Crash Database Research Articles

Traffic collisions and micromobility: A comparison between personal mobility devices and bicycles based on police reports

Introduction: The recent increase in the use of bicycles and personal mobility devices (PMDs), including mostly E-scooters, is associated with a rapid rise in injuries. Understanding the main crash scenarios leading to these injuries is essential to evaluate and improve preventive and protective measures, especially for PMDs, which are often equated with bicycles. The objective of this study is to identify and compare the most common two-party collision scenarios for bicycles and PMDs, and to identify factors affecting injury severity. Method: Crashes involving at least one PMD or one bicycle and another road user were analyzed from the 2019–2022 French police-reported road crashes database. We investigated the rider, the other vehicle, the road, and the crash scenarios characteristics (pre-crash maneuvers, impact zone on vehicles) and their joint effect on injury severity (hospitalization or fatality: yes/no). Results: We included 16,302 bicycle crashes and 4,118 PMD crashes in the analysis. Most of these collisions (75%) were against a car. The most frequent and the most severe collision scenario was the side-on-head for both bicycles (51%) and PMDs (58%); 67% of both bicycles and PMDs were going straight before the collision. Main factors associated with increased injury severity included colliding with a greater size vehicle, age above 50, and riding on roads with a higher speed limit. Bicycles remained at a higher risk of severe injury than PMDs after accounting for adjustment factors. Conclusions: Although collision scenarios appear similar for bicycles and PMDs, differences in other crash characteristics and injury severity suggest that these two modes of transportation should not be equated in crash investigations. Practical implications: These findings emphasize the need to primarily investigate side-on-head collisions with a moving car for both PMDs and bicycles in order to develop, evaluate, and improve protective devices to reduce the risk of injuries.

Comprehensive analysis of traumatic rupture of the aorta in road traffic crashes: incorporating epidemiological insights and K-prototype clustering

Objectives The identification of crash characteristics associated with traumatic rupture of the aorta (TRA) can significantly enhance countermeasures against TRA. Conventional epidemiological approaches struggle to adequately handle the substantial variability of traffic crash data. Consequently, this study aims to integrate conventional epidemiological analysis with data-driven cluster analysis to more comprehensively analyze TRA-related crash characteristics. Methods A total of 350 unweighted TRA crashes were extracted from traffic crash databases including comprehensive crash details and injury descriptions. Initially, a selection was made of 11 continuous variables and 9 categorical variables, describing crash characteristics. After correlation analysis and principal component analysis were applied to the dataset, K-prototype clustering was finally conducted using 6retained categorical variables and 6 principal components derived from the continuous variables. Results This study found significant age and gender disparities among TRA victims, with 50% falling within the age range of 25–59 years and an overwhelming majority (62.2%) being males. Side impacts emerged as the primary cause of TRA-related crashes (37.2%), followed by collisions with off-road objects (28.6%) and head-on collisions (24.8%). Cluster analyses revealed 6 distinct clusters within the TRA-related crash dataset. These clusters were characterized by factors such as vehicle model year, curb weight, collision dynamics, and seatbelt usage, providing a deeper understanding of the heterogeneity in TRA incidents and their associated factors. Conclusions Although limitations related to available data sources and factors such as accompanying injuries and vehicle weight warrant further comprehensive investigations in the future, this study contributes valuable insights into TRA analysis to enhance understanding and prevention strategies.

Estimating occupation-related crashes in light and medium size vehicles in Kentucky: A text mining and data linkage approach

Occupational motor vehicle (OMV) crashes are a leading cause of occupation-related injury and fatality in the United States. Statewide crash databases provide a good source for identifying crashes involving large commercial vehicles but are less optimal for identifying OMV crashes involving light or medium vehicles. This has led to an underestimation of OMV crash counts across states and an incomplete picture of the magnitude of the problem. The goal of this study was to develop and pilot a systematic process for identifying OMV crashes in light and medium vehicles using both state crash and health-related surveillance databases. A two-fold process was developed that included: 1) a machine learning approach for mining crash narratives and 2) a deterministic data linkage effort with crash state data and workers compensation (WC) claims records and emergency medical service (EMS) data, independently. Overall, the combined process identified 5,302 OMV crashes in light and medium vehicles within one year’s worth of crash data. Findings suggest the inclusion of multi-method approaches and multiple data sources can be implemented and used to improve OMV crash surveillance in the United States.

