Traffic Injury Severity Research Articles

Exploring characteristics and severity of road traffic injuries in Lebanon using emergency department hospital-based data

In Lebanon, the lack of quality data on road traffic injuries (RTIs) led to the implementation of a hospital-based RTI surveillance system by the Ministry of Health in in private and public-run hospitals in the Bekaa governorate. This paper aims to describe the characteristics and severity of RTIs recorded over two years during the pilot phase. It also assesses the strengths and challenges of the surveillance system, highlighting areas for enhancement. The data collected from the Emergency department (ED) was used to conduct a retrospective analysis of population-based injuries hospitalized for road traffic crashes (RTC). Designated focal persons reported injuries weekly using a standardized form, which included demographic and crash-related variables, body lesions, and vital signs. Data were coded per the International Classification of Diseases (ICD-10), entered into Epidata, and analyzed using SPSS. The RTI surveillance system was assessed using Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis. Over two years, a total of 1576 cases of RTIs were reported. The male-to-female ratio was 2.16 and the majority of RTIs (44.4%) were recorded among those aged between 15 and 29 years old. From 2013 to 2015, a decrease of 0.7% in the revised trauma score (RTS < 4) was recorded. On the contrary, an increase of 3.9% in injury severity score (ISS) that ranged between 15 and 75 was reported. The probability of survival of an injured individual at one month was improved. The hospital-based surveillance system demonstrated strengths in structured data collection and ethical considerations but faced challenges like underreporting, limited coverage, and resource constraints. Recommendations for improvement include enhancing data quality and timely reporting, ultimately supporting evidence-based road safety interventions.

Identifying emerging hot spots of road traffic injury severity using spatiotemporal methods: longitudinal analyses on major roads in Ghana from 2005 to 2020

BackgroundAlthough road traffic injuries and deaths have decreased globally, there is substantial national and sub-national heterogeneity, particularly in low- and middle-income countries (LMICs). Ghana is one of few countries in Africa collecting comprehensive, spatially detailed data on motor vehicle collisions (MVCs). This data is a critical step towards improving roadway safety, as accurate and reliable information is essential for devising targeted countermeasures.MethodsHere, we analyze 16 years of police-report data using emerging hot spot analysis in ArcGIS to identify hot spots with trends of increasing injury severity (a weighted composite measure of MVCs, minor injuries, severe injuries, and deaths), and counts of injuries, severe injuries, and deaths along major roads in urban and rural areas of Ghana.ResultsWe find injury severity index sums and minor injury counts are significantly decreasing over time in Ghana while severe injury and death counts are not, indicating the latter should be the focus for road safety efforts. We identify new, consecutive, intensifying, and persistent hot spots on 2.65% of urban roads and 4.37% of rural roads. Hot spots are intensifying in terms of severity and frequency on major roads in rural areas.ConclusionsA few key road sections, particularly in rural areas, show elevated levels of road traffic injury severity, warranting targeted interventions. Our method for evaluating spatiotemporal trends in MVC, road traffic injuries, and deaths in a LMIC includes sufficient detail for replication and adaptation in other countries, which is useful for targeting countermeasures and tracking progress.

Cumulative costs of severe traffic injuries in Finland: a 2-year retrospective observational study of 252 patients

Road traffic injuries cause considerable financial strain on health care systems worldwide. We retrospectively analyzed injury-related costs of 252 severely injured (New Injury Severity Score, NISS ≥ 16) patients treated at Tampere University Hospital (TAUH) between 2013 and 2017, with 2-year follow-up. The costs were divided into direct treatment, indirect costs, and other costs. We analyzed various injury- and patient-related factors with costs. The total costs during the 2-year study period were 20 million euros. Median cost was 41,202 euros (Q1 23,409 euros, Q3 97,726 euros), ranging from 2,753 euros to 549,787 euros. The majority of costs (69.1%) were direct treatment costs, followed by indirect costs (28.4%). Other costs were small (5.4%). Treatment costs increased with the severity of the injury or when the injury affected the lower extremities or the face. Indirect costs were higher in working age patients and in patients with a higher level of education. The relative proportions of direct and indirect costs were constant regardless of the amount of the total costs. The largest share of costs was caused by a relatively small proportion of high-cost patients during the 1st year after injury. Combined, this makes planning of resource use challenging and calls for further studies to further identify factors for highest costs.

