Fatal Crashes Research Articles

Effect of spatial relationship between curves on crash severity at horizontal curves in a mountainous terrain

The study aimed to model the effect of spatial relationship between adjacent curves on the severity of curve-based crashes along with driver and crash causal characteristics, reflecting driver’s short-term expectancy. The crash and other associated data was retrieved from the web-based Road Accident Data Management System available in Himachal Pradesh, India, and curvature attributes were extracted using GIS. Overall, the study included 1113 curve based crashes. The driver’s perception of the sharpness of a curve was quantified as a single representative categorical factor based simultaneously on its length and radius using K-Medoid Clustering. Separate crash severity models catering to the two possible approach directions of the subject curve were developed reflecting its independent interaction with its corresponding adjacent curve in each direction. Partial Proportional Odds models were developed to overcome the predictive limitations of Ordinal and Multinomial logit models. Indicators of spatial relationship and the intensity of sharpness of the subject curve were found to be statistically significant. A sharp approach curve (radius:40–60 m) increased the risk of fatality by 2.16 times with a similar increase (2.5 times) observed for a short (length:30–60 m) adjacent curve. Adjacent curves turning in the same direction were 2.34 times more prone to fatalities. A very sharp subject curve with radius ≤ 40 m increased the risk of fatal crashes by 2.5 times, as did the short subject curves (30–60 m) (at least 3 times). Subject curves characterized by a short length and a very sharp curvature contributed relatively 3–4 times more to fatal crashes. The identified risk factors and their impact can help the relevant stakeholders to take appropriate actions and can further assist them in identifying high risk scenarios.

Modeling of drinking and driving behaviors among adolescents and young adults in the United States: Complexities and Intervention outcomes

Alcohol-impaired driving is a formidable public health problem in the United States, claiming the lives of 37 individuals daily in alcohol-related crashes. Alcohol-impaired driving is affected by a multitude of interconnected factors, coupled with long delays between stakeholders’ actions and their impacts, which not only complicate policy-making but also increase the likelihood of unintended consequences. We developed a system dynamics simulation model of drinking and driving behaviors among adolescents and young adults. This was achieved through group model building sessions with a team of multidisciplinary subject matter experts, and a focused literature review. The model was calibrated with data series from multiple sources and replicated the historical trends for male and female individuals aged 15 to 24 from 1982 to 2020. We simulated the model under different scenarios to examine the impact of a wide range of interventions on alcohol-related crash fatalities. We found that interventions vary in terms of their effectiveness in reducing alcohol-related crash fatalities. In addition, although some interventions reduce alcohol-related crash fatalities, some may increase the number of drinkers who drive after drinking. Based on insights from simulation experiments, we combined three interventions and found that the combined strategy may reduce alcohol-related crash fatalities significantly without increasing the number of alcohol-impaired drivers on US roads. Nevertheless, related fatalities plateau over time despite the combined interventions, underscoring the need for new interventions for a sustained decline in alcohol-related crash deaths beyond a few decades. Finally, through model calibration we estimated time delays between actions and their consequences in the system which provide insights for policymakers and activists when designing strategies to reduce alcohol-related crash fatalities.

Visual Function and Driving Performance Under Different Lighting Conditions in Older Drivers: Preliminary Results From an Observational Study.