An evaluation of front seat distance from rear facing child restraint systems in prevention of injury in frontal crash tests

Objectives Elevated head injury incidence in infants compared to toddlers involved as occupants in motor vehicle crashes has been demonstrated in multiple population-representative crash databases. Further, experimental studies have revealed a potential injury mechanism via impact between a rear-facing, CRS-restrained child and the back of the vehicle seat or console on the row in front of the CRS. Subsequently, experimental studies have suggested that bracing the CRS against the seat immediately in front of the CRS could mitigate head injury, but also indicated that more research was necessary. Thus, we investigated the effect of bracing against the front seat, as well as distance from the front seat with rear-facing infant carriers and rear-facing convertibles, with a focus on changes to measured head, neck and chest injury metrics in rear facing CRSs. Further, we examined the effect of using the infant carrier with and without a base on these injury metrics. Methods 34 frontal sled tests at 30 or 35 mph were conducted using a simulated rear-row vehicle seat and structure representing the front seatback. A Q1.5 anthropomorphic test device (ATD) was placed in a single make/model LATCH-affixed rear-facing convertible or single make/model infant carrier; infant carrier without base was affixed with lap and shoulder belt. To evaluate the effect of bracing and distance, tests were conducted with a 300, 140, 70, or 15 mm gap between the CRS seatback and the front seatback, or a touching (0 mm) or braced (-20 mm) condition. Bayesian regression models quantified the effects of various predictors and model uncertainty. Results For tests with the convertible CRS, no head contact was observed between the head and the front vehicle seatback. For the infant carrier, head contact occurred at both 70 and 140 mm distances but not the other distances. On average, the −20, 0, or 15 mm distances yielded a 60% reduction in head injury criterion with 15 millisecond window (HIC15), and a 60% to 80% reduction in neck tension, compared to the 70 and 140 mm distances; chest acceleration also decreased for the convertible seat only. In the case of both carriers and convertibles, each mm of distance the CRS moves away from the front seatback up to 70 mm, adds 5.3 HIC15 points (95% Credible Interval (CrI):[4.6, 6.2]), and 3.5 Newtons (95% CrI: [2.2, 4.8]) of neck tension, on average. Conclusions Placing a rear facing CRS, both convertibles and infant carriers, against or close to the seatback of the seat immediately in front of the CRS reduces head and tensile neck injury criteria in ATDs. The amount of gap between the front seat and the rear facing CRS is strongly and positively correlated with HIC for both convertibles and infant carriers. RF infant carriers with and without a base yield comparable injury metrics and kinematics when touching or nearly touching the back of the front vehicle seatback.

The association between county ordinances allowing off-road vehicles on public roads and crash rates

BackgroundLegislative bodies across the country have increasingly allowed off-road vehicles (ORVs) including all-terrain vehicles (ATVs) and utility task vehicles (UTVs) on public roads, an environment for which they are not designed. In 2004, Iowa gave individual counties the discretion to pass ordinances allowing ORVs on public roadways. The objective of this study was to evaluate the relationship between the passage of ORV ordinances and ORV crash rates, especially on public roads.MethodsAn Iowa ORV roadway ordinance database and an Iowa ORV crash database (2002–2018) for all 99 counties were compiled. Crashes for which county location could not be determined were excluded. Utilizing a zero-inflated Poisson model, correcting for background crash frequency trends and population, investigators compared the relative rates of crashes after ordinance passage to time points before ordinance implementation and to counties without such ordinances. Sub-analyses, including that focused on more recent years (2008–2018), were also performed.ResultsForty-five county ORV roadway ordinances went into effect between 2011 and 2018 and 2,347 crashes (69%) met inclusion criteria. Adjusted for year, there was a 58% greater ORV crash rate in counties after passing an ORV roadway ordinance (incidence rate ratio (IRR) 1.58, 95% CI 1.32–1.90). Roadway crashes (n = 834) increased 48% after ordinance passage (IRR 1.48, 95% CI 1.14–1.94). This roadway crash association remained statistically significant when analysis was limited to the years 2008–2018 (IRR 1.39, CI 1.06–1.83, n = 544); to ATV crashes only (IRR 1.70, CI 1.20–2.40, n = 683); and to ATV crashes excluding counties with UTV-only ordinances (IRR 1.74, CI 1.40–2.15, n = 2,011).ConclusionsORV roadway and total crashes increased significantly after implementation of county ordinances allowing ORVs on public roadways and when compared to counties without such ordinances. It is likely that these increased crashes have resulted in more injuries and possibly deaths. Results from this study may help inform policymakers as they consider legislation regarding ORV usage on public roads.