Explainable artificial intelligence in transport Logistics: Risk analysis for road accidents

Automobile traffic accidents represent a significant threat to global public safety, resulting in numerous injuries and fatalities annually. This paper introduces a comprehensive, explainable artificial intelligence (XAI) artifact design, integrating accident data for utilization by diverse stakeholders and decision-makers. It proposes responsible, explanatory, and interpretable models with a systems-level taxonomy categorizing aspects of driver-related behaviors associated with varying injury severity levels, thereby contributing theoretically to explainable analytics. In the initial phase, we employed various advanced techniques such as data missing at random (MAR) with Bayesian dynamic conditional imputation for addressing missing records, synthetic minority oversampling technique for data imbalance issues, and categorical boosting (CatBoost) combined with SHapley Additive exPlanations (SHAP) for determining and analyzing the importance and dependence of risk factors on injury severity. Additionally, exploratory feature analysis was conducted to uncover hidden spatiotemporal elements influencing traffic accidents and injury severity levels. We developed several predictive models in the second phase, including eXtreme Gradient Boosting (XGBoost), random forest (RF), deep neural networks (DNN), and fine-tuned parameters. Using the SHAP approach, we employed model-agnostic interpretation techniques to separate explanations from models. In the final phase, we provided an analysis and summary of the system-level taxonomy across feature categories. This involved classifying crash data into high-level causal factors using aggregate SHAP scores, illustrating how each risk factor contributes to different injury severity levels.

Seat belt detection using gated Bi-LSTM with part-to-whole attention on diagonally sampled patches

One of the high-risk behaviors leading to severe traffic injuries is not wearing a seat belt. It is therefore very important to be able to automatically detect seat belts from surveillance images, encourage drivers to wear seat belts, and enhance passenger safety. In this paper, a novel deep neural network, Gated Bi-directional Long Short-Term Memory network with part-to-whole attention (GBL-PA), is proposed for seat belt detection from surveillance images. The innovation of our model lies in its unique diagonal sampling strategy, which meticulously captures the seat belt’s fine details, typically oriented from top right to bottom left across the torso of vehicle occupants. Our framework’s novelty is further encapsulated by the part-to-whole attention mechanism, which intelligently harmonizes the detailed local information from seat belt-specific patches with the broader contextual insights from the regional proposals. The pioneering design of a Gated Bi-directional LSTM network facilitates the dynamic integration of interactions across patches to deliver an optimized final prediction. The superiority of GBL-PA is established through rigorous comparison with the state-of-the-art methods on a new, large benchmark dataset comprising 14,936 images from traffic surveillance footage. Our framework demonstrates a notable improvement, achieving a mean Average Precision (mAP) of 72.3%, which surpasses the second best, YOLOX, by 0.9% mAP. This significant and consistent outperformance across various metrics underscores the transformative potential of GBL-PA in the realm of traffic safety enforcement. The source code of our framework is available at ANONYMISED.

Development and validation of a prediction model for severe traffic accident injury caused by drivers with disabilities in Korea

Development and validation of a prediction model for severe traffic accident injury caused by drivers with disabilities in Korea

Prediction for Severe Traffic Accident Injury in Korea: a Nationwide Database Study

Prediction for Severe Traffic Accident Injury in Korea: a Nationwide Database Study

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.

Factors affecting injury severity in motorcycle crashes: Different age groups analysis using Catboost and SHAP techniques

Objective Motorcycle crashes often result in severe injuries on roads that affect people’s lives physically, financially, and psychologically. These injuries could be notably harmful to drivers of all age groups. The main objective of this study is to investigate the risk factors contributing to the severity of crash injuries in different age groups. Methods This Objective is achieved by developing accurate machine learning (ML) based prediction models. This research examines the relationship between potential risk factors of motorcycle-associated crashes using (ML) and Shapley Additive explanations (SHAP) technique. The SHAP technique further helped interpreting ML methods for traffic injury severity prediction. It indicates the significant non-linear interactions between dependent and independent variables. The data for this study was collected from the Provincial Emergency Response Service RESCUE 1122 for the Rawalpindi region (Pakistan) over three years (from 2017 to 2020). The Synthetic Minority Oversampling Technique (SMOTE) is employed to balance injury severity classes in the pre-processing phase. Results The results demonstrate that age, gender, posted speed limit, the number of lanes, and month of the year are positively associated with severe and fatal injuries. This research also assesses how the modeling framework varies between the ML and classical statistical methods. The predictive performance of proposed ML models was assessed using several evaluation metrics, and it is found that Catboost outperformed the XGBoost, Random Forest (RF) and Multinomial Logit (MNL) model. Conclusion The findings of this study will assist road users, road safety authorities, stakeholders, policymakers, and decision-makers in obtaining substantial and essential guidance for reducing the severity of crash injuries in Pakistan and other countries with prevailing conditions.

