2 years (adjusted OR [aOR] = 0.98; 95% CI: 0.72, 1.35), whereas incident migraine significantly increased the odds of having an MVC within 1 year (aOR = 3.27; 1.21, 8.82). Prevalent migraine was associated with small reductions in driving days and trips per month and increases in hard braking events in adjusted models. CONCLUSION: Our results suggest substantially increased likelihood of MVCs in the year after newly diagnosed migraine, indicating a potential need for driving safety interventions in these patients. We found little evidence for MVC risk or substantial changes in driving habits associated with prevalent migraine. Future research should examine timing, frequency, and severity of migraine diagnosis and symptoms, and use of medications specifically prescribed for migraine, in relation to driving outcomes.

Development of German pedelec (and bicycle) crashes between 2013 and 2021.

INTRODUCTION: The continuous growth in the use of e-bikes (in Germany mostly pedelecs that support pedaling up to 25 km/h) raises questions about the use of historic crash data for the development of road safety measures. The aim of this study was to address this issue, by conducting a longitudinal analysis of pedelec and bicycle crash data over a period of nine years to identify trends and to clarify whether such trends are specific to pedelecs. METHOD: We analyzed 95,338 police reported pedelec and bicycle injury crashes from 2013 through 2021. The dataset consisted of crashes from three federal states of Germany: Brandenburg, Hesse and Saxony. Data were analyzed with respect to sex and age distribution, time, location and type of accident, conflict partner, cause of crash and injury severity. RESULTS: Many of the analyzed variables showed a considerable degree of temporal stability, with differences as well as similarities between the two bicycle types staying quite consistent over the years. One notable difference was the mean age of the involved riders, with crashed pedelec riders being significantly older than conventional cyclists. At the same time, however, the mean age of these pedelec riders has decreased by eight years over time. Single vehicle crashes were consistently more common for pedelec riders than for cyclists. Similarly, pedelec rider crashes went with a higher injury severity over all the years. CONCLUSIONS AND PRACTICAL APPLICATIONS: While, on a more detailed level, we found differences between the two bicycle types, overall crash characteristics were remarkably similar and consistent over time. Our findings provide no clear argument for road safety measures that are specifically designed to target pedelecs. Instead, the stable crash total, and the increases in ridership of both bicycles and pedelecs, highlight the demand for new, innovative solutions to improve cycling safety in general.

Risks of motor vehicle crash before and during pregnancy: A population-based controlled study in Taiwan.

BACKGROUND: Changes in risk of motor vehicle crashes (MVCs) during pregnancy are less known, and very few studies have assessed this issue by using unselected population-based datasets and adopting a before-and-during design. The study aimed to address the risk of MVC events in association with pregnancy using a national pregnant women cohort in Taiwan. METHODS: We conducted a self-matched design in which each woman served as a driver before and during pregnancy. A total of 1,372,664 pregnant women with live birth(s) at 18-50 years of age between 2008 and 2017 were identified from the Birth Notification dataset. MVC events as a driver were ascertained from the Police-reported Traffic Accident Registry dataset. We calculated the rate ratio (RR) with a 95 % confidence interval (CI) using the conditional Poisson regression model to compare the MVC event rates between pre-pregnancy and pregnancy periods. RESULTS: The overall rate of MVC events was significantly reduced during pregnancy (RR = 0.69, 95 % confidence interval (CI) = 0.68-0.71). Mothers with alcoholism during pregnancy were associated with an increased RR at 2.00 but with a very wide CI. Reduction in RR was primarily attributed to the reduced MVC event rate involving scooter crashes (0.60, 95 % CI = 0.58-0.62). CONCLUSION: Although MVC event rates decreased during women became pregnant, many women drivers were still involved in MVCs during pregnancy. Their potential maternal and perinatal conditions along with their offspring's health outcomes need further investigations.

Promoting transportation safety in adolescence: the drivingly randomized controlled trial.