Age-related vision changes significantly contribute to fatal crashes at night among older drivers. However, the effects of lighting conditions on age-related vision changes and associated driving performance remain unclear. This pilot study examined the associations between visual function and driving performance assessed by a high-fidelity driving simulator among drivers 60 and older across 3 lighting conditions: daytime (photopic), nighttime (mesopic), and nighttime with glare. Active drivers aged 60 years or older participated in visual function assessments and simulated driving on a high-fidelity driving simulator. Visual acuity (VA), contrast sensitivity function (CSF), and visual field map (VFM) were measured using quantitative VA, quantitative CSF, and quantitative VFM procedures under photopic and mesopic conditions. VA and CSF were also obtained in the presence of glare in the mesopic condition. Two summary metrics, the area under the log CSF (AULCSF) and volume under the surface of VFM (VUSVFM), quantified CSF and VFM. Driving performance measures (average speed, SD of speed [SDspeed], SD of lane position (SDLP), and reaction time) were assessed under daytime, nighttime, and nighttime with glare conditions. Pearson correlations determined the associations between visual function and driving performance across the 3 lighting conditions. Of the 20 drivers included, the average age was 70.3 years; 55% were male. Poor photopic VA was significantly correlated with greater SDspeed (r=0.26; P<.001) and greater SDLP (r=0.31; P<.001). Poor photopic AULCSF was correlated with greater SDLP (r=-0.22; P=.01). Poor mesopic VUSFVM was significantly correlated with slower average speed (r=-0.24; P=.007), larger SDspeed (r=-0.19; P=.04), greater SDLP (r=-0.22; P=.007), and longer reaction times (r=-0.22; P=.04) while driving at night. For functional vision in the mesopic condition with glare, poor VA was significantly correlated with longer reaction times (r=0.21; P=.046) while driving at night with glare; poor AULCSF was significantly correlated with slower speed (r=-0.32; P<.001), greater SDLP (r=-0.26; P=.001) and longer reaction times (r=-0.2; P=.04) while driving at night with glare. No other significant correlations were observed between visual function and driving performance under the same lighting conditions. Visual functions differentially affect driving performance in different lighting conditions among older drivers, with more substantial impacts on driving during nighttime, especially in glare. Additional research with larger sample sizes is needed to confirm these results.

Comparative Sensitivity Analysis on Intersection Crash Prediction Models by Control Type: Highway Safety Manual Approach

A comprehensive, in-depth investigation into the comparative safety of different intersection control types is presented, specifically signalized and minor road stop-controlled intersections across facility types. Employing a rigorous sensitivity analysis, the study scrutinizes intersection crash prediction models (CPMs) rooted in the Highway Safety Manual (HSM) methodology. Through meticulous data analyses and thorough comparisons of predictive models across a range of major and minor road traffic volumes, the paper critically examines the portrayal of signalized and stop-controlled intersections within the default HSM Part C CPMs. Additionally, the study delves into the effects of certain crash modification factors on CPMs, further exploring the impacts of calibration factors and locally developed models on crash prediction results. CPMs for signalized intersections do not uniformly lead to fewer crashes compared with stop-controlled intersections, especially under certain traffic volumes and when considering fatal and injury crashes. Therefore, the choice of intersection control should be based on more nuanced criteria than traffic volume alone. Additionally, the research emphasizes the role of intersection geometry and operations, for example, the benefits of adding turn lanes and varying left-turn phasing, in influencing safety outcomes. This study significantly contributes to the understanding of intersection safety and HSM modeling approaches, providing a valuable resource for transportation professionals dedicated to advancing intersection safety. Examining strengths and limitations of existing models provides valuable input for researchers developing future intersection CPMs, and offers essential insights for transportation planners and engineers, informing decision-making for optimizing intersection control strategies and enhancing overall intersection safety and traffic management.

Alcohol is a risk factor for helmet non-use and fatalities in off-road vehicle and motorcycle crashes.

