Crash Data Research Articles

Investigation of winter weather crash injury severity using winter storm classification techniques

This study employs a unique methodology for analyzing seasonal traffic crashes by first identifying adverse winter-weather events using traditional traffic-crash data (from Nebraska) and correlating them to detailed meteorological data for subsequent analyses. Specifically, the novel component of this analysis utilized a state-specific winter storm and seasonal severity index metric in conjunction with classifications of storm type. Both crash and meteorological data were combined to determine if the amount of snowfall and/or storm intensity/severity could be a predictor of the number and severity of crashes. Analysis was performed to identify weather conditions causing highway safety concerns. The important takeaway of the study was that most winter weather-related vehicular crashes in Nebraska were associated with relatively minor winter weather conditions. The reported crashes typically occurred with relatively low snowfall amounts due to residual snowfall on the ground, emphasizing the importance of continuing winter maintenance operations activities and public service announcements long after a storm has passed through the region. Modeling crash injury severity revealed that icy pavements, higher duration of snowfall, work zones, and higher number of trucks and buses involved in a crash were linked to higher injury severity; higher visibility was associated with a greater chance of crashes involving visible injuries but a lower likelihood of crashes involving disabling injury/fatality. Snowfall was associated with more visible injury crashes, whereas snow-depth was linked to fewer visible injury and disabling injury/fatality crashes. The study's limitations include the spatial and temporal aggregation of weather data and a lack of detailed winter roadway maintenance/clearance activity data.

Conflict-Based safety evaluations at unsignalized intersections using surrogate safety measures

Conflict-based road safety assessments may provide a deeper insight into the processes leading to crashes compared to assessments solely based on field crash data. The evaluation of road safety is conducted on specific road segments using different surrogate measure of safety indicators, such as temporal, spatial, and kinematic proximity measures, depending on the relevant context and applicability of these measures. Therefore, this study endeavored to develop a methodology by adopting safety measures such as post encroachment time (PET) and conflicting speeds of through vehicles for crossing maneuvers and time to collision (TTC) for rear-end collisions at five unsignalized intersections in urban mixed traffic conditions. Critical conflicts are calculated by calculating a speed variable known as the critical speed, which is based on the braking distance. A study found that the motorized two wheeler (MTW) categories involve the highest proportion of critical conflict with right-turning vehicles, followed by cars, autos, and light commercial vehicle (LCVs). Furthermore, crossing conflicts were modeled as a function using the generalized linear regression approach. The findings revealed that the most significant factors were traffic volume and vehicular composition in a conflicting stream. The unsupervised classification technique k-mean clustering was used to determine the defined severity level threshold for rear-end maneuvers. The result observed was that a TTC threshold of less than 1.15 s was identified as high-risk vehicular interaction.Additional investigation indicated that presence of certain moving vehicle categories, including MTWs and cars, led to a higher proportion of critical crossing conflicts. The conceptualized safety framework can be applied to evaluate safety at unsignalized intersections in the mixed traffic scenarios.

An Empirical Evaluation of a New Heuristic Method for Identifying Safety Improvement Sites on Rural Highways: An Oregon Case Study

Road safety is considered one of the fundamental factors for sustainable mobility, which requires establishing effective highway safety management programs and processes. Identifying safety improvement sites (network screening) is a critical step in the state roadway safety programs. The overall effectiveness of these programs largely relies on the robustness of the network screening method in identifying sites with high potential for safety improvements. This study investigates the network screening performance of a new proposed methodology that employs heuristic scoring schemes to account for roadway and roadside characteristics, crash history, and traffic exposure. The new method, originally proposed for use on rural low-volume roads, is simplistic in that it does not require exact and detailed information on traffic and geometric characteristics and can still be applied in the absence of crash data. The performance evaluation is conducted using a sample of 1495 miles of rural two-lane highway segments in Oregon. The effectiveness of the proposed method is assessed using observed crash history and compared to the well-established Empirical Bayes method as a reference. The effect of traffic level on the performance of the proposed method was evaluated using separate analyses for lower and higher traffic volume segments. The study findings indicate that the effectiveness of the proposed method in identifying sites with potential safety improvements is overall high and slightly exceeded that of the more sophisticated and data-intensive Empirical Bayes method. When data were stratified by traffic volume, the proposed method was found to be more effective for lower volume roads; however, the Empirical Bayes method outperformed the proposed method for higher volume roads.

