A novel approach for analyzing severe crash patterns on multilane highways

Little crash equity research has controlled for active transportation rates, examined the Southeastern United States, or used composite sociodemographic metrics. This study analyzed disparities in pedestrian and cyclist crashes and crash severity according to level of social vulnerability (SV) across South Carolina. Data about SV and its 4 dimensions (socioeconomic status, household composition and disability, minority status and language, and housing type and transportation) were compiled for all census tracts (N = 1103) within South Carolina. Data for all crashes involving a pedestrian (n = 10,688) and/or cyclist (n = 4802) from 2011 to 2021 were obtained from the South Carolina Department of Transportation and geocoded to the respective census tract. Total average pedestrian and cyclist crash severity (Equivalent Property Damage Only) were also calculated for each tract. Crash frequency and severity scores were adjusted using the average number of walking and cycling trips in the census tract per year using StreetLight Data. Mixed-model linear regression analyzed the relationships between overall SV and the 4 SV dimensions and 4 crash measures-pedestrian and cyclist frequency and severity. Stratified analyses were conducted for urban and rural tracts. Overall SV was positively and significantly associated with all 4 crash outcomes in urban areas: pedestrian crashes per trip (B = 0.048, SE = 0.012, P < .001), pedestrian crash severity per trip (B = 9.018, SE = 2.516, P < .001), cyclist crashes per trip (B = 0.093, SE = 0.029, P < .01), and cyclist crash severity per trip (B = 16.370, SE = 5.482, P < .01). In urban areas, greater SV was associated with more severe pedestrian and cyclist crash outcomes. Targeted policy and programmatic and infrastructure interventions are needed to improve active transportation safety and public health.

Objective: The objective in this study, using data from crashed cars fitted with on-board crash pulse recorders, was to present differences in average crash severity, distribution of crash severity, and injury outcomes, based on an independent safety rating of roads, also taking road type and speed limit into consideration. Furthermore, the objective was to evaluate differences in injury risk, based on the distribution of crash severity. Methods: The investigation included both frontal two-vehicle crashes and single-vehicle crashes with known injury outcome. In total, 209 real-world crashes involving cars fitted with crash pulse recorders were included. For all crashes, average mean acceleration and change of velocity of the vehicle acceleration pulse were measured and calculated. All crash spots were classified according to an independent road safety rating program (European Road Assessment Programme Road Protection Score), where the safety quality of roads is rated in relation to posted speed limits. The crash severity and injury outcome in crashes that occurred on roads with good safety ratings were compared with crashes on roads with poor safety ratings. The data were also divided into subcategories according to posted speed limit and road type, to evaluate whether there was a difference in crash severity and injury outcome within the categories. Results and Conclusions: In total, crash severity was statistically significantly lower in crashes occurring on roads with good safety ratings than in crashes occurring on roads with poor safety ratings. It was found that crash severity and injury risk were lower on roads with good safety ratings with a speed limit of above 90 km/h compared with roads with poor safety ratings, irrespective of speed limit. On the other hand, crash severity was higher on roads with good safety ratings with speed limit of 70 km/h than on roads with poor safety ratings with the same speed limit. Though it was found that a higher speed limit resulted in higher crash severity on roads with poor safety ratings, the opposite was found on roads with good safety ratings. The main reason for this was that lanes for traffic traveling in opposite directions were more often separated at higher speeds on roads with good safety ratings. On divided roads with good safety ratings, there were no crashes resulting in crash severity above the level corresponding to a 10 percent risk of sustaining serious or fatal injury. This indicates that one of the most important safety measures is divided roads.

This study investigated the main factors affecting the severity of injury to pedestrians in taxi–pedestrian crashes on urban arterial roads. Video data recorded by an in-car black box were used. Because the video data provided direct crash observation, they were more reliable than the crash data, and video images and speed profiles retrieved from the black box were advantageous for safety studies. For analysis of the black box data, this study defined new explanatory variables that affected injury severity; these variables could not have been identified by the conventional method, which was based on crash reports. A multiple-indicator and multiple-cause model was used to investigate the relationship between the explanatory variables and injury severity. A total of 484 taxi–pedestrian crash scenes over 2 years was used for the multivariate analysis in the city of Incheon, South Korea. The crash characteristics most strongly associated with increased crash severity were failure by the pedestrian to watch for approaching vehicles, jaywalking by the pedestrian, the pedestrian being elderly, excessive vehicle speed, failure by the driver to immediately stop, limited driver vision, and nighttime. This study emphasized the potential of individualized black box video recording data for crash severity analysis and investigation of the causal factors of crashes.

Analysis of driver injury severity in single-vehicle crashes on rural and urban roadways

&lt;div&gt;As pedestrians are among the most critical road users, this research analyzes their vulnerability characteristics and predicts the injury severity of pedestrian crashes through decision tree techniques, rather than using statistical regression models that have particular predefined causal relationships between dependent and independent variables. Five years have been studied in Nablus Governorate/Province (2012–2016), one of 16 governorates in Palestine, as a case study based on reported crash frequencies for developing countries. Tree techniques (CART [Classification and Regression Tree] and CHAID [Chi-Square Automatic Interaction Detector]) were applied to extract the main impacting factors on injury severity for pedestrian crashes. The main contributions considered a small regional context in developing countries and found differences between the results of various methods in injury severity. Fourteen independent variables have been analyzed. A CART model with Gini splitting has produced the most accurate model. The most important variables were the victim’s gender, followed by area classification as rural, and the age categories of pedestrians older than 65 and younger than 15 years. The least important variables were found to be the driver’s gender, land use, and pavement conditions. Results also showed that the proximity of crashes to schools is relatively high; therefore, some policies were suggested regarding children’s awareness, school zones, and driver behavior. It was found that the majority of factors influencing pedestrian crashes are related to human characteristics such as age, gender, or attitude whereas, in developed countries, they were related to vehicles and infrastructure. Based on the results of the study, tree techniques were considered effective in the analysis of injury severity of pedestrians in the context of developing countries to identify the main factors of vulnerability.&lt;/div&gt;