Developing machine-learning-based models to diminish the severity of injuries sustained by pedestrians in road traffic incidents

An essential step in devising measures to improve road safety is road accident prediction. In particular, it is important to identify the risk factors that increase the likelihood of severe injuries in the event of an accident. There are two distinct ways of analyzing data in order to produce predictions: machine learning and statistical methods. This study explores the severity of road traffic injuries sustained by pedestrians through the use of machine-learning methodology.In general, the goal of the statistician is to model and understand the connections between variables, whereas machine learning focuses on more intricate and expansive datasets, with the aim of creating algorithms that can recognize patterns and make predictions without being explicitly programmed. The ability to handle very large datasets constitutes a distinct advantage of machine learning over statistical techniques. In addition, machine-learning models can be adapted to a wide range of data sources and problem domains, and can be utilized for numerous tasks, from image identification to natural language processing. Machine-learning models may be taught to recognize patterns and make predictions automatically, minimizing the need for manual involvement and enabling rapid data processing of enormous quantities of data. The use of new data to retrain or fine-tune a machine-learning model allows the model to adapt to changing conditions and enhances its accuracy over time. Finally, while non-linear interactions between variables can be difficult to predict using conventional statistical techniques, they can be recognized by machine-learning models.The study begins by compiling an inventory of features linked to both the accident and the environment, focusing on those that exert the greatest influence on the severity of pedestrian injuries. The “optimal” algorithm is then chosen based on its superior levels of accuracy, precision, recall, and F1 score. The developed model should not be regarded as fixed; it should be updated and retrained on a regular basis using new traffic accident data that mirror the evolving interplay between the road environment, driver characteristics, and pedestrian conduct. Having been constructed using Israeli data, the current model is predictive of injury outcomes within Israel. For broader applicability, the model should undergo retraining and reassessment using traffic accident data from the pertinent country or region.

A new Norwegian Injury Registration System as a starting point to assess the burden of injuries in Norway

Abstract Introduction The police register is the main database for monitoring traffic injuries. A study made by Safe Traffic in Norway found that only one of three severe traffic injuries is registered in this register. For giving more complete data, a health-based injury monitoring system was established in 2022 for a period of five years, financed by the traffic safety authorities. Methods An Injury Minimum Data Set (MDS) is collected from all injuries that are treated in ten hospitals and seven municipality AEDs in the primary health care, a representative sample of all medically treated injuries in Norway. By recording the unique birth number of the patient, double registrations are deleted. When fully developed, this system will register about 50 % of all injuries treated in the hospitals as in- and out-patients, and about 10 % of all injuries in the primary health care system in Norway. Max AIS (Abbreviated Injury Scale) and diagnosis (ICD-10 - hospitals, and ICPC-2 - primary health care) are among the registered variables for each injury. Based on the ICD-10 codes, long term disability weights (DW) can be calculated (Gabbe et al 2014). Then the injury burden of injuries in Norway can be shown by: 1) Threat to life: a) minor (AIS 1), b) moderate (AIS 2) and c) serious, (AIS 3+, including AIS 4-6). 2) Threat of disability: a) low, b) medium and c) high probability of long-term disability. Results Data from 2023 seems to be complete enough to enable calculations of the annual injury burden in Norway. Future work There is a need to establish internationally agreed disability weights to enable comparisons across countries. And to establish conversions from ICPC-2 codes to ICD-10 codes for enabling calculations of DWs of injuries treated in the primary health system.

Identification of determinant factors for crash severity levels occurred in Addis Ababa City, Ethiopia, from 2017 to 2020: using ordinal logistic regression model approach

BackgroundRoad traffic Injuries (RTI) are multifaceted occurrences determined by the combination of multiple factors. Also, severity levels of injuries from road traffic accidents are determined by the interaction of the composite factors. Even though most accidents are severe to fatal in developing countries, there is still a lack of extensive researches on crash severity levels and factors associated with these accidents. Hence, this study was intended to identify severity levels of road traffic injuries and determinant factors in Addis Ababa City, Ethiopia.MethodsThe study was conducted in Addis Ababa, the capital city of Ethiopia, using secondary data obtained from the Addis Ababa Police Commission office. The ordinal logistic regression model was used to investigate road traffic injury severity levels and factors worsening injury severity levels using the recorded dataset from October 2017 to July 2020.ResultsA total of 8458 car accidents were considered in the study, of which 15.1% were fatal, 46.7% severe, and 38.3% were slight injuries. The results of the ordinal logistic regression analysis estimation showed that being a commercial truck, college and above level educated driver, rollover crash, motorbike passengers, the crash day on Friday, and darkness were significantly associated factors with crash injury severity levels in the study area. On the contrary, driving experience (> 10 years), passenger of the vehicle, two-lane roads, and afternoon crashes were found to decrease the severity of road traffic injuries.ConclusionsRoad traffic injury reduction measures should include strict law enforcement in order to maintain road traffic rules especially among commercial truckers, motorcyclists, and government vehicle drivers. Also, it is better to train drivers to be more alert and conscious in their travels, especially on turning and handling their vehicles while driving.

