Crash Injury Research Articles

Design of energy-absorbing bionic workstation table for trains and research on occupant collision injury

Secondary impact is a significant contributor to occupant injuries in train crashes. To mitigate collision injuries, a workbench with energy-absorbing properties was developed, drawing inspiration from leaf-vein and honeycomb structures. A local collision model of a dummy compartment was established to evaluate the impact of different sandwich thicknesses on occupant injuries. By optimizing the wall thickness of the energy-absorbing sandwich, the optimal gradient parameters were determined. The assessment of occupant collision injury from the workstation table with a graded wall thickness sandwich was conducted using the LSTC Hybrid III 50th seating dummy. The results demonstrate that the workstation table influences the occupant's movement posture during a collision, preventing head impact with the tabletop. Apart from slightly elevated leg injuries in the dummy, which remained within permissible values, the head, neck, and chest injuries were reduced by 98.45%, 48.17%, and 81.18%, respectively.

Supervised and unsupervised techniques to analyze risk factors associated with motorcycle crash.

Motorcycles are one of the highly used modes of transport in Barcelona, Spain, in particular, and in many different regions, in general. This situation is compromising safety on the road and may be attributed to a potential increase in traffic crashes. Therefore, this study examines several risk factors and their consequential impacts on the level of injury that is resulted in case of a traffic crash. Two approaches are employed to analyze the risk factors, including a supervised learning technique which is a binary probit model, and an unsupervised technique which is the Kohonen clustering. The results for both models show that alcoholism and road in poor condition can indeed increase the probability of having different levels of injuries as reasons for the crash. Elderly users are less likely to be involved in motorcycle crash injuries compared to other age categories, especially the age group that ranges from 25 to 40 years old which has the highest odds. For both techniques, the performance in analyzing the utilized data shows that both approaches can be successfully utilized for this type of dataset. This study highlights the considerable danger faced by motorcyclists due to various risk factors. It stresses the critical importance of maintaining roads in optimal condition not just for efficient travel but also to enhance motorcyclists' safety. Additionally, the research underscores the significant threat posed by speeding, particularly exceeding speed limits, to motorcyclists' safety, emphasizing the urgent need for more 30 km/h speed limit zones and stricter enforcement of speed regulations. As a result, the research has identified several risk factors that increase the likelihood of severe or fatal injuries among motorcyclists in Barcelona and has suggested certain recommendations to mitigate their impact.

MODELLING THE FACTORS AFFECTING CRASH OCCURRENCE AND FREQUENCY RESULTED FROM MOBILE PHONE USE WHILE DRIVING: EVIDENCE FROM AL-NAJAF, IRAQ

The recent reports of road traffic accident statistics in Iraq have disclosed a rise in the number of crash injuries resulted from the use of mobile phone while driving. This paper aims to explore the factors contributing to the occurrence and prevalence of such crashes and near crashes in Al-Najaf governorate, Iraq. A representative sample of 417 drivers were interviewed as part of a questionnaire driving survey. Several frequency and modelling analyses were conducted using the IBM SPSS software. The frequency analysis revealed a high use of mobile phones for calling and texting activities while driving. Almost 20% and 55% of the interviewed drivers reported their involvement in a crash or in a near crash because of such use, respectively. Regarding the developed logistic models, the crash involvement sequential regression analysis revealed that factors such gender, education, handheld phoning, calling-answering frequency, and inadequate driving can affect the likelihood of crash occurrence. In contrast, the ordinal logistic near miss models revealed that age, gender, high phone use rate, and improper driving due to such use are influential factors in rising the likelihood of being in multiple near crashes. The analysis results confirm the influence of using phones in distracting the attention of drivers and hence threating their lives; as a result, these findings would be enlightening for agencies and policy makers interested in highway safety.