Evaluation of comfort zone boundary based automated emergency braking algorithms for car‐to‐powered‐two‐wheeler crashes in China

AbstractCrashes between cars and powered two‐wheelers (PTWs: motorcycles, scooters, and e‐bikes) are a safety concern; as a result, developing car safety systems that protect PTW riders is essential. While the pre‐crash protection system automated emergency braking (AEB) has been shown to avoid and mitigate injuries for car‐to‐car, car‐to‐cyclist, and car‐to‐pedestrian crashes, much is still unknown about its effectiveness in car‐to‐PTW crashes. Further, the characteristics of the crashes that remain after the introduction of such systems in traffic are also largely unknown. This study estimates the crash avoidance and injury risk reduction performance of six different PTW‐AEB algorithms that were virtually applied to reconstructed car‐to‐PTW pre‐crash kinematics extracted from a Chinese in‐depth crash database. Five of the algorithms include combinations of drivers’ and PTW riders’ comfort zone boundaries for braking and steering, while the sixth is a traditional AEB. Results show that the average safety performance of the algorithms using only the driver's comfort zone boundaries is higher than that of the traditional AEB algorithm. All algorithms resulted in similar distributions of impact speed and impact locations, which means that in‐crash protection systems likely can be made less complex, not having to consider differences in AEB algorithm design among car manufacturers.

Advancing road safety strategy development: A data-driven multi-objective optimisation integrated approach

Road traffic accidents pose a significant global health concern, with an alarming 1.19 million fatalities reported in 2021. Traditionally, strategies to address this challenge have relied on expert input and subjective evaluations. This study introduces an adaptive and novel two-stage model that minimizes expert interference by integrating multi-objective optimisation with association rule mining. This innovative approach provides a systematic framework to enhance the efficiency of decision-making and optimise resource allocation outcomes in road infrastructure management, facilitating adaptability to diverse objectives. A case study in Utrecht from iRAP validates the efficiency of the approach, demonstrating significant improvements across various objectives by using the non-dominant sorting genetic algorithm and enabling road local authorities to tailor investment plans to their specific road network characteristics by crash database mining. However, the methodology requires refinement, particularly in identifying risk levels considering the interactive effects of multiple road attributes. In conclusion, while representing a substantial advancement, further refinement is necessary to fully realize its potential in enhancing road safety.

Road Safety Evaluation during the COVID-19 Pandemic: An Exploratory Analysis on Brazilian Federal Highways

Because of the increase in the number of cases of COIVD-19, government authorities needed to contain the spread and implemented several measures, including restricting hours of operation and social distancing measures, such as isolation. This leads to a considerable decrease in urban mobility and, consequently, to an effect on road safety. Therefore, the problem question arises: What is the impact of the COVID-19 pandemic on road safety on Brazilian federal highways? Thus, the objective of this paper is to evaluate the safety trends during the COVID-19 pandemic in 2020. This paper used the crash database from the Brazilian Federal Highway Police (PRF), was analyzed, which enabled identifying changes in crash occurrence in 2020 when compared to the previous years (2011 - 2019). A decrease in the number of crashes on federal highways was observed, which might not have a direct relationship with the COVID-19 pandemic.

Does abdominal protrusion in pregnant women affect abdominal injury severity in motor vehicle collisions? A nationwide database study.