Effects of speed difference on injury severity of freeway rear-end crashes: Insights from correlated joint random parameters bivariate probit models and temporal instability

Rear-end crashes particularly on freeways are the most frequent type of collisions causing many injuries, damage and congestion. This paper investigates the impact of varying speed differences between following and leading vehicles on injury severity in two-vehicle rear-end crashes. It develops three groups of correlated joint random parameters bivariate probit models with heterogeneity in means. The rear-end crash data from 2019 to 2021 on Interstate freeways in Florida are utilized, and categorized into periods before, during, and after the COVID-19 pandemic. The study considers two potential injury severity outcomes: no injury and injury/fatality, for both drivers involved in these crashes. The findings indicate that a range of variables, including driver, vehicle, roadway, environmental, crash, and temporal attributes, significantly influence the injury severity outcomes for drivers in both following and leading vehicles. Demonstrating superior goodness-of-fit, the proposed approach sheds light on interactive unobserved heterogeneity, captured through heterogeneity in means and significant correlations among random parameters. The study observes critical influences on the injury severity outcomes of both drivers, with significant factors such as gender, age, vehicle type, weather conditions, lighting, and time of day. Furthermore, the results substantiate the heightened risk outcomes associated with greater speed differences and the period of the COVID-19 pandemic. These findings yield further insights into the risk mechanisms of two-vehicle rear-end crashes and offer guidance for the development of effective safety countermeasures.

Exploring the injury severity of vulnerable road users to truck crashes by ensemble learning

Large numbers of vulnerable road users were killed in truck crashes. In this study, ensemble machine learning models are constructed to predict the injury severity of the vulnerable road user (VRU) to truck (VRU-T) crashes. The study is based on the five years (2017–2021) of VRU-T crash data in the Shandong Province from the Center for Accident Research in Zibo. The injury severity of VRUs is estimated using machine learning ensemble models- Stacking, Voting, Random Forest, and eXtreme Gradient Boosting (XGBoost). Compared to the other three models, the Stacking has excellent predictive performance on the pedestrian and non-motorized datasets. Then, SHapley Additive exPlanations and Partial Dependence Plot box are introduced to analyze risk factors qualitatively and quantitatively. The innovative findings of this study are as follows: (1) as VRUs age, they are more likely to be seriously injured in truck crashes; (2) middle-aged truck drivers and truck drivers with medium driving experience increase the probability of VRUs being severe and fatally injured in truck crashes; (3) crashes involving heavy trucks, under signalized crossing, or on the national and provincial road and urban road have a positive effect on the crash severity for cyclists, and E-Bike riders.

Extensive hypothesis testing for estimation of crash frequency models

Estimating crash data count models poses a significant challenge which requires extensive knowledge, experience, and meticulous hypothesis testing to capture underlying trends. Simultaneous consideration of multiple modelling aspects is required including, among others, functional forms, likely contributing factors, and unobserved heterogeneity. However, model development, frequently affected by time and knowledge, can easily overlook crucial modelling aspects such as identification of likely contributing factors, necessary transformations, and distributional assumptions. To facilitate model development and an estimation that can extract as many insights as possible, an optimization framework is proposed to generate and simultaneously test a diverse array of hypothesis. The framework comprises a mathematical programming formulation and three alternative solution algorithms. The objective function involves minimizing the Bayesian Information Criterion (BIC) to avoid overfitting. The solution algorithms include metaheuristics to deal with an NP-hard problem and search through a complex and nonconvex space. The metaheuristics also enable to handle unique datasets through varying search strategies. The effectiveness of the proposed framework was ascertained using three distinct datasets, and published models used as benchmarks. The results highlighted the ability of the proposed framework to estimate crash data count models, surpassing benchmark models in terms of insights and goodness-of-fit. The framework provides several advantages, such as robust hypothesis testing, uncovering unique specifications and vital insights in the data, and leveraging existing knowledge to enhance search efficiency. The framework also exposes the vulnerability of traditional analyst efforts to fall into local optima, bias, and limitations in creating more efficient models. In a compelling example using crash data from Washington, the proposed framework unveiled insights overlooked by a benchmark published model, identifying speed, interchanges, and grade breaks as likely crash contributors, and revealing the potential danger of excessively wide shoulders. Conversely, the benchmark model identified fewer contributing factors and missed a crucial non-linear relationship between crash safety and shoulder widths. While wider shoulders are typically associated with improved safety, the proposed models suggest a safety threshold beyond which further widening could decrease safety. The introduction of random parameters in the analysis revealed a more nuanced relationship with crash frequency, thereby underlining the limitations of models incapable of capturing heterogeneity.