Injury severity prediction and exploration of behavior-cause relationships in automotive crashes using natural language processing and extreme gradient boosting

Addressing the global challenge of traffic crashes necessitates transcending traditional statistical models, which often fail to fully capture the interactions between factors causing crashes. This oversight restricts the predictive accuracy and adaptability of current methodologies. Additionally, there is a notable gap in research that examines the links between behavior-cause relationships and crash injury severity. Our study deploys Natural Language Processing (NLP) and Frequent Pattern (FP) growth algorithm to mine crash narratives for behavior-cause connections, combines with the predictive strength of eXtreme Gradient Boosting (XGBoost) and the interpretative clarity offered by SHapley Additive exPlanations (SHAP), our approach not only predicts crash injury severity with satisfactory precision but also explains the influence of specific behavior-cause and environment conditions on crash outcomes. The integration of NLP and XGBoost, complemented by SHAP insights, has shown promising results with an accuracy of 0.79, outperforming traditional discrete choice models and competes closely with other machine learning approaches, including Support Vector Machines, Random Forest, Categorical Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM). Through detailed textual analysis and the establishment of a behavior-cause matrix, identifying five broad crash causes linked to 141 specific crash cause with behaviors, we uncover critical patterns such as the prominence of distracted driving in severe crashes. This comprehensive approach not only fills a critical research gap by linking behavior-cause relationships with injury severity but also sets the stage for developing targeted interventions to enhance road safety.

A comparative analysis of risk factors in taxi-related crashes using XGBoost and SHAP

Taxis play a crucial role in urban public transportation, but the traffic safety situation of taxi drivers is far from optimistic, especially considering the introduction of ride-hailing services into the taxi industry. This study conducted a comparative analysis of risk factors in crashes between traditional taxi drivers and ride-hailing taxi drivers in China, including their demographic characteristics, working conditions, and risky driving behaviors. The data was collected from 2,039 traditional taxi drivers and 2,182 ride-hailing taxi drivers via self-reported questionnaires. Four XGBoost models were established, taking into account different types of taxi drivers and crash types. All models showed acceptable performance, and SHAP explainer was used to analyze the model results. The results showed that for both taxi drivers, risk factors related to risky driving behaviors are more important in predicting property damage (PD) crashes, while risk factors related to working conditions are more important in predicting person injury (PI) crashes. However, the relative importance of each risk factor varied depending on the type of crashes and the type of taxi drivers involved. Furthermore, the results also validated certain interactions among the risk factors, indicating that the combination of certain factors generated a greater impact on crashes compared to individual factors alone. These findings can provide valuable insights for formulating appropriate measures to enhance road safety for taxi driver.

Effect of vehicle speed on injury of child passenger in frontal crashes

Speed is major cause of death to vehicle occupants especially children. Studying effect of speed on child injury severity is crucial to establishing safety regulations. This work investigates the effect of vehicle speed on the severity of injury sustained by three-year-old (3YO) children. Restrained 3YO child dummy Finite Element (FE) model was used to conduct crash simulation at impact speed of 40km/h, 46 km/h and 48 km/h in Ford Taurus FE model using LS DYNA code. Vehicle deceleration was found to increase with increase in speed. Head Injury Criteria (HIC36), HIC15, chest acceleration (CA), chest severity index (CSI), chest deflection (CD) and neck moment (NM) were evaluated and compared with National Highway Traffic Safety Administration (NHTSA) threshold. It was found that the injury parameters sustained by 3YO child increased with increase in speed. HIC36 was found to be above threshold for 48km/h and 46km/h. Speed of 40 km/h had all the injury values below the threshold. Chest deflection was lower than the threshold for all the three speeds. Chest acceleration and neck moment were also presented and their values also increase with speed, though, NHTSA doesn’t capture their threshold. The study disseminates information on the effect of speeding on child injuries in frontal crashes for road safety agents and vehicle designers and users. Limiting vehicle speed is therefore crucial to child occupant protection.

Study of the necessity of a speed monitoring display at the returning chicane on a freeway bypass

