Exploring correlated effects on crash severity in Korea’s metropolitan areas: A random-parameter with interaction effect approach

Background: Considering the importance of pedestrian traffic crashes and the role of environmental and demographic factors in the severity of these crashes, this article aimed to review the published evidence and synthesize the results of related studies to determine any associations between demographic and environmental factors and the severity of pedestrian-vehicle crashes. Methods: All epidemiological studies published from 1970 to 2019 were searched in international electronic databases (PubMed [Medline], Scopus, Web of Science, Embase, ScienceDirect, and Ovid) and reference lists of the identified articles were also searched. Studies were included if they investigated the severity of pedestrian-vehicle crashes as outcome, measured any environmental and demographic factors for pedestrian-vehicular crashes as exposure, designed observational, and if they were written in all languages. Quality of included studies was evaluated using the strengthening the reporting of observational studies in epidemiology checklist for observational studies. Results: We found 3126 references among which 24 studies were included in this review. All retrieved studies were conducted between 1990 and 2019 and had a cross-sectional design. In most of these studies, the associations between environmental and demographic variables such as vehicle speed or speed limits, pedestrian age, lighting, type of road, type of vehicle, and alcohol intake with the severity of pedestrian traffic crashes were examined. Conclusion: This study showed that few studies were conducted in this area; in fact, most of the studies were carried out in metropolises of developed countries. As a result, studies which provide strong causal inferences by focusing on high-risk groups and a higher level of evidence such as cohort and case-control ones are needed in developing countries.

Objectives Understanding the factors influencing crash severity of autonomous vehicles is important for increasing road safety. This study focuses on a multi-source accident dataset of vehicles equipped with autonomous driving systems to explore the endogenous relationship between manual takeover of autonomous vehicles and the severity of crash, as well as the influencing factors. Methods By screening and summarizing data on autonomous vehicle accidents. We choose self-driving car takeover and crash severity as potential variables to build a structural equation model to explore the influences of crash severity through continuous variable updating and path improvement. We select autonomous vehicle takeover and crash severity as potential variables and designed a structural equation model to explore the factors affecting crash severity through continuous variable updating and path improvement. Meanwhile, we establish a generalized linear logit model to analyze the factors affecting manual takeover. Finally, the intrinsic link between crash severity and manual takeover is discussed through path analysis and comparison of model results. Results Cloudy and rainy weather, left rear of vehicle contact area, and daylight lighting significantly impact manual takeover and crash severity. Specifically, wet road surface, rainy weather, and daylight have relatively more significant effects on takeover in the structural equation model. And takeover, roadway type including non-freeway and intersection can significantly impact crash severity. Additionally, the study demonstrates the endogeneity between crash severity and takeover at the time of autonomous vehicle crash. Conclusions This study analyzes the potential relationships and influencing factors between takeover events of autonomous vehicles and crash severity. It is found that the frequency of takeover events significantly increases when driving in rainy weather and at night. It is suggested that a real-time monitoring module for adverse weather or lighting conditions should be added to the autonomous driving system to provide early warnings and reduce the occurrence of takeover events, thereby enhancing the safety and reliability of autonomous vehicles.

Investigating the uniqueness of crash injury severity in freeway tunnels: A comparative study in Guizhou, China

Understanding the factors that contribute to traffic crashes can help provide a fundamental basis to plan and develop appropriate countermeasures for road safety issues emerging in particular on two-lane rural roads. However, most of the studies have focused on urban roadways and freeway systems, and few studies have investigated the issue of heterogeneity on two-lane rural roads. The purpose of this study is to uncover the risk factors influencing crash severity on two-lane rural roads in China. A sample of 1490 traffic crashes occurring on two-lane rural roads between 2012 and 2017 was collected from the Mouding County Highway Bureau in Yunnan, China. A random-parameter ordered probit model was estimated using these data to capture underlying unobserved characteristics in personal traits, vehicle attributes, roadway conditions, environmental factors, and crash attribute. To better understand the effect of critical factors on crash severity outcome probability, an elasticity analysis was then introduced. The results show that six factors such as driver’s attribution, illegal driving behaviour, access segment, day of week, vehicle type, and crash form have a significant impact on the injury severity, and the impacts of driving behaviours, access segment, and vehicle-fixed object crashes had significant variation across observations. Besides, the correlations between critical factors and the probability of serious injury sustained in traffic crashes are identified and discussed. The local driver indicator has more positive impact on the crash severity than nonlocal driver, and nonaccess segment appears a higher probability of serious or vicious collisions. It is worth mentioning that motorcycle-involved crashes do show an obvious correlation with crash injury severity. As for crash forms, vehicle-vehicle crashes are more likely to lead to severe crash injury. Besides, high-risk driving behaviour (e.g., fatigue driving, speeding, and converse driving), weekends, and holidays are found to have significant contribution to increasing the probability of traffic crash injuries and fatalities on two-lane rural roads.