Some pregnant women avoid vehicle driving owing to the risk of contact between their protruding abdomen and steering wheel. This study was performed to determine whether abdominal protrusion in late-term pregnant car users affects the occurrence and severity of abdominal injuries in motor vehicle collisions using a national crash database. The National Automotive Sampling System/Crashworthiness Data System was used to analyse maternal background, collision characteristics, outcome and Abbreviated Injury Scale (AIS) scores for the body regions of all persons involved in the collision. Comparison of pregnant and non-pregnant women in the driver's seat showed no significant differences in the rate of AIS scores of ≥2 (2+) for abdominal injuries and female outcomes. Comparison of use of the driver's seat and front passenger's seat by pregnant women showed no significant difference in rate of AIS 2+ injuries or in maternal and fetal outcomes. Comparison of pregnant women with a gestational age of ≤27 and >27 weeks in the driver's seat showed no significant differences in rate of AIS 2+ injuries or in maternal and fetal outcomes. Based on the data from relatively low-speed frontal collisions, obstetrician/gynaecologists should advise pregnant women that they do not need to change their preferred car seat from the driver's seat to another seat because of fear of contact between their protruding abdomen and the vehicle interior.

PODE: privacy-enhanced distributed federated learning approach for origin-destination estimation

The statewide consumer transportation demand model analyzes consumers’ transportation needs and preferences within a particular state. It involves collecting and analyzing data on travel behavior, such as trip purpose, mode choice, and travel patterns, and using this information to create models that predict future travel demand. Naturalistic research, crash databases, and driving simulations have all contributed to our knowledge of how modifications to vehicle design affect road safety. This study proposes an approach named PODE that utilizes federated learning (FL) to train the deep neural network to predict the truck destination state, and in the context of origin-destination (OD) estimation, sensitive individual location information is preserved as the model is trained locally on each device. FL allows the training of our DL model across decentralized devices or servers without exchanging raw data. The primary components of this study are a customized deep neural network based on federated learning, with two clients and a server, and the key preprocessing procedures. We reduce the number of target labels from 51 to 11 for efficient learning. The proposed methodology employs two clients and one-server architecture, where the two clients train their local models using their respective data and send the model updates to the server. The server aggregates the updates and returns the global model to the clients. This architecture helps reduce the server’s computational burden and allows for distributed training. Results reveal that the PODE achieves an accuracy of 93.20% on the server side.

Pre-crash scenarios for safety testing of autonomous vehicles: A clustering method for in-depth crash data

Autonomous vehicles (AVs) provide an opportunity to enhance traffic safety. However, AVs market penetration is still restricted due to their safety concerns and dependability. For widespread adoption, it is crucial to thoroughly assess the safety response of AVs in various high-risk scenarios. To achieve this objective, a clustering method was used to construct typical testing scenarios based on the China In-depth Mobility Safety Study-Traffic Accident (CIMSS-TA) database. Initially, 222 car-to-powered two-wheelers (PTWs) crashes and 180 car-to-car crashes were reconstructed from CIMSS-TA database. Second, six variables were extracted and analyzed, including the motion of the two vehicles involved, relative movement, lighting condition, road condition, and visual obstruction. Third, these variables were clustered using the k-medoids algorithm, identifying five typical pre-crash scenarios for car-to-PTWs and seven for car-to-car. Additionally, we extracted the velocities and surrounding environmental information of the crash-involved parties to enrich the scenario description. The approach used in this study used in-depth case review and thus provided more insightful information for identifying and quantifying representative high-risk scenarios than prior studies that analyzed overall descriptive variables from Chinese crash databases. Furthermore, it is crucial to separately test car-to-car scenarios and car-to-PTWs scenarios due to their distinct motion characteristics, which significantly affect the resulting typical scenarios.