Objective Roadwork zones represent a hazardous place within the highway system, with higher crash rates and injury severity, often due to excessive speed and noncompliance with speed limits. On freeways, a common layout is to close a full roadway and to divert traffic onto the opposite roadway, with one lane closed to accommodate the redirected traffic, by driving through the median strip. In this scenario, the second chicane can be a hazardous point if it is not correctly signaled. This paper examines the need to install a speed monitoring display (SMD) before the returning chicane on a bypass. Methods A two-phase study was conducted on a Spanish freeway where a roadway was temporarily closed. Two measurement points were established on the bypass, one in the middle and one at the end, prior to the return chicane through the median strip. During Phase 1, the portable SMD was installed and during Phase 2, it was removed. The average and the 85th percentile of the speed distribution at each point during both phases were compared. Additionally, mean difference tests were conducted and a speed prediction model was developed. Results With the SMD, drivers reduced their speed from the midpoint to the returning chicane, within the range of 7-10 km/h. Conversely, upon removal of the SMD, motorists increased their speed while driving through the bypass, resulting in excessive speed at the most hazardous point, the chicane leading back to the original roadway. The difference in mean speed between the two phases was 18 km/h at the returning chicane. Conclusions In addition to the traffic calming measures implemented prior to entering roadwork zones on freeways, which are conveniently established in the standards; it is necessary to evaluate potentially dangerous areas of the layout and implement additional measures where required. Specifically, in the case of final chicanes of bypasses with reduced radii, it is recommended that a speed monitoring display be installed as a mandatory element in order to inform drivers of this challenging segment. Highway administrations around the world should maintain a SMD at the returning chicane of a bypass while roadworks last.

Toll road crash severity using mixed logit model incorporating heterogeneous mean structures

The current study examined 1,465 crash observations (2017–2021) from Louisiana, identifying significant variables grouped into three major categories: drivers’, crash, and road characteristics. Considering crash injury severity as a dependent variable, we employed classic Multinomial Logit (MNL) model, and several other models to address unobserved heterogeneity in crash data including Random Parameter Logit (RPL), Random Parameter Logit with Heterogeneity in Means (RPLHM), and Random Parameter Logit with Heterogeneity in Means and Variance (RPLHMV). Our findings highlight the impact of factors such as driver gender, age, traffic violations, driver distractions, crash types, surface conditions, and roadway attributes on crash injury severity. These insights emphasise the complexity of toll road safety and inform targeted interventions to mitigate crash injury severity. Notably, male drivers and those under 25 years old increased property damage likelihood, while factors like driver distractions and lower posted speed limits reduced the likelihood of severe injuries or fatalities.

Effectiveness of Data Imbalance Treatment in Weather-Related Crash Severity Analysis

Accurate predictive modeling is often hindered by the prevalent issue of class imbalance within weather-related crash datasets. To address this critical challenge, this study introduces a novel and tailored synthetic data generation technique aimed at effectively handling nominal predictors specific to weather-related crash cases in North Carolina. Data treatment techniques such as the synthetic minority over-sampling technique-nominal (SMOTE-N) and adaptive synthetic-nominal (ADASYN-N) were investigated in this study. A comprehensive comparison of these data treatment techniques is conducted using two prominent machine learning models: the bagging algorithm (random forest [RF]) and the boosting algorithm (extreme gradient boosting [XGBoost]). The findings indicate that the effectiveness of data treatment varies with the severity level and the algorithm used. The ADASYN-N technique was observed to be highly effective for severe and moderate injury crash prediction using both the RF and XGBoost models, while the control dataset and SMOTE-N demonstrated notable performance in property damage only crash prediction using both the RF and XGBoost models. The findings from evaluating the performance of these models with data treatment methods serve as a benchmark for practitioners in selecting appropriate synthetic sample generation techniques, consequently facilitating the development of more accurate crash severity prediction models and contributing to enhanced traffic safety strategies.

Evaluation of DAMAGE Algorithm in Frontal Crashes.

With the current trend of including the evaluation of the risk of brain injuries in vehicle crashes due to rotational kinematics of the head, two injury criteria have been introduced since 2013 - BrIC and DAMAGE. BrIC was developed by NHTSA in 2013 and was suggested for inclusion in the US NCAP for frontal and side crashes. DAMAGE has been developed by UVa under the sponsorship of JAMA and JARI and has been accepted tentatively by the EuroNCAP. Although BrIC in US crash testing is known and reported, DAMAGE in tests of the US fleet is relatively unknown. The current paper will report on DAMAGE in NCAP-like tests and potential future frontal crash tests involving substantial rotation about the three axes of occupant heads. Distribution of DAMAGE of three-point belted occupants without airbags will also be discussed. Prediction of brain injury risks from the tests have been compared to the risks in the real world. Although DAMAGE correlates well with MPS in the human brain model across several test scenarios, the predicted risk of AIS2+ brain injuries are too high compared to real-world experience. The prediction of AIS4+ brain injury risk in lower velocity crashes is good, but too high in NCAP-like and high speed angular frontal crashes.