Systemic analysis is considered an important safety analysis approach that is complementary to the Highway Safety Manual hotspot analysis. The network screening step in systemic analysis is to identify sites with characteristics that are associated with specific types of severe crashes. Traditionally, determining the risk scores involves subjective criteria. This research aims to develop a data-driven approach to replace the subjective methods used in the past. To achieve the research objective, this study collected roadway and crash data from the Connecticut Department of Transportation. A data-driven crash risk factor categorization methodology is proposed to estimate accurately the performance measures indicating crash risks. Moreover, this study proposes and compares four different risk scoring matrices to identify an optimal risk scoring method that is attuned with the principles of the systemic approach to safety as well as providing additional insights on justifying the systemic safety analysis results. The proposed methodology is implemented to conduct network screening for severe roadway departure crashes and later validated using severe aggressive-driving related crashes. Risk-based network screening results indicate that risk scores derived from normalized crash over-representation provide additional emphasis on sites with low traffic volume that are associated with high severe crash counts. The highest modified crash rate was obtained using normalized crash over-representation based risk scores indicating that the proposed network screening methodology can not only identify roadway attributes that are correlated with severe crashes but also account for low-volume roadway sites with severe crashes. The validation analysis indicated proposed method is transferrable to different emphasis area related crashes.

Application of a Crash-predictive Risk Assessment Model to Prioritise Road Safety Investment in Australia

Injury severity and vehicle damage are two of the main indicators of the level of crash severity. Other factors, such as driver characteristics, roadway conditions, highway geometry, environmental factors, vehicle type, and roadside objects, may also be directly or indirectly related to crash severity. All these factors interact in such complicated ways that it is often difficult to identify their interrelationships. The aim of this study was to examine the relationships between these contributors and the severity of single-vehicle crashes. Structural equation modeling (SEM) offers the opportunity to explore the complex relationships between variables by handling endogenous variables and exogenous variables simultaneously. Furthermore, SEM allows latent variables to be included in the model and bridges the gap between dependent and explanatory variables. In this study, the number of latent variables was defined by the understanding of collision force, kinetic energy, and mechanical process of a collision, as well as statistical goodness of fit that was based on available data. Three SEM models (one with one latent variable, one with two, and one with three) representing the hypothesized relationships between collision force, speed of a vehicle, and severity of a crash were developed and evaluated in an attempt to unravel the relationships between exogenous factors and severity of single-vehicle crashes. On the basis of goodness of fit and model predictive power, the model with two latent variables outperformed the other two. Additional insights about model selection were provided through the development and comparison of the three models.

Both crash count and severity are thought to quantify crash risk at defined transport network locations (e.g. intersections, a particulate section of highway, etc.). Crash count is a measure of the likelihood of occurring a potential harmful event, whereas crash severity is a measure of the societal impact and harm to the society. As the majority of safety improvement programs are focused on preventing fatal and serious injury crashes, identification of high-risk sites—or blackspots—should ideally account for both severity and frequency of crashes. Past research efforts to incorporate crash severity into the identification of high-risk sites include multivariate crash count models, equivalent property damage only models and two-stage mixed models. These models, however, often require suitable distributional assumptions for computational efficiency, neglect the ordinal nature of crash severity, and are inadequate for capturing unobserved heterogeneity arising from possible correlations between crash counts of different severity levels. These limitations can ultimately lead to inefficient allocation of resources and misidentification of sites with high risk of fatal and serious injury crashes. Moreover, the implication of these models in blackspot identification is an important, unanswered question.While a joint econometric model of crash count and crash severity has the flexibility to account for the limitations mentioned previously, its ability to identify high-risk sites also needs to be examined. This study aims to fill this research gap by employing the joint model for blackspot identification. Using data from state-controlled roads in Queensland, Australia, a new risk score is developed based on predicted crash counts by severity, weighted by the cost ratio of severity levels. This weighted risk score is then used for identifying road segments with high risk of fatal and injury crashes. Results show that the joint model of crash count and crash severity has substantially improved prediction accuracy compared to the traditional count models. The correlation between crash counts of different severity levels captures the unobserved heterogeneity caused by the extra-variation in total crash counts and moderates the parameters in the joint model. In comparison with the traditional approaches, the proposed weighted risk score approach with the joint model of crash count and crash severity leads to the identification of a higher number of fatal and serious injury crashes in the top ranked sites flagged for safety improvements.

Determinants of crash injury severity for delivery riders: Insights from an error components mixed logit model with heterogeneous means and variances

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.

PurposeThe objective of this paper is to provide a new method for estimating crash rate and severity simultaneously.MethodsThis study explores a Heckman selection model of the crash rate and severity simultaneously at different levels and a two-step procedure is used to investigate the crash rate and severity levels. The first step uses a probit regression model to determine the sample selection process, and the second step develops a multiple regression model to simultaneously evaluate the crash rate and severity for slight injury/kill or serious injury (KSI), respectively. The model uses 555 observations from 262 signalized intersections in the Hong Kong metropolitan area, integrated with information on the traffic flow, geometric road design, road environment, traffic control and any crashes that occurred during two years.ResultsThe results of the proposed two-step Heckman selection model illustrate the necessity of different crash rates for different crash severity levels.ConclusionsA comparison with the existing approaches suggests that the Heckman selection model offers an efficient and convenient alternative method for evaluating the safety performance at signalized intersections.