Off-road vehicle (ORV) and motorcycle use is common in Canada; however, risk of serious injury is heightened when these vehicles are operated without helmets and under the influence of alcohol. This study evaluated the impact of alcohol intoxication on helmet non-use and mortality among ORV and motorcycle crashes. Using data collected from the Nova Scotia Trauma Registry, a retrospective analysis (2002-2017) of ORV and motorcycle crashes resulting in major traumatic brain injury was performed. Patients were grouped by blood alcohol concentration (BAC) as negative (< 2 mmol/L), legally intoxicated (2-17.3 mmol/L) or criminally intoxicated (> 17.3 mmol/L). Logistic regression models were constructed to test for helmet non-use and mortality. A total of 424 trauma patients were included in the analysis (220 ORV, 204 motorcycle). Less than half (45%) of patients involved in ORV crashes were wearing helmets and 65% were criminally intoxicated. Most patients involved in motorcycle crashes were helmeted at time of injury (88.7%) and 18% were criminally intoxicated. Those with criminal levels of intoxication had 3.7 times the odds of being unhelmeted and were 3 times more likely to die prehospital compared to BAC negative patients. There were significantly increased odds of in-hospital mortality among those with both legal (OR = 5.63), and criminal intoxication levels (OR = 4.97) compared to patients who were BAC negative. Alcohol intoxication is more frequently observed in ORV versus motorcycle crashes. Criminal intoxication is associated with helmet non-use. Any level of intoxication is a predictor of increased in-hospital mortality.

Fatal crashes involving vehicles with driver warning systems: Identifying risk factors using a correlated random parameters ordered logit modeling approach

Recent advancements in vehicular technology are expected to enhance traffic safety by either warning the drivers or by automating the tasks related to driving to reduce the human driver's involvement. The driver warning systems (DWSs) are designed to warn drivers in unsafe situations such as forward collision, lane departure, or when changing lanes with vehicles in blind spot areas. Although these features are designed to enhance safety, recent crash data shows vehicles with these features are still getting involved in crashes, making it necessary to identify the contributing factors. Further, it also requires research to quantify the benefits of vehicles with one or multiple DWS in terms of safety during multivehicle crashes. This study presents a methodological framework to compare factors affecting fatal crashes involving vehicles with no, one and two DWSs. A three-step method is proposed to incorporate unobserved heterogeneity while modeling. Fixed parameter and correlated random parameter order logit models are employed. The results shows that correlated random parameters ordered logit model outperforms traditional fixed parameter ordered logit model. Vehicles equipped with DWSs are safer than vehicles without DWSs during wet or snowy road conditions, when the vehicle skids laterally or longitudinally, and at intersections. Vehicles with one or both DWSs can reduce drink-and-drive and speeding-related crash involvement than vehicles without DWSs. Female and elderly drivers are at a higher risk while driving a vehicle with one or both DWSs compared to driving a vehicle without DWSs, demanding vehicular modifications.

An In-Depth Analysis of Road Fatal Crash Patterns and Discussions in Ho Chi Minh City

An In-Depth Analysis of Road Fatal Crash Patterns and Discussions in Ho Chi Minh City

A novel generative adversarial network for improving crash severity modeling with imbalanced data

Traffic crash data is often greatly imbalanced with the majority of non-fatal crashes and only a small number of fatal crashes. Such data imbalance issue poses a challenge for crash severity modelling, especially for classifying and interpreting fatal crashes with very limited samples. To address the data imbalance issues, the data resampling techniques are commonly used methods to rebalance the number of samples among all categories of the dataset, such as under-sampling and over-sampling techniques. However, it is challenging for most traditional and existing deep learning-based resampling methods, e.g., synthetic minority oversampling technique (SMOTE) and Generative Adversarial Networks (GAN), to handle both continuous and discrete risk factors in traffic crash datasets, since they are built upon by smooth and continuous functions which are not applicable for processing discrete variables. Though some resampling methods are capable of handling both continuous and discrete variables, they may struggle with mode collapse issues associated with sparse discrete risk factors so that the diversity of the underlying data distribution can not be captured due to oversampling repetitive and similar samples. To address the aforementioned issues, the current study proposes a traffic crash data generation method based on the Conditional Tabular GAN (CTGAN) to rebalance crash datasets for improving performance of crash severity classification and interpretation. The designed experiments are conducted to evaluate contributions of the synthetic data for improving crash severity classification, the distribution consistency between synthetic and benchmark datasets, and the parameter recovery (i.e., the accuracy of parameter estimation and probability prediction) for various resampling strategies. A 4-year real-world dataset collected in Washington State, U.S., and Monte Carlo simulations are utilized for demonstrating the designed experiments. The results indicate that crash severity modeling using synthetic data generated by the mix-resampling of CTGAN and random under-sampling (CTGAN-RU) outperforms all baseline methods. In addition, the proposed deep generative method demonstrates the capability in maintaining distribution consistency and achieving accurate parameter recovery. This study can provide valuable insights for traffic safety researchers and engineers into crash severity modeling, especially when handling imbalanced crash data of various types.