Analyzing Pile-Up Crash Severity: Insights from Real-Time Traffic and Environmental Factors Using Ensemble Machine Learning and Shapley Additive Explanations Method

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 > 90 km/h, TV/C < 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 > 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.

Assessing the effects of geometry and non-geometry related factors in work-zone crashes

Objective Work zones are unique in geometry and traffic management, utilizing special traffic signs, standard channelizing devices, appropriate barriers, and pavement markings. These configurations can introduce unexpected driving conditions, potentially posing risks to drivers. This analysis aims to explore potential differences in contributing factors between work-zone crashes where geometry was identified as a factor and those where it was non-geometry factor. To gain insights into driver injury severities in single-vehicle work-zone crashes, this study analyzed work zone crash data from Florida. Method This study employed random parameters logit models, accommodating potential variations in parameter estimates’ means and variances. The dataset encompassed a wide array of factors known to influence driver injury severity, encompassing crash characteristics, vehicle attributes, roadway features, prevailing traffic volume, driver profiles, and spatial and temporal considerations. Results This analysis yielded significantly distinct parameters for work-zone crashes, distinguishing between geometry-related and non-geometry-related factors (primarily the human factors). This distinction suggests a complex interplay between these factors. Notably, the marginal effects of individual parameter estimates exhibited marked differences between these two categories – geometry and non-geometry factors. Conclusion These findings contribute to the growing body of research indicating that geometric restrictions within work zones introduce a distinct set of risk factors compared to non-geometry-related factors. Recognizing the significance of geometric restrictions, beyond typical driving conditions, holds the implications for enhancing safety within various work zone configurations and offers valuable insights for crash scene investigators to pinpoint contributing factors accurately.

Investigating pedestrian-vehicle crashes on interstate highways: Applying random parameter binary logit model with heterogeneity in means

In the U.S., the interstate highway system is categorized as a controlled-access or limited-access route, and it is unlawful for pedestrians to enter or cross this type of highway. However, pedestrian-vehicle crashes on the interstate highway system pose a distinctive safety concern. Most of these crashes involve ‘unintended pedestrians’, drivers who come out of their disabled vehicles, or due to the involvement in previous crashes on the interstate. Because these are not ‘typical pedestrians’, a separate investigation is required to better understand the pedestrian crash problem on interstate highways and identify the high-risk scenarios. This study explored 531 KABC (K = Fatal, A = Severe, B = Moderate, C = Complaint) pedestrian injury crashes on Louisiana interstate highways during the 2014–2018 period. Pedestrian injury severity was categorized into two levels: FS (fatal/severe) and IN (moderate/complaint). The random parameter binary logit with heterogeneity in means (RPBL-HM) model was utilized to address the unobserved heterogeneity (i.e., variations in the effect of crash contributing factors across the sample population) in the crash data. Some of the factors were found to increase the likelihood of pedestrian’s FS injury in crashes on interstate highways, including pedestrian impairment, pedestrian action, weekend, driver aged 35–44 years, and spring season. The interaction of ‘pedestrian impairment’ and ‘weekend’ was found significant, suggesting that alcohol-involved pedestrians were more likely to be involved in FS crashes during weekends on the interstate. The obtained results can help the ‘unintended pedestrians’ about the crash scenarios on the interstate and reduce these unexpected incidents.