Examination of Crash Injury Risk as a Function of Occupant Demographics.

The objectives of this study were to provide insights on how injury risk is influenced by occupant demographics such as sex, age, and size; and to quantify differences within the context of commonly-occurring real-world crashes. The analyses were confined to either single-event collisions or collisions that were judged to be well-defined based on the absence of any significant secondary impacts. These analyses, including both logistic regression and descriptive statistics, were conducted using the Crash Investigation Sampling System for calendar years 2017 to 2021. In the case of occupant sex, the findings agree with those of many recent investigations that have attempted to quantify the circumstances in which females show elevated rates of injury relative to their male counterparts given the same level bodily insult. This study, like others, provides evidence of certain female-specific injuries. The most problematic of these are AIS 2+ and AIS 3+ upper-extremity and lower-extremity injuries. These are among the most frequently observed injuries for females, and their incidence is consistently greater than for males. Overall, the odds of females sustaining MAIS 3+ (or fatality) are 4.5% higher than the odds for males, while the odds of females sustaining MAIS 2+ (or fatality) are 33.9% higher than those for males. The analyses highlight the need to carefully control for both the vehicle occupied, and the other involved vehicle, when calculating risk ratios by occupant sex. Female driver preferences in terms of vehicle class/size differ significantly from those of males, with females favoring smaller, lighter vehicles.

Modeling of Risk Factors for Vulnerable Road Users’ Crashes on National Highways Passing through Medium-sized Indian Cities

The safety of vulnerable road users, such as pedestrians, bicyclists and motorised twowheelers, on national highways passing through urban areas has been a significant concern in developing countries like India. This study analyzes Visakhapatnam police crash data (2014-2017) to identify high-risk road segments to vulnerable road users. Establishing negative binomial models helps analyze risk variables for fatal and injury crashes concerning several susceptible road users on mid-block sections. Further, this study focuses on site-specific information collected from road-safety audits, speed, average daily traffic, length of road section and percentage of two-wheelers, also considered to fully help understand the likelihood of a crash. The statistical analysis of this paper identified the risk variables, like length of road segment, presence of service roads, land-use type (i.e., commercial/mixed land use), number of curves and average daily traffic associated with the incidence of fatal and injury crashes of vulnerable road users in Visakhapatnam city, India. The conclusions highlight the crucial safety measures that transportation planners and policymakers must take to create a more secure environment for road users. Keywords: Vulnerable road users, Mid-block sections, Negative binomial models, Crash, Count-data models, Safety measures.

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

BackgroundPedestrians 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.MethodsCrash 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.ResultsWe 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.ConclusionOur 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.

Temporal analysis of factors affecting injury severities of expressway rear-end crashes during weekdays and weekends

ABSTRACT High fatality rates in frequent rear-end crashes have underscored significant safety concerns in China. This study aims to explore the mechanisms and determinants of rear-end crashes, with a particular focus on the factors influencing crash severity during weekdays and weekends (W-W). Employing the Random Parameter Logit Model (RPLM) to account for variability in data, we analyzed W-W rear-end crashes on the Beijing-Shanghai Expressway in Jiangsu province from 2017 to 2019, considering three severity levels: no injury, minor injury, and severe injury. Our comprehensive analysis covered variables from temporal, roadway, vehicle, crash, and environmental categories, alongside calculating the marginal effects of each significant variable on crash severity. Findings reveal temporal instability over the three-year period and notable differences in W-W crash severity. Out of all variables, four displayed random parameter characteristics, indicating potential interactions that influence crash outcomes. Specifically, our results indicate that rear-end crashes involving three or more vehicles on bridges are more likely to result in casualties. Interchange segments typically saw no injuries in two-vehicle crashes. Speeding during winter or on sunny days significantly increases the risk of injuries and fatalities. Furthermore, rear-end crashes in interchange areas during winter are particularly prone to causing injuries. These findings offer guidance for the development of effective safety countermeasures targed at different pediods.