Analysis of Traffic Injury Crash Proportions Using Geographically Weighted Beta Regression

The classical linear regression model allows for a continuous quantitative variable to be modeled simply from other variables. However, this model assumes independence between observations, which, if ignored, can lead to methodological issues. Additionally, not all data follow a normal distribution, prompting the need for alternative modeling methods. In this context, geographically weighted beta regression (GWBR) incorporates spatial dependence into the modeling process and analyzes rates or proportions using the beta distribution. In this study, GWBR was applied to the traffic injury (fatal and non-fatal) crash proportions in Fortaleza, Ceará, Brazil, from 2009 to 2011. The results demonstrated that the local approach using the beta distribution is a viable model for explaining the traffic injury crash proportions, due to its flexibility in handling both symmetric and skewed distributions. Therefore, when analyzing rates or proportions, the use of the GWBR model is recommended.

Generation of nighttime pedestrian fatal precrash scenarios at junctions in Tamil Nadu, India, using cluster correspondence analysis

Objective Modern transportation amenities and lifestyles have changed people’s behavioral patterns while using the road, specifically at nighttime. Pedestrian and driver maneuver behaviors change based on their exposure to the environment. Pedestrians are more vulnerable to fatal injuries at junctions due to increased conflict points with vehicles. Generation of precrash scenarios allows drivers and pedestrians to understand errors on the road during driver maneuvering and pedestrian walking/crossing. This study aims to generate precrash scenarios using comprehensive nighttime fatal pedestrian crashes at junctions in Tamil Nadu, India. Methods Though numerous studies were available on identifying pedestrian crash patterns, only some focused on identifying crash patterns at junctions at night. We used cluster correspondence analysis (CCA) to address this research gap to identify the patterns in nighttime pedestrian fatal crashes at junctions. Further, high-risk precrash scenarios were generated based on the positive residual means available in each cluster. This study used crash data from the Road Accident Database Management System of Tamil Nadu State in India from 2009 to 2018. Characteristics of pedestrians, drivers, vehicles, crashes, light, and roads were input to the CCA to find optimal clusters using the average silhouette width, Calinski-Harabasz measure, and objective values. Results CCA found 4 clusters with 2 dimensions as optimal clusters, with an objective value of 3.3618 and a valence criteria ratio of 80.03%. Results from the analysis distinctly clustered the pedestrian precrash behaviors: Clusters 1 and 2 on pedestrian walking behaviors and clusters 3 and 4 on crossing behaviors. Moreover, a hidden pattern was observed in cluster 4, such as transgender drivers involved in fatal pedestrian crashes at junctions at night. Conclusion The generated precrash scenarios may be used to train drivers (novice and inexperienced for nighttime driving), test scenario creation for developing advanced driver/rider assistance systems, hypothesis creation for researchers, and planning of effective strategic interventions for engineers and policymakers to change pedestrian and driver behaviors toward sustainable safety on Indian roads.

Assessing the effect of automated speed enforcement and comprehensive measures on road safety in Rwanda