Investigating safety and cost-effectiveness of cable median barriers in Louisiana

Objectives Cable median barriers (CMBs) are installed on freeway medians to prevent cross-median crashes and reduce the severity of median-related crashes. Though CMBs are effective in preventing cross-median crashes, they are also known to increase the number of property damage–only (PDO) crashes. The higher frequency of PDO crashes could result in increased CMB maintenance and repair expenses. The aim of this study is to evaluate the safety impact and economic justification of CMBs in Louisiana. Methods Initially, a flowchart was developed using Louisiana crash data to identify targeted crashes, such as median-related and cross-median crashes. This was followed by a 3-year observational before-and-after crash analysis with an emphasis on head-on collisions and crashes involving large trucks. Using a 4-step improved prediction method, crash modification factors were then developed to quantitatively assess the impact of CMBs on crash outcomes, accounting for and adjusting to changes in the annual average daily traffic (AADT) and relevant crash frequencies before and after CMB implementation. Finally, an exhaustive benefit–cost analysis was conducted to determine the cost-effectiveness of CMBs. Results The results revealed that CMBs significantly reduced cross-median crashes of all severities. However, an increase in PDO crashes was observed in both total and median-related crashes. Large truck cross-median crashes and head-on collisions also decreased significantly after CMB implementation. Testing Level 4 (TL-4) CMBs were found to be more effective in preventing vehicles from crossing the median and in reducing crashes of higher severity levels. The benefit–cost ratios, calculated using economic crash unit costs for both total and targeted crashes, were greater than 1. Notably, the estimated benefit–cost ratios were considerably higher, demonstrating that CMBs are cost-effective countermeasures for enhancing traffic safety. Conclusion This study contributes to the understanding of CMB performance from both traffic safety and economic perspectives. The findings may assist transportation agencies in making decisions regarding the management of CMB systems. Based on the comprehensive analysis of CMBs on Louisiana freeways, this project has revealed that CMBs are an effective and economically justified crash countermeasure. Thus, further implementation of CMBs is recommended until better alternatives are available.

Analyzing urban crash incidents: An advanced endogenous approach using spatiotemporal weights matrix

AbstractContemporary spatial statistics studies often underestimate the complexity of road networks, thereby inhibiting the strategic development of effective interventions for car accidents. In response to this limitation, the primary objective of this study is to enhance the spatiotemporal analysis of urban crash data. We introduce an innovative spatial‐temporal weight matrix (STWM) for this purpose. The STWM integrates external covariates, including road network topological measurements and economic variables, offering a more comprehensive view of the spatiotemporal dependence of road accidents. To evaluate the functionality of the presented STWM, random effect eigenvector spatial filtering analysis is employed on Boston's traffic accident data from January to March 2016. The STWM improves analysis, surpassing distance‐based SWM with a lower residual standard error of 0.209 and a higher adjusted R2 of 0.417. Furthermore, the study emphasizes the influence of road length on crash incidents, spatially and temporally, with random standard errors of 0.002 for spatial effects and 0.026 for non‐spatial effects. This is particularly evident in the north and center of the study area during specific periods. This information can help decision‐makers develop more effective urban development models and reduce future crash risks.

Fatal Crash Occurrence Prediction and Pattern Evaluation by Applying Machine Learning Techniques

Background Highway safety remains a significant issue, with road crashes being a leading cause of fatalities and injuries. While several studies have been conducted on crash severity, few have analyzed and predicted specific types of crashes, such as fatal crashes. Identifying the key factors associated with fatal crashes and predicting their occurrence can help develop effective preventative measures. Objective This study intended to develop cluster analysis and ML-based models using crash data to extract the prominent factors behind fatal crash occurrences and analyze the inherent pattern of variables contributing to fatal crashes. Methods Several branches and categories of supervised ML models have been implemented for fatality prediction and their results have been compared. SHAP analysis was conducted using the ML model to explore the contributing factors of fatal crashes. Additionally, the underlying hidden patterns of fatal crashes have been evaluated using K-means clustering, and specific fatal crash scenarios have been extracted. Results The deep neural networks model achieved 85% accuracy in predicting fatal crashes in Kansas. Factors, such as speed limits, nighttime, darker road conditions, two-lane highways, highway interchange areas, motorcycle and tractor-trailer involvement, and head-on collisions were found to be influential. Moreover, the clusters were able to discern certain scenarios of fatal crashes. Conclusion The study can provide a clear image of the important factors related to fatal crashes, which can be utilized to create new safety protocols and countermeasures to reduce fatal crashes. The results from cluster analysis can facilitate transportation professionals with representative scenarios, which will benefit in identifying potential fatal crash conditions.