Implementation of a realistic artificial data generator for crash data generation

In this paper, a framework is outlined to generate realistic artificial data (RAD) as a tool for comparing different models developed for safety analysis. The primary focus of transportation safety analysis is on identifying and quantifying the influence of factors contributing to traffic crash occurrence and its consequences. The current framework of comparing model structures using only observed data has limitations. With observed data, it is not possible to know how well the models mimic the true relationship between the dependent and independent variables. Further, real datasets do not allow researchers to evaluate the model performance for different levels of complexity of the dataset. RAD offers an innovative framework to address these limitations. Hence, we propose a RAD generation framework embedded with heterogeneous causal structures that generates crash data by considering crash occurrence as a trip level event impacted by trip level factors, demographics, roadway and vehicle attributes. Within our RAD generator we employ three specific modules: (a) disaggregate trip information generation, (b) crash data generation and (c) crash data aggregation. For disaggregate trip information generation, we employ a daily activity-travel realization for an urban region generated from an established activity-based model for the Chicago region. We use this data of more than 2 million daily trips to generate a subset of trips with crash data. For trips with crashes crash location, crash type, driver/vehicle characteristics, and crash severity. The daily RAD generation process is repeated for generating crash records at yearly or multi-year resolution. The crash databases generated can be employed to compare frequency models, severity models, crash type and various other dimensions by facility type – possibly establishing a universal benchmarking system for alternative model frameworks in safety literature.

The effect of opioid use on traffic fatalities.

We use a difference-in-differences design to study the effect of opioid use on traffic fatalities. Following Alpert etal., we focus on the 1996 introduction and marketing of OxyContin, and we examine its long-term impacts on traffic fatalities involving Schedule II drugs or heroin. Based on the national fatal vehicle crash database, we find that the states heavily targeted by the initial marketing of OxyContin (i.e., non-triplicate states) experienced 2.4 times more traffic fatalities (1.6 additional deaths per million individuals) involving Schedule II drugs or heroin during 2011-2019, when overdose deaths from heroin and fentanyl became more prominent. We find no difference in traffic fatalities until after the mid-2000s between states with and without a triplicate prescription program. The effect is mainly concentrated in fatal crashes with drug involvement of drivers ages between 25 and 44. Our results highlight additional long-term detrimental consequences of the introduction and marketing of OxyContin.

Severity Analysis of Secondary Crashes on High-Speed Roadways: Pattern Recognition Using Association Rule Mining

Secondary crashes (SCs) are a major concern, posing additional safety threats to both non-involved vehicles and incident responders. The objective of this study was to identify the affiliated factors contributing to SCs on roadways with a speed limit of 55 mph or above. Traditional police-investigated crash dataset was analyzed, spanning more than four years (January 2016–February 2020) for the entire state of Alabama. As the crash database did not directly include information on SCs and did not allow for linking a primary crash with a subsequent SC, a data extraction process was developed to identify SCs and understand their characteristics. Association rule mining (ARM) was applied to identify crash patterns based on maximum injury severity levels. The generated rules were filtered based on support, confidence, and lift, and then validated by the lift increase criterion. The results revealed complex relationships between risk factors and severity of SCs. In relation to SCs with injuries, single-vehicle crashes were frequently observed during peak hours and when drivers swerved to avoid objects/persons/vehicles. In contrast, concerning SCs with possible/no injuries, single-vehicle collisions were more likely to occur when drivers failed to notice objects/persons/vehicles and were involved in speeding. On urban interstates, single-vehicle SCs were frequently associated with injuries, while rear-end SCs were often linked to possible/no injuries. The findings of this study can be helpful in enhancing existing traffic incident management programs to mitigate the occurrence of SCs.

A methodological procedure for evaluating curve-related misclassifications in motor vehicle crash databases

Motor vehicle crashes have been identified as a leading cause of death all over the world. To better promote traffic safety and protect lives of the traveling public, transportation agencies develop crash databases to effectively manage reportable motor vehicle crashes. Crash reports from law enforcement authorities usually serve as the primary source for motor vehicle crash data. However, unintentional errors in law enforcement crash reports, errors in state-maintained crash databases, and the migration and rearrangement of data could introduce problems of data consistency in crash databases. One such identified data inconsistency is regarding horizontal curves. It can result in misidentifications of curve-related crashes, which can affect safety analysis using these data. This is significant since many studies have related horizontal curves to crash frequency and severity. To solve this problem, this study proposed a methodological procedure for evaluating (i.e., identifying, classifying, and quantifying) curve-related misclassifications in crash databases. The applicability of the proposed methodological procedure was illustrated through a case study using the Crash Records Information System (CRIS) maintained by the Texas Department of Transportation (TxDOT). The results indicated that transportation agencies could employ the proposed methodological procedure to evaluate inconsistencies associated with curve-related crash data both effectively and efficiently.