This research examines the utility of combined crash modification factors (CMFs) in minimizing crash severity at urban roundabouts. Conventional CMFs typically assess the influence of singular interventions on road safety. However, traffic dynamics and the interactions among multiple safety measures necessitate a more comprehensive strategy. This study introduces a novel methodology for developing integrated CMFs that accounts for the interactive effects of multiple safety measures, providing a comprehensive understanding of their collective impact on road safety. The investigation utilized data from 16 urban roundabouts in Amman, Jordan, from 2015 to 2021. Regression models have been employed to develop individual and combined CMFs for various geometric and traffic characteristics, such as entry and exit widths, speed limits, and weaving patterns. Key findings indicate that interventions, such as reducing speed limits, modifying entry and exit widths, and adjusting weaving lengths, significantly improve safety. The analysis identifies hazardous roundabouts and proposes effective single and combined treatments to mitigate crash risks. This study highlights the importance of considering multiple treatments simultaneously to improve the predictive accuracy of safety assessments and supports the development of more effective road safety strategies tailored to specific crash types and severities. The approach demonstrates a significant potential to enhance road safety analysis and decision-making, ultimately contributing to safer road environments.

Analyzing crash injury severity for a mountainous freeway incorporating real-time traffic and weather data

Pile-up (PU) crashes, which involve multiple collisions between more than two vehicles within a brief timeframe, carry substantial consequences, including fatalities and significant damages. This study aims to investigate the real-time traffic, environmental, and crash characteristics and their interactions in terms of their contributions to severe PU crashes, which have been understudied. This study investigates and interprets the effects of Total Volume/Capacity (TV/C), “Heavy Vehicles Volume/Total Volume” (HVV/TV), and average speed. For this purpose, the PU crash severity was modelled and interpreted using the crash and real-time traffic data of Iran’s freeways over a 5-year period. Among six machine learning methods, the CatBoost model demonstrated superior performance, interpreted via the SHAP method. The results indicate that avg.speed &gt; 90 km/h, TV/C &lt; 0.6, HVV/TV ≥ 0.1, horizontal curves, longitudinal grades, nighttime, and the involvement of heavy vehicles are associated with the risk of severe PU crashes. Additionally, several interactions are associated with severe PU crashes, including the co-occurrence of TV/C ≈ 0.1, HVV/TV ≥ 0.25, and nighttime; the interactions between TV/C ≈ 0.1 or 0.45, HVV/TV ≥ 0.25, and avg.speed &gt; 90 km/h; horizontal curves and high average speeds; horizontal curves; and nighttime. Overall, this research provides essential insights into traffic and environmental factors driving severe PU crashes, supporting informed decision-making for policymakers.

BackgroundDespite substantial progress, motor vehicle crashes remain a leading killer of US children. Previously, we documented significant positive impacts of Safe Routes to School interventions on school-age pedestrian and pedalcyclist...

Single-vehicle crash severity outcome prediction and determinant extraction using tree-based and other non-parametric models

A data-driven approach to child pedestrian crash analysis using dimension reduction, clustering, and explainable AI.

Patterns and sources of spatial inequity in freight crashes: An application of decomposition analysis

This study investigates the relationship between crash severity and risk factors, addressing the limitations of conventional methods in capturing spatial and temporal heterogeneity. We developed and compared five binary logit models: a basic model, temporal heterogeneity model, spatial heterogeneity model, spatiotemporal heterogeneity, and a partially constrained spatiotemporal heterogeneity model. The latter leverages the posterior distribution properties to ensure similar adjacent time and space regions share distribution. These models were calibrated and validated using 3,222 pedestrian crashes occurring at mid-blocks in Hong Kong (2010-2019). The results showed that both the unconstrained and partially constrained spatiotemporal models outperformed others in fit and predictive accuracy. Temporal heterogeneity analysis revealed evolving risk factors, while spatial heterogeneity analysis identified hot zones for crash severity. Key risk factors included pedestrian age, head injury, pedestrian actions, driver operations, vehicle type, and the first collision point. These insights support targeted, region- and time-specific pedestrian strategies.

Investigating the injury severity of single-vehicle truck crashes in a developing country

Previous studies have investigated various factors that contribute to the severity of work zone crashes. However, little has been done on the specific effects of light conditions. Using the data from the Enhanced Tennessee Roadway Information Management System (E-TRIMS), crashes that occurred in the Tennessee work zones during 2003–2015 are categorized into three light conditions: daylight, dark-lighted, and dark-not-lighted. One commonly used decision tree method—Classification and Regression Trees (CART)—is adopted to investigate the factors contributing to crash severity in highway work zones under these light conditions. The outcomes from the three decision trees with differing light conditions show significant differences in the ranking and importance of the factors considered in the study, thereby indicating the necessity of examining traffic crashes according to light conditions. By separately considering the crash characteristics under different light conditions, some new findings are obtained from this study. The study shows that an increase in the number of lanes increases the crash severity level in work zones during the day while decreasing the severity at night. Similarly, drugs and alcohol are found to increase the severity level significantly under the dark-not-lighted condition, while they have a limited influence under daylight and dark-lighted conditions.