BACKGROUND: The impact of young drivers' motor vehicle crashes (MVC) is substantial, with young drivers constituting only 14% of the US population, but contributing to 30% of all fatal and nonfatal injuries due to MVCs and 35% ($25 billion) of the all medical and lost productivity costs. The current best-practice policy approach, Graduated Driver Licensing (GDL) programs, are effective primarily by delaying licensure and restricting crash opportunity. There is a critical need for interventions that target families to complement GDL. Consequently, we will determine if a comprehensive parent-teen intervention, the Drivingly Program, reduces teens' risk for a police-reported MVC in the first 12 months of licensure. Drivingly is based on strong preliminary data and targets multiple risk and protective factors by delivering intervention content to teens, and their parents, at the learner and early independent licensing phases. METHODS: Eligible participants are aged 16-17.33 years of age, have a learner's permit in Pennsylvania, have practiced no more than 10 h, and have at least one parent/caregiver supervising. Participants are recruited from the general community and through the Children's Hospital of Philadelphia's Recruitment Enhancement Core. Teen-parent dyads are randomized 1:1 to Drivingly or usual practice control group. Drivingly participants receive access to an online curriculum which has 16 lessons for parents and 13 for teens and an online logbook; website usage is tracked. Parents receive two, brief, psychoeducational sessions with a trained health coach and teens receive an on-road driving intervention and feedback session after 4.5 months in the study and access to DriverZed, the AAA Foundation's online hazard training program. Teens complete surveys at baseline, 3 months post-baseline, at licensure, 3months post-licensure, 6 months post-licensure, and 12 months post-licensure. Parents complete surveys at baseline, 3 months post-baseline, and at teen licensure. The primary end-point is police-reported MVCs within the first 12 months of licensure; crash data are provided by the Pennsylvania Department of Transportation. DISCUSSION: Most evaluations of teen driver safety programs have significant methodological limitations including lack of random assignment, insufficient statistical power, and reliance on self-reported MVCs instead of police reports. Results will identify pragmatic and sustainable solutions for MVC prevention in adolescence. TRIAL REGISTRATION: ClinicalTrials.gov # NCT03639753.

Using motor vehicle crash records for injury surveillance and research in agriculture and forestry.

PROBLEM: Fatal injuries in the agriculture, forestry, and fishing sector (AgFF) outweigh those across all sectors in the United States. Transportation-related injuries are among the top contributors to these fatal events. However, traditional occupational injury surveillance systems may not completely capture crashes involving farm vehicles and logging trucks, specifically nonfatal events. METHODS: The study aimed to develop an integrated database of AgFF-related motor-vehicle crashes for the southwest (Arkansas, Louisiana, New Mexico, Oklahoma, and Texas) and to use these data to conduct surveillance and research. Lessons learned during the pursuit of these aims were cataloged. Activities centered around the conduct of traditional statistical and geospatial analyses of structured data fields and natural language processing of free-text crash narratives. RESULTS: The structured crash data in each state include fields that allowed farm vehicles or equipment and logging trucks to be identified. The variable definitions and coding were not consistent across states but could be harmonized. All states recorded data fields pertaining to person, vehicle, and crash/environmental factors. Structured data supported the construction of crash severity models and geospatial analyses. Law enforcement provided additional details on crash causation in free-text narratives. Crash narratives contained sufficient text to support viable machine learning models for farm vehicle or equipment crashes, but not for logging truck narratives. DISCUSSION: Crash records can help to fill research and surveillance gaps in AgFF in the southwest region. This supports traffic safety's evolution to the current Safe System paradigm. There is a conceptual linkage between the Safe System and Total Worker Health approaches, providing a bridge between traffic safety and occupational health. PRACTICAL APPLICATIONS: Despite limitations, crash records can be an important component of injury surveillance for events involving AgFF vehicles. They also can be used to inform the selection and evaluation of traffic countermeasures and behavioral interventions.

Injury severity of single-vehicle large-truck crashes: accounting for heterogeneity.

This research examines the injury severity of single-vehicle large-truck crashes in Florida while exploring the role of heterogeneity. A random parameter ordered logit (RPOL) model was applied to 27,505 single-vehicle large-truck crashes from 2007 to 2016 in Florida, and the contributing factors were identified. Random parameters and interaction effects were introduced to the model to determine the heterogeneity and its potential sources. The results suggested that driving speed of 76-120 mph and defective tires were the most influential factors in crash injury severity, increasing the probability of severe crashes. Regarding truckers' attributes, asleep or fatigued conditions and driving under the influence were correlated with a higher possibility of severe crashes. Interestingly, the results showed that truckers from outside the state of Florida were less likely to cause severe single-vehicle large-truck crashes compared to their Floridian counterparts. Y-intersections were also found as a high-risk location for single-vehicle large-truck crashes, leading to more severe outcomes. Regarding heterogeneity, the results indicated that the impacts of driving speed (26-50 mph) and light condition (dark - not lighted) significantly varied among the observations, and these variations could be attributed to driver action, vision obstruction, driver distraction, roadway type and roadway alignment.