Causal Analysis and Classification of Traffic Crash Injury Severity Using Machine Learning Algorithms

Causal Analysis and Classification of Traffic Crash Injury Severity Using Machine Learning Algorithms

Comparison of traffic accident injury severity prediction models with explainable machine learning

ABSTRACT Traffic accidents are still the main cause of fatalities, injuries and significant delays in highways. Understanding the accident contributing factor is imperative to increase safety in a traffic network. Recent research confirms that predictive modeling is an important tool to comprehend accident contributing factors. However, little effort has been put forward to explain complex machine learning models and their feature effects in accident prediction models. Thus, this study aims to build predictive models based on different machine learning methods and tries to explain the most contributing factors by using Shapley values which was developed based on game theory. Decision Trees, Neural Networks with Multilayer Perceptron (MLP), Support Vector Classifier, Case-Based Reasoning and Naive Bayes Classifier were used to predict the injury severity in accidents. Belt usage, alcohol consumption and speed violations were found as the most effective features and MLP gave the highest accuracy among all the applied predictive models.

Building machine-learning models for reducing the severity of bicyclist road traffic injuries

Predicting the severity of injuries caused by traffic accidents is an important undertaking because it may lead to establishing regulations increasing road-user safety. Bicyclists are a particularly susceptible category of road users, which is especially troubling considering the environmental, financial, and health benefits of this mode of transportation. As a result, this study aims to apply machine learning to identify risk variables that may result in serious biker injuries in the case of an accident.Machine-learning models make no assumptions about the connections between variables. Hence, it has been argued that machine-learning approaches produce better outcomes than statistical procedures. This study selects the “best” machine-learning classification system from a vast pool of similar algorithms to predict the severity of bicycling injuries. Machine learning allows the system to learn from experience and improve without being too programmed.We first use a variety of feature selection algorithms to identify a list of features related to the accident and the environment that have the greatest impact on the severity of bicyclist injuries. This feature list is then used as input data to various machine-learning algorithms that predict the class of bicyclist injury severity at one of three levels (fatal, serious, and slight). The “best” machine-learning algorithm is identified on the basis of having the highest levels of accuracy, precision, recall, and F1 score. The current models were developed and trained based on Israeli road-traffic accident data from 2009 to 2019, meaning that new models would need to be developed for other geographical locations. In addition, the models would need to be updated to take account of the changing relationships between motorists, bicyclists, and the environment. Nevertheless, the proposed methodology has universal applicability.

Exploring weather-related factors affecting the delay caused by traffic incidents: Mitigating the negative effect of traffic incidents

BackgroundExisting studies mainly focus on the relationship between real-time weather and traffic crash injury severity, while few scholars have investigated the operation risk levels caused by traffic incidents. Identifying weather-related factors that affect the incident-induced delay is helpful for estimating the delay levels when an incident occurs. Accordingly, the present study profoundly explores the relationship between weather conditions and traffic delays caused by traffic incidents. MethodsThe traffic incident and weather datasets from January 1 to December 31, 2020, in New York State are used. To that end, the hazard-based duration and multinomial logit modeling frameworks are employed to determine the effect of weather conditions on the duration of traffic delay and the delay severity, respectively. More importantly, to account for multiple layers of unobserved heterogeneity, a random parameter with heterogeneity in means approach is introduced into the above two models. Results(1) The strong breeze (wind speed over 8 m/s) and low visibility (visibility under 5 km) significantly affect the duration of delay. (2) Hot day (between 20 and 30 °C) has a 344.03 % greater probability of minor delay. A strong breeze has a higher probability of severe delay. The low visibility is found to increase the estimated odds of moderate delay and severe delay by 51.15 % and 13.39 %, respectively. In comparison, the normal visibility (between 10 and 20 km) significantly decreases the estimated odds of severe delay by 119.17 %. ConclusionsCompared with other weather factors, wind speed, temperature, and visibility have the greatest impact on the traffic delay levels after a traffic accident, and there are significant differences in the impact under different delay severity. Findings from this study will help policymakers to establish comprehensive differentiating security measures to resolve traffic delays.