Comparative Evaluation of Crash Hotspot Identification Methods: Empirical Bayes vs. Potential for Safety Improvement Using Variants of Negative Binomial Models

The empirical Bayes (EB) method is widely acclaimed for crash hotspot identification (HSID), which integrates crash prediction model estimates and observed crash frequency to compute the expected crash frequency of a site. The traditional negative binomial (NB) models, often used to estimate crash predictive models, typically struggle with accounting for the unobserved heterogeneity in crash data. Complex extensions of the NB models are applied to overcome these shortcomings. These techniques also present new challenges, for instance, applying the EB procedures, especially for out-of-sample data. This study applies a random parameter negative binomial (RPNB) model within the EB framework for HSID using out-of-sample data, comparing its performance with a varying dispersion parameter NB model (VDPNB). The research also evaluates the potential for safety improvement (PSI) scores for both models and compares them with EB estimates using three generalised criteria: high crashes consistency test (HCCT), common sites consistency test (CSCT), and absolute rank differences test (ARDT). The results yield dual insights. Firstly, the study highlights associations between crash covariates and frequency, emphasising the significance of roadway geometric design characteristics (e.g., lane width, number of lanes, and parking type) and traffic volume. Some variables also influenced overdispersion parameters in the VDPNB model. In the RPNB model, annual average daily traffic (AADT) and lane width emerged as random parameters. Secondly, the HSID performance assessment revealed the superiority of the EB method over PSI. Notably, the RPNB model, compared to the VDPNB, demonstrates superior performance in EB estimates for HSID with out-of-sample data. This research recommends adopting the EB method with RPNB models for robust HSID.

A New Approach for Identifying Safety Improvement Sites on Rural Highways: A Validation Study

The research presented in this paper examines a new proposed approach for identifying safety improvement sites on rural highways. Unlike conventional approaches, the proposed approach does not require crash history, but rather utilizes classified variables for traffic volume, geometric features, and roadside characteristics that do not require access to exact data or extensive technical expertise. The research validates the performance of the proposed approach using field data from a large sample of rural two-lane highway segments in the state of Oregon including traffic, roadway, and crash data. A mathematical model for the prediction of the EB expected number of crashes using multivariate regression analysis is developed and used as the network screening criterion. The model’s independent variables include roadway geometry, roadside characteristics, and traffic exposure, while the dependent variable is the EB expected number of crashes. Using observed crash history as a reference, the performance of the proposed approach was compared to two of the well-established methods in practice, namely, the Empirical Bayes (EB) and the potential for safety improvement (PSI) methods. The study results suggest that by using crash density for highway segments, the performance of the proposed method was lower than that of the EB and PSI methods. This is despite the high R-square value of the predictive model used in the proposed method. However, when using crash frequencies for highway segments, the performance of the proposed method was found comparable to the well-established EB and PSI methods.

Traffic Safety Assessment and Injury Severity Analysis for Undivided Two-Way Highway–Rail Grade Crossings

The safety and reliability of undivided two-way highway–rail grade crossings (HRGCs) are of paramount importance in transportation systems. Utilizing crash data from the Federal Railroad Administration between 2020 and 2021, this study aims to predict crash injury severity outcomes and investigate various factors influencing injury severities. The χ2 test was first used to select variables that were significantly associated with injury outcomes. By employing the eXtreme Gradient Boosting (XGBoost) model and interpretable SHapley Additive exPlanations (SHAP), a cross-category safety assessment that offers an evidence-based hierarchy and statistical inference of risk factors associated with crashes, crossings, vehicles, drivers, and environment was provided for killed, injured, and uninjured outcomes. Some significant predictors overlapped between the killed and injured models, such as old driver, driver was in vehicle, main track, went around the gate, adverse crossing surface, and truck, while the other different significant factors revealed that the model could distinguish between different severity levels. Additionally, the results suggested that the model has varying performances in predicting different injury severities, with the killed model having the highest accuracy of 93.36%. The SHAP dependency plots for the top three features also ensure reliable predictions and inform potential interventions aimed at strengthening traffic safety and risk management practices, such as enhanced warning systems and targeted educational campaigns for older drivers.