Purpose – This chapter gives an overview of methods for defining and analysing crash severity. Methodology – Commonly used methods for defining crash severity are surveyed and reviewed. Factors commonly found to be associated with crash severity are discussed. Approaches for formulating and estimating models for predicting crash severity are presented and critiqued. Two examples of crash severity modelling exercises are presented and findings are discussed. Suggestions are offered for future research in crash severity modelling. Findings – Crash severity is usually defined according to the outcomes for the persons involved. The definition of severity levels used by law enforcement or crash investigation professionals is less detailed and consistent than what is used by medical professionals. Defining crash severity by vehicle damage can be more consistent, as vehicle response to crash forces is more consistent than that of humans. Factors associated with crash severity fall into three categories – human, vehicle/equipment and environmental/road – and can apply before, during or after the crash event. Crash severity can be modelled using ordered, nominal or several different types of mixed models designed to overcome limitations of the ordered and nominal approaches. Two mixed modelling examples demonstrate better prediction accuracy than ordered or nominal modelling. Research Implications – Linkage of crash, roadway and healthcare data sets could create a more accurate picture of crash severity. Emerging statistical analysis methods could address remaining limitations of the current best methods for crash severity modelling. Practical Implications – Medical definitions of injury severity require observation by trained medical professionals and access to private medical records, limiting their use in routine crash data collection. Crash severity is more sensitive to human and vehicle factors than environmental or road factors. Unfortunately, human and vehicle factor data are generally not available for aggregate forecasting.

Objective: The objective of this paper is to identify the list of crash severity contributing factors and evaluate their impact on multiple-vehicle crashes on two high use Trans-European interurban, freight corridors in Spain (southern Europe): Madrid - Irùn and Barcelona – Almerìa.Methods: We have used both logistic regression and random forests to identify crash severity predictors and estimate their impacts on crash outcomes. Although both statistical methods can provide useful information to help explain the safety implications of highway crashes, using both methods may further enable a more comprehensive understanding of this phenomenon. For this effort, we disaggregated the crash data into different crash types (i.e., head-on, angle, sideswipe and rear-end) and analyzed this data using roadway design elements, driver characteristics, and environmental factors. To identify the most important predictors of crash severity, we used the random forests data mining approach. We then used ordered logit models to estimate the effect of external factors on the severity of each crash type. Finally, we assessed the accuracy of the model estimates using bootstrap sampling.Results: The results of data mining analyses indicated that roadway design factors such as horizontal and vertical curvature, super elevation, and lane and shoulder width are among the most important factors associated with crash severity. The results of logistic regression show that the impact of the selected roadway element on the crash outcome is conditional on the crash type and the direction of the effects is not always consistent.Conclusions: The contribution of this paper to the existing literature is two-fold: the first important contribution of the paper is related to the safety analysis of two of the most important freight corridors in Spain and southern Europe. The second contribution of this paper is to address the existing gap in the literature relating to the comparison and compatibility of data mining and the logistic regression model.

The impact that trucks have on crash severity has long been a concern in crash analysis literature. Furthermore, if a truck crash happens in a tunnel, this would result in more serious casualties due to closure and the complexity of the tunnel. However, no studies have been reported to analyze traffic crashes that happened in tunnels and develop crash databases and statistical models to explore the influence of contributing factors on tunnel truck crashes. This paper summarizes a study that aims to examine the impact of risk factors such as driver factor, environmental factor, vehicle factor, and tunnel factor on truck crashes injury propensity based on tunnel crashes data obtained from Shanghai, China. An ordered logit model was developed to analyze injury crashes and property damage only crashes. The driver factor, environmental factor, vehicle factor, and tunnel factor were explored to identify the relationship between these factors and crashes and the severity of crashes. Results show that increased injury severity is associated with driver factors, such as male drivers, older drivers, fatigue driving, drunkenness, safety belt used improperly, and unfamiliarity with vehicles. Late night (00:00–06:59) and afternoon rushing hours (16:30–18:59), weekdays, snow or icy road conditions, combination truck, overload, and single vehicle were also found to significantly increase the probability of injury severity. In addition, tunnel factors including two lanes, high speed limits (≥80 km/h), zone 3, extra-long tunnels (over 3000 m) are also significantly associated with a higher risk of severe injury. So, the gender, age of driver, mid-night to dawn and afternoon peak hours, weekdays, snowy or icy road conditions, the interior zone of a tunnel, the combination truck, overloaded trucks, and extra-long tunnels are associated with higher crash severity. Identification of these contributing factors for tunnel truck crashes can provide valuable information to help with new and improved tunnel safety control measures.

Electric motorcycle crash severity: a random parameters logit model considering interaction effects and counterfactual policy evaluation