Towards safer streets: A framework for unveiling pedestrians’ perceived road safety using street view imagery

Road safety has become a global concern but its impact in low- and middle-income countries is widespread mainly due to lack of appropriate crash database system and under-reporting. In this context, the primary objective of this paper is to provide a scalable framework for unveiling pedestrians’ perceived road safety that can also be applied in regions where accessible crash data are limited or near-crashes are left unreported. In the first step of our methodology, a deep learning architecture-based semantic segmentation model (HRNet+OCR) is trained using labeled Google Street View (GSV) images from specific study areas in Dhaka, Bangladesh, which facilitates the identification of both man-made components (such as roads, sidewalks, buildings, and vehicles) and natural elements (including trees and sky). The developed model showed excellent performance in identifying different features in an image by achieving high precision (0.95), recall (0.97), F1-score (0.96), and intersection over union (IoU) (91.86). Secondly, a group of trained raters scored the perceived road safety on an ordinal scale from 0 to 10 (extremely unsafe to extremely safe to walk in terms of road crashes) by assessing the GSV images. Then, several regression models have been used on features extracted from GSV images, and socio-demographic factors (i.e., population density, and relative wealth index) to estimate the perceived road safety, and random forest regression model was found to perform the best. Further, Shapley Additive Explanations (SHAP), a model-agnostic technique has been used for examining feature importance by computing the contribution of each feature to the random forest regression model output. The results show that sidewalk, road, population density, wall, and relative wealth index have higher impact on determining the perceived road safety rating. Additionally, the results of t-tests between the average perceived road safety scores for crash-prone and non crash-prone areas revealed the existence of significant differences. This study also provides perceived road safety rating map on a neighborhood scale, which can be a useful visualization tool for policy-makers and practitioners to identify the road safety deficiencies at specific locations, and formulate appropriate and strategic countermeasures to improve pedestrians’ road safety.

Review on Automated Accident Detection System

Abstract: Major accidents on highways, freeways and local roads can lead to huge social and economic impacts. Minor accidents may be resolved by the passengers themselves and do not require escorting to hospitals whereas major accidents where airbags are deployed require the immediate attention of authorities. Automated Accident Detection System [AADS] is an auto-detection unit system that immediately notifies an Emergency Contact through an alert alarm when someone presses the trigger button present on poles having CCTV cameras in the end vehicle is detected by the system, detailing the location and time of the accident. The idea is that as soon as an accident is detected by the system, the authorities should immediately be notified to prevent further car congestion as well as allow the passengers to be escorted to the hospital within time. A complete system would automatically detect and record traffic conditions associated with accidents such as the time of the accident, video of the accident, and the traffic light signal controller parameters. The basic research required to develop the system is considered. This involves developing methods for processing acoustic signals and recognizing accident events from the background traffic events. A database of vehicle crashes, car braking, construction, and traffic sounds was created. The meal frequency cepstral coefficients were computed as a feature vector for input to the classification system. A neural network was used to classify these features of the crash and non-crash events.

Looking back in the rearview: Insights into Queensland’s rear-end crashes

Objective Rear-end crashes cost the most out of any crash type to the Compulsory Third Party insurance scheme of Queensland. Rear-end crashes are also one of the most common types of crashes, peaking as the most common type of crash in Queensland in 2019. This study investigated the environmental, driver, and vehicle characteristics associated with rear-end collisions in Queensland, Australia. Methods A totle of 367,230 crashes in the Queensland crash database between 2001 and 2021 were used to identify crash trends in Queensland, particularly related to rear-end crashes. For crashes between 2015 and 2021, additional information was gathered and allowed further analysis of factors contributing to rear-end crashes using the quasi-induced exposure method. Two binary classification tree models were used. Model 1 investigated the environmental characteristics that predicted a crash being a rear-end or not. Model 2 investigated the controller and vehicle characteristics that predict a rear-end crash-involved vehicle to be either the striking (at-fault) or struck (not-at-fault) vehicle. Results Model 1 indicated that rear-end crashes were overrepresented in urban areas and major cities and where speed limits exceeded 50 km/h. Model 2 indicated that occupants of struck vehicles (not at-fault) were more likely to be severely injured than the striking (at-fault) vehicles in rear-end crashes. License type and vehicle type also influenced the frequency and severity of rear-end crashes. Conclusion Rear-end crashes can be unjust in that the not-at-fault party is typically more severely injured. Results from the current study suggest that keeping speed limits no higher than 50 km/h could help reduce the severity of rear-end crashes. Increased enforcement of safe following distances could also assist in preventing road trauma as a result of rear-end crashes.