Linking crash and breathalyzer data in Connecticut

Objectives To document the process of linking breathalyzer and motor vehicle crash (MVC) data for the State of Connecticut using a unique identifier in the place of personal and private information. Methods Deterministic linkage methodologies were utilized in Microsoft SQL Server to join 5,634 (of 6,650) breathalyzer records to corresponding MVC driver records for the period of January 1, 2017 to December 31, 2022. Differences between the linked and original datasets were documented by comparing the consistency of frequency and proportion distributions of key variables. Results Proportions of annual records, alcohol breath tests, and refusals were nearly unchanged when comparing linked and original breathalyzer data. When examining variables in the original MVC driver records, there were differences in the within-group proportions for sex and age, with an overrepresentation of males and drivers aged 26-to-40 years old. For crash and injury severity, the linked dataset had lower proportions of more severe injury records when compared to the original MVC data. Additionally, 1,007 breathalyzer records were not matched with an associated MVC record. Conclusions Linkage methodology is sound and produced quality matches. The use of a unique identifier provided a strong match qualifier in the absence of personal and private data. Changes in proportions for age, sex, crash and injury severity align with previous research. Potential missed matches may be attributed to several factors outside of the linkage process, including data discrepancies and varied reporting practices. Future studies will further explore these differences and incorporate additional toxicology data as part of a continued effort to fuze crash, citation, toxicology, and public health data. The end result will be a holistic, comprehensive, and multifaceted database for transportation research and education.

Predicting Highway Risk Event with Trajectory Data: A Joint Approach of Traffic Flow and Vehicle Kinematics

Real-time collision risk prediction is essential for improving highway safety and reducing traffic accidents. However, previous studies have mainly used crash data and associated spatially discrete and temporally continuous traffic data, overlooking the potential of vehicle trajectory data, which provides comprehensive spatio-temporal information to characterize traffic near a specific location. Moreover, researchers have typically focused on either traffic flow characteristics or inter-vehicle microscopic kinematic characteristics for real-time risk prediction, with a dearth of studies integrating these two aspects. Given that risk events transpire more frequently than accidents and exhibit a strong correlation with them, it is imperative to concentrate more on risk events to proactively diminish crash probabilities. This study introduces a novel approach that extracts traffic flow and inter-vehicle kinematic features from risk events. It also provides a comparative analysis of the effectiveness of five machine-learning methods (Logistic Regression, K-Nearest Neighbors, eXtreme Gradient Boosting, Random Forests, and Multilayer Perceptron) and two data-processing strategies (oversampling and undersampling) in addressing risk identification and prediction issues. The results showed that (1) the synergistic use of traffic flow and inter-vehicle kinematic features surpasses the use of a single feature in identifying and predicting risks; (2) The eXtreme Gradient Boosting model, trained on the undersampled dataset, emerges as the optimal model for risk identification, boasting an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.976 and an F1 score of 0.604; (3) The RF model exhibits commendable performance under both risk prediction conditions (5 s ahead prediction and 10 s prediction), demonstrating the highest performance with F1 scores of 0.377 and 0.374, respectively. Additionally, it was discovered that the resampling strategy does not always prove effective in developing risk analysis models and should be chosen based on the model’s characteristics and target metrics. This offers valuable insights into the selection of data-processing strategies when handling unbalanced data. Finally, the study’s limitations and potential enhancements are discussed.