Identifying Important Roadway Corridor Factors which Affect Crashes Influenced by Unsafe Driving Behaviors to Help Law Enforcement Proactively Reduce Crashes

Effective allocation of traffic law enforcement officers can proactively reduce crashes by stopping unsafe drivers before crashes occur, but this can be difficult because of staffing limitations. Therefore, it is important to identify roadway factors that could encourage unsafe driving behaviors so appropriate proactive enforcement methods can be taken. In this paper, signalized arterial roadway corridors in Florida are modeled to identify the important factors that affect the crash rate for seven unsafe driving behaviors. Using a standardized corridor definition, 406 corridors in seven urban Florida counties were identified. These corridors contained 18,518 crashes, including 5053 fatal and injury (FI) crashes, from 2017 to 2021 involving the studied behaviors. Three random forest regression models were developed to identify the important corridor-level factors that affect the crash rate for all crashes (model 1), FI crashes (model 2), and only FI crashes caused by careless or reckless driving (model 3). Corridors in Pasco County, corridors with greater signalized intersection densities, and corridors with six or more lanes had above average predicted crash rates for all three models. In addition, corridors with speed limits greater than 45 mph (model 1), two lanes (models 1 and 3), no school zones (models 1 and 3), bike lanes (models 2 and 3), and no horizontal curvature (models 2 and 3) were predicted to have higher than average crash rates. By considering these factors when making allocation decisions, law enforcement agencies can likely reduce crashes, injuries, and fatalities caused by these unsafe behaviors.

Safety Assessment of Suburban-Type Arterial Roadways: New Findings Using Heterogeneity Models

This study investigates the safety performance of suburban-type roads (STRs), a category of medium-speed arterial roads characterized by high vehicular traffic and limited pedestrian and cyclist mobility. Such roadways present unique challenges in road safety, often overlooked in traditional road design focused predominantly on driver needs. By employing a correlated random parameter ordered probit model to analyze police-reported crash severity data from STRs, this research uncovers critical insights into the factors influencing crash injury severities on these roads. Our findings highlight the nuanced impact of various dimensions, such as road design, traffic volume, and environmental conditions, on STR safety; the results offer a detailed understanding of crash contexts on STRs and point toward targeted interventions for enhancing road safety. These interventions include the potential redesign of roadways to accommodate diverse user needs and the implementation of speed regulation measures tailored to specific road characteristics. This research contributes significantly to the existing body of knowledge on suburban road safety, providing evidence-based recommendations for improving the overall safety of these increasingly prevalent road types. The implications of these findings are far-reaching, offering a foundation for future studies to explore diverse methodologies in road safety analysis and to extend this research to other geographic contexts.

Learning from Moped Crash Data: Identifying Risk Factors Contributing to the Severity of Injuries Sustained by Moped Riders

Mopeds are light vehicles that provide solutions for short-distance travel, traffic congestion, and environmental pollution issues. However, because of the limited physical protection they offer, moped riders are more likely to sustain severe injuries in a crash than vehicle passengers. This study aims to investigate the features and patterns associated with recent moped crashes and analyzed the severity of injuries sustained by riders and related risk factors in 1,657 moped crashes in Michigan during a five-year period (2017–2021). A multinomial logit model was built to examine the association between crash injury severity levels and 13 variables from three main categories: crash features; rider behaviors/cognitive status; and environmental conditions. The descriptive statistics showed that 79% of injuries sustained by moped riders in crashes were minor, and the injured riders tended to be younger, male, and not wearing helmets. The findings indicate a significant correlation between the severity of injuries sustained by riders and several factors: the type of crash (whether single, head-on, or angle collision); the age of the riders (with older riders being more susceptible), visibility conditions (especially during nighttime); and helmet usage. Notably, single and head-on crashes were the primary causes of injuries. These results highlight the importance of heightened vigilance among moped riders, particularly when navigating poorly maintained surfaces or riding under low-light conditions. It is imperative that riders consistently use helmets to minimize risks. Additionally, setting guidelines or restrictions on helmet use, rider age, riding hours, and permissible areas in which to ride could further contribute to improving overall safety for moped riders.