Effects of design consistency measures and roadside hazard types on run-off-road crash severity: Application of random parameters hierarchical ordered probit model

Run-off-road crashes are one of the most significant causes of road deaths worldwide. Given such significant safety concerns, a number of earlier studies examined the critical factors contributing towards run-off-road crash severity outcomes, mostly by using the information compiled in the official crash database. However, the official crash databases are less likely to have detailed information on driver behavior (errors/expectations) and roadway environment (roadway geometry and roadside attributes). This study aims to investigate the effects of design consistency measures on run-off-road crash severity mechanisms by applying a random parameters hierarchical ordered Probit model. This study contributes towards existing safety literature by demonstrating a complementary approach to capturing the effects of driver behavior and heterogeneity in roadway environment on run-off-road crash severity outcome through the composite measures of design consistency indices and cosmopolite measures of roadside hazard type variables. Specifically, 17 different functional forms of design consistency indices are developed to capture the behavioral factors from the road-geometric changes in developing run-off-road crash severity models. Further, in examining the effect of different types of the roadside environment on run-off-road crash severity outcomes, seven roadside hazard type variables are generated as a composite function of roadside object type and clear zone (lateral distance to roadside object). The empirical analysis of this study involves a two-step modelling approach - in the first step, the decision tree algorithm is applied to identify the higher-order interaction among independent variables, and in the second step, crash severity models are developed by employing several econometric approaches. The hybrid models are estimated by employing four econometric frameworks, which include Ordered Probit, Hierarchical Ordered Probit, Random Parameters Ordered Probit, and Random parameters Hierarchical Ordered Probit models. The run-off-road crash severity models are estimated by using crash data collected from the State of Queensland, Australia, for the years 2015 through 2019. Overall, this study reveals the importance of considering the interaction of drivers' behavior, road geometry, and roadside attributes along with other independent variables in developing run-off-road crash severity models.

Patterns in the Use of Emergency Telemedicine Support in the Management of Traumatic Road Crashes.

Objective: The introduction of emergency telemedicine care models is a common theme in health jurisdictions that include rural and remote populations. How the availability of these models influences the way clinicians manage traumatic road crashes is not yet fully understood. This study seeks to compare road crashes where telemedicine was and was not used and to identify any variables that may increase the likelihood of telemedicine usage by treating clinicians. Methods: Road crashes reported in the state Department of Transport and Main Roads (Queensland, Australia) crash database between January 1, 2019, and November 30, 2020 (n = 23,734) were compared to videoconferencing call logs to determine which crashes resulted in treatment that was supported by telemedicine (n = 204). Analysis was performed to examine differences in characteristics related to the crash depending on whether telemedicine support was requested. Results: Road crashes where telemedicine support was requested on average involved more casualties (1.6 vs. 1.41; t(11,287) = -3.26, p < 0.001, relative risk = 1.13). Crashes that occurred in rural settings accounted for most requests for telemedicine (65.68%; X2 = 159.2, p < 0.001) and a greater percentage of crashes in remote locations (3.36% vs. 2.35%; X2 = 256.97, p < 0.001, relative risk = 1.43). The use of telemedicine support for crashes was associated with a 13% increase in the mean number of casualties, compared to crashes where telemedicine support was not used. Conclusion: Telemedicine support is requested by clinicians providing emergency treatment in the management of road crashes that produce more severe injuries, involve multiple casualties, and take place in more rural settings or remote locations.