Objectives Daily, approximately 3,400 traffic-related deaths occur globally, with over 90% concentrated in low and middle-income countries (LMICs). Notably, Rwanda has one of the highest road traffic death rates in the world (29.7 per 100,000 people) and is the first low-income country to implement a national Automated Speed Enforcement (ASE) policy. The primary goal of this study is to evaluate the effectiveness of ASE cameras in reducing the primary outcome of road traffic deaths and secondary outcomes of serious injury crashes and fatal crashes. Methods The study used data on road traffic deaths, and serious injury and fatal crashes collected by the Rwanda National Police between 2010 and 2022. Interrupted time series (ITS) models were fit to quantify the association between ASE and change in road traffic crash outcomes, adjusted for COVID-19-related variables (such as the start of the pandemic, the closure of schools and bars), along with exposure variables (such as GDP and population), and other concurrent road safety measures (such as road safety campaigns). Results The ITS models show that the implementation of ASE cameras significantly reduced road traffic deaths, serious injury crashes, and fatal crashes at the provincial level. For instance, the implementation of ASE cameras in the whole of Rwanda in April 2021 was significantly associated with a 0.14 (95% CI [0.072, 0.212]) reduction in monthly death incidence, equating to a 38.16% monthly decrease compared to the period before their installation (January 2010-March 2021). Conclusion This study emphasizes the significant association of ASE in Rwanda with improved road traffic crash outcomes, a result that may inform road safety policy in other LMICs. Rwanda has become the first low-income country to implement nationwide scaling of ASE in Africa, paving the way for the generation of valuable evidence on speed-related interventions. In addition to new knowledge generation, African road safety research efforts like this one are opportunities to grow academic and law enforcement cooperations while improving data systems and sources for future research benefits.

Impact of COVID-19 on road crashes in Thailand

The main goal of this study is to investigate the impact of COVID-19 on road crashes in Thailand using time series and interrupted time series analysis. To achieve the goal, road crash data from the Department of Highway (DOH), which includes total crashes, single vehicle crashes, fatalities, fatal crashes, speeding crashes, and drunk driving crashes, was obtained to conduct Seasonal Autoregressive Integrated Moving Average (SARIMA) time series models and Interrupted Time Series (ITS) models. SARIMA models were applied to forecast the number of crashes in the absence of COVID-19 then compare them to the observed values to identify the difference. The impact of a policy change aimed at addressing the spread of COVID-19 was assessed using ITS models on a time series accident dataset. The goal was to ascertain if the intervention had a meaningful and causative impact on the outcome. The result showed that the first wave of COVID-19 caused a significant reduction in all road crash indicators instead of skyrocketing to a peak. After releasing the lockdown measures from the first wave of spreading, an increase was found in all of the crash indicators as well. However, the third wave of COVID-19, which lasted longest for nearly 7 months, also caused a decrease in the number of crashes, but not as much as the first wave of the outbreak. Moreover, the result from the interrupted time series also revealed that curfews and the closure of entertainment places are associated with a significant decrease in the number of speeding crashes and drunk driving crashes from 10 p.m. to 4 a.m., respectively. It can be observed that the COVID-19 countermeasures, such as curfews and bans on the sales of alcoholic beverages, led to a drop in the number of speeding and drunk driving crashes.

Determinants of Road Traffic Crash Fatalities: A Case of Road Accidents in India, 2004–2018

The purpose of this study is to determine the factors that influence deaths in road accidents in India. The study uses panel econometric methodologies such as the fixed effects model, random effects model, and Hausman–Taylor instrumental variable technique to achieve this goal. Unobserved heterogeneity between states/union territories (UTs) and time is accounted for by time-fixed and state-fixed effects. Based on the panel data set from Indian states/UTs for the period 2004–2018, the study confirms previous results that motorisation, population, urbanisation and income level are all potential contributors to road accidents and fatalities. It also shows that spending on health care and law enforcement appears to help the Indian states minimise mortality in road traffic accidents. On the one hand, spending on health care facilities can provide health-related assistance to persons who have been involved in traffic accidents; on the other hand, spending on police increases the enforcement of rules, resulting in a reduction in traffic accidents. Thus, to lessen the economic and financial cost of road accidents in the country, the government should focus on boosting both per capita health expenditure and per capita police expenditure. JEL Codes: C23, C26, H51, I18, L92, O18, R41, R42

Effects of state opioid prescribing laws on rates of fatal crashes in the USA

BackgroundState opioid prescribing cap laws, mandatory prescription drug monitoring programme query or enrolment laws and pill mill laws have been implemented across US states to curb high-risk opioid prescribing. Previous...