Statistical and Spatial Analysis of Large Truck Crashes in Texas (2017–2021)

Freight transportation, dominated by trucks, is an integral part of trade and production in the USA. Given the prevalence of large truck crashes, a comprehensive investigation is imperative to ascertain the underlying causes. This study analyzed 2017–2021 Texas crash data to identify factors impacting large truck crash rates and injury severity and to locate high-risk zones for severe incidents. Logistic regression models and bivariate analysis were utilized to assess the impacts of various crash-related variables individually and collectively. Heat maps and hotspot analysis were employed to pinpoint areas with a high frequency of both minor and severe large truck crashes. The findings of the investigation highlighted night-time no-passing zones and marked lanes as primary road traffic control, highway or FM roads, a higher posted road speed limit, dark lighting conditions, male and older drivers, and curved road alignment as prominent contributing factors to large truck crashes. Furthermore, in cases where the large truck driver was determined not to be at fault, the likelihood of severe collisions significantly increased. The study’s findings urge policymakers to prioritize infrastructure improvements like dual left-turn lanes and extended exit ramps while advocating for wider adoption of safety technologies like lane departure warnings and autonomous emergency braking. Additionally, public awareness campaigns aimed at reducing distracted driving and drunk driving, particularly among truck drivers, could significantly reduce crashes. By implementing these targeted solutions, we can create safer roads for everyone in Texas.

Estimating crash risk and injury severity considering multiple traffic conflict and crash types: A bivariate extreme value approach

Traffic conflicts are generally considered independent events in existing extreme value theory models to estimate the risk of total or single types of crashes. However, traffic events at a road entity are not necessarily independent interactions and can lead to multiple traffic conflicts with shared common unobserved factors. A comprehensive estimation of crash risks in a road entity needs to consider the correlation of potential traffic conflicts to avoid possible bias in prediction performance and the problem of undetected deficiencies. This study proposes a Bayesian non-stationary bivariate generalised extreme value modelling framework to estimate the severe and non-severe crash risks accounting for the correlation between right-turn and rear-end conflicts at signalised intersections. A deep neural network-based computer vision technique was applied to extract the traffic conflicts from 77 h of video recordings over two right-turn approaches at two signalised intersections in Cairns, Australia. Post encroachment time and modified time to collision were used to characterise right-turn and rear-end conflicts, respectively, while an expected post-collision velocity difference was combined with post encroachment time and modified time to collision for crash risk estimation by injury severity levels. Several covariates were used to address the time-varying heterogeneity of traffic conflict extremes and to estimate the differential crash risks at signal cycles. Results showed a significant correlation between right-turn and rear-end crashes at signal cycle levels, indicating the importance of accounting for the dependency among traffic conflict types. Overall, the bivariate models considering the correlation among traffic conflict types were found to understandably perform better than their univariate counterparts. This study provides a demonstration of a correlated crash risk modelling framework that addresses issues related to the suitable traffic conflict measures, time varying risks (non-stationarity), heterogeneity, and injury severity levels of different crash types.

Delays in blood collection and drug toxicology results among crash-involved drivers arrested for impaired driving

Objective The concentration of drugs in a driver’s system can change between an impaired driving arrest or crash and the collection of a biological specimen for drug testing. Accordingly, delays in specimen collection can result in the loss of critical information that has the potential to affect impaired driving prosecution. The objectives of the study were: (1) to identify factors that influence the time between impaired-driving violations and specimen collections (time-to-collection) among crash-involved drivers, and (2) to consider how such delays affect measured concentrations of drugs, particularly with respect to common drug per se limits. Method Study data included blood toxicology results and crash-related information from 8,923 drivers who were involved in crashes and arrested for impaired driving in Wisconsin between 2019 and 2021. Analyses examined how crash timing and severity influenced time-to-collection and the effects of delays in specimen collection on blood alcohol concentrations (BACs) and blood delta-9-tetrahydrocannabinol (THC) concentrations. Results The mean time-to-collection for the entire sample was 1.80 h. Crash severity had a significant effect on time-to-collection with crashes involving a fatality having the longest duration ( M = 2.35 h) followed by injury crashes ( M = 2.06 h) and noninjury crashes ( M = 1.69 h). Time of day also affected time-to-collection; late night and early morning hours were associated with shorter durations. Both BAC (r = −0.11) and blood THC concentrations (r = −0.16) were significantly negatively correlated with time-to-collection. Conclusions Crash severity and the time of day at which a crash occurs can result in delays in the collection of blood specimens after impaired driving arrests. Because drugs often continue to be metabolized and eliminated between arrest and biological specimen collection, measured concentrations may not represent the concentrations of drugs that were present at the time of driving. This has the potential to affect drug-impaired driving prosecution, particularly in jurisdictions whose laws specify per se impairment thresholds.