The value of Association Rule Analysis in understanding serious and fatal road traffic crashes - a case study of the N4 toll road between 2015 and 2019

In spite of the fact that the road traffic crash fatality rate in South Africa is significantly higher than the global average, the characteristics and causes of road traffic crashes are still not well understood. Without a clear understanding of the characteristics and causes, intelligence-led countermeasures to reduce crashes cannot be developed or applied. The analysis of local South African road crash data is of particular importance in identifying the underlying problems responsible for the high crash rates across the country. While descriptive analyses can be used to present a snapshot of the crash problem, they are largely inadequate when it comes to throwing light on the underlying patterns and relationships between the contributing factors and causes of the crashes themselves. This paper presents a study of the use of Association Rule Analysis (ARA) on data related to crashes on the N4 between Pretoria and the Mozambican border, between 2015 and 2019, to see what added value ARA may offer to standard descriptive analyses of crash data. Apriori Association Rule algorithm was employed on the dataset to identify what contributory factors were not initially evident in the occurrence of serious and fatal crashes. ARA revealed that pedestrians, negligent driving, overtaking and single-vehicle crashes are common factors likely to be present in a serious crash. However, this study found that descriptive statistical analysis and ARA methods are not mutually exclusive, but rather complementary. Where descriptive statistical analysis focuses on summarising and describing data, ARA focuses on identifying relationships and patterns within the data.

Analysis of dynamic determinants of vehicles involved in crash affecting severity based on in-depth crash data

Objective The dynamic characteristics of vehicles involved in crashes may be an important factor affecting the crash severity. This study investigates the relationship between the dynamic characteristics of vehicles involved in crashes in the five seconds before the occurrence and the crash severity. The findings aim to offer insights for preventing severe crashes and advancing autonomous vehicle technology. Methods This study aims to investigate the impact of dynamic features, such as speed, acceleration, and relative distance of vehicles involved in the crash in the five seconds before the crash, on the crash severity. Five hundred ninety-six crash samples from the China In-depth Mobility Safety Study-Traffic Accident database were selected for crash reconstruction. A random parameters logit model was used to extract and analyze the effect of dynamic features of the vehicles involved in the crash on the crash severity. Results The random parameters logit model demonstrated a satisfactory fit. Analysis of the parameter estimation results of the model showed that the variables of speed, acceleration, and relative distance between vehicles involved in the crash at some time points during the five seconds before the crash significantly affected the crash severity. Notably, the coefficient of variation of relative distance over 5 s emerged as the most influential positive determinant of the crash severity. Conclusions Certain dynamic characteristics of vehicles involved in a crash in the five seconds before a crash significantly impact the crash severity. The study’s findings can serve as a reference for preventing severe crashes and advancing the development of autonomous vehicles.

A Review of the Processes and Procedures of Road Traffic Accident Mortality Data Collection in Zambia

Road traffic crashes (RTCs) pose significant public health challenges globally, leading to millions of deaths, injuries and disabilities each year. Low- and middle-income countries, including Zambia, bear a disproportionate burden of RTC fatalities and injuries. However, the collection and accuracy of RTC data in Zambia are hampered by various challenges, including underreporting and inadequate data collection procedures. Strengthening RTC data collection systems is crucial to accurately understand the magnitude of the problem and implement effective measures to reduce RTC fatalities and injuries. This article presents a review of RTC mortality data collection processes and procedures in Zambia, focusing on the key stakeholders involved, namely the Zambia Police Service, Health Care Facilities, and the Department of National Registration, Passports and Citizenship. The study identifies key challenges such as paper-based records, inadequate training, resource limitations, varying case definitions, and low death registration rates. The research emphasizes the need to strengthen data systems and improve coordination among stakeholders. Recommendations include establishing a multidisciplinary road crash data analysis team, utilizing technology for data collection and analysis, providing training and capacity building, conducting public awareness campaigns, and enhancing collaboration between the police, hospitals and Civil Registration and Vital Statistics (CRVS). Implementing these recommendations is crucial to improve the accuracy and completeness of RTC mortality data, leading to evidence-based road safety interventions and policies. This research contributes to the understanding of RTC data collection practices in Zambia and provides valuable insights for policymakers, researchers, and practitioners in addressing the challenges in RTC mortality data collection.