Investigating and modeling motorcycle crash rates at urban unsignalized intersections in India

Objective The present study focuses on understanding the behavior of motorized 2-wheeler (MTW) riders at urban unsignalized intersections in India. In the Indian context, over 60% of road crash fatalities are attributed to vulnerable road users, with MTWs serving as the predominant contributors, accounting for 44% of total fatalities. Notably, unsignalized intersections have emerged as critical sites for accidents involving vulnerable road users. Methods Postencroachment time is used to assess traffic conflicts of MTW users. Furthermore, the study employs the exceedance property of extreme value theory to calculate crash probabilities. Tobit and grouped random parameters Tobit regression models are developed to model crash probabilities, incorporating variables such as traffic volume, traffic composition, gap acceptance time, intersection characteristics, and intersection conflict area at 4 urban unsignalized intersections in Surat, India. Results MTW riders have the lowest gap acceptance time among vehicles in the traffic stream. Cars and other heavy vehicles readily accept gaps when MTWs are in the conflicting stream at unsignalized intersections, which increases traffic conflicts. MTWs have the highest crash rates in the traffic stream. Among the developed models, the grouped random parameters Tobit regression captures the spatial unobserved heterogeneity of the study sites and outperforms the simple Tobit regression model. The results also indicate that MTW riders are exposed to a higher risk of crashes while turning at unsignalized intersections. The presence of a central traffic island has varied implications; it raises crash rates at 3-legged intersections but lowers them at 4-legged intersections for 2-wheelers. Conclusion The study concludes that MTW crash rates are influenced by traffic and intersectional factors. Increased gap acceptance time correlates with lower crash rates. Countermeasure selections require detailed investigations, because it was observed that the presence of central traffic islands has varied effects on crash rates at 3-legged and 4-legged unsignalized intersections.

The utility of telematics data for estimating the prevalence of driver handheld cellphone use, 2019–2022

Introduction: Driver distraction from handheld cellphone use contributes to fatal crashes every year but is underreported in terms of the proportion of crashes attributed to any distraction or cellphone use specifically. Existing methods to estimate the prevalence of cellphone distractions are also limited (e.g., observing drivers stopped at intersections, when crash risk is low). Our study used data from Cambridge Mobile Telematics to estimate the prevalence of drivers’ handheld calls and cellphone manipulation while driving, with “cellphone motion” based on movement recorded by the phones’ gyroscopes used as a surrogate for manipulation. Method: We compared the telematics measures with the National Highway Traffic Safety Administration’s roadside observations of driver electronic device use, and logistic regression tested relationships between regional, legislative, and temporal factors and the odds of cellphone behaviors occurring on a trip or at a given point in time. Results: Results showed 3.5% of trips included at least one handheld phone call and 33.3% included at least an instance of cellphone motion, with handheld calls occurring during 0.78% of overall trip duration and cellphone motion during 2.4% of trip duration. Conclusions: Correspondence between trends in cellphone distractions across regional, legislative, and temporal factors suggest telematics data have considerable utility and appear to complement existing datasets.

Evaluating Road Crash Severity Prediction with Balanced Ensemble Models

This study evaluates the performance of an ensemble of five ML models (Random Forest, XGBoost, AdaBoost, LightGBM and CatBoost) on crash data from New South Wales, Australia. The model is evaluated based on ROC-AUC score, with a result of 0.68, indicating a moderate level of predictive accuracy. Feature importance analysis reveals the key predictors being the vehicle type involved, with sedans/hatchbacks and motorcycles being the most common in fatal crashes, and the collision type, with vehicle-to-object impacts often leading to fatalities. Furthermore, fatal crashes occur more on Saturdays, in country non-urban LGAs and speed limits of 100 km/h as the most usual settings for fatal accidents.

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.

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.