Factors Contributing to Traffic Accidents in Hospitalized Patients in Terms of Severity and Functionality.

Trauma-related injuries in traffic-accident victims can be quite serious. Evaluating the factors contributing to traffic accidents is critical for the effective design of programs aimed at reducing traffic accidents. Therefore, this study identified which factors related to traffic accidents are associated with injury severity in hospitalized victims. Factors related to traffic accidents, injury severity, disability and data collected from blood toxicology were evaluated, along with associated severity and disability indices with data collected from toxicology on victims of traffic accidents at the largest tertiary hospital in Latin America. One hundred and twenty-eight victims of traffic accidents were included, of whom the majority were young adult men, motorcyclists, and pedestrians. The most frequent injuries were traumatic brain injury and lower-limb fractures. Alcohol use, hit-and-run victims, and longer hospital stays were shown to lead to greater injury severity. Women, elderly individuals, and pedestrians tend to suffer greater disability post-injury. Therefore, traffic accidents occur more frequently among young male adults, motorcyclists, and those who are hit by a vehicle, with trauma to the head and lower limbs being the most common injury. Injury severity is greater in pedestrians, elderly individuals and inebriated individuals. Disability was higher in older individuals, in women, and in pedestrians.

Interpretable Dynamic Ensemble Selection Approach for the Prediction of Road Traffic Injury Severity: A Case Study of Pakistan’s National Highway N-5

Road traffic accidents are among the top ten major causes of fatalities in the world, taking millions of lives annually. Machine-learning ensemble classifiers have been frequently used for the prediction of traffic injury severity. However, their inability to comprehend complex models due to their “black box” nature may lead to unrealistic traffic safety judgments. First, in this research, we propose three state-of-the-art Dynamic Ensemble Learning (DES) algorithms including Meta-Learning for Dynamic Ensemble Selection (META-DES), K-Nearest Oracle Elimination (KNORAE), and Dynamic Ensemble Selection Performance (DES-P), with Random Forest (RF), Adaptive Boosting (AdaBoost), Classification and Regression Tree (CART), and Binary Logistic Regression (BLR) as the base learners. The DES algorithm automatically chooses the subset of classifiers most likely to perform well for each new test instance to be classified when generating a prediction, making it more efficient and flexible. The META-DES model using RF as the base learner outperforms other models with accuracy (75%), recall (69%), precision (71%), and F1-score (72%). Afterwards, the risk factors are analyzed with SHapley Additive exPlanations (SHAP). The driver’s age, month of the year, day of the week, and vehicle type influence SHAP estimation the most. Young drivers are at a heightened risk of fatal accidents. Weekends and summer months see the most fatal injuries. The proposed novel META-DES-RF algorithm with SHAP for predicting injury severity may be of interest to traffic safety researchers.

Effect of speed humps on injury consequences on trunk roads traversing towns in Ghana: A quasi-experimental study

BackgroundSpeed calming interventions have been employed globally as a road safety measure to curb outcomes of RTCs such as injuries and deaths. In Ghana, few studies have reported on the...

Existence of the safety-in-numbers effect in the aspect of injury severity: A macroscopic analysis for bicyclists and pedestrians

Objective: Several studies have confirmed the existence of a safety-in-numbers effect in relation to vulnerable road users. The safety-in-numbers effect refers to a phenomenon wherein the number of bicyclists/pedestrians on a road is higher, and consequently, the risk of each bicyclist/pedestrian being involved in a crash is lower. Nevertheless, the existence of the safety-in-numbers effect in the aspect of injury severity in traffic crashes has not yet been investigated. Thus, this study aimed to explore whether traffic injuries are more (less) severe with fewer (more) pedestrians/bicyclists at the county level. Method: Using two fractional split multinomial logit models, the relationships between the number of bicyclists/pedestrians and the proportion of crashes involving bicyclists/pedestrians based on crash severity were investigated at the county level using crash data from Florida. In other words, we explored whether differing number of bicyclists/pedestrians could change the distribution of traffic injury severity levels. Results: The modeling results clearly revealed a lower proportion of severe injuries caused to bicyclists/pedestrians at a higher level of daily bicycle/pedestrian flows, indicating existence of the safety-in-numbers effect. Several variables (e.g., the percentage of people aged 65 years and older, the percentage of commuters using public transportation, and the proportion of recreational land use) were found to have a significant effect on the distribution of traffic injury severity among bicyclists/pedestrians. Conclusion: This study proves that a safety-in-numbers effect exists in the aspect of injury severity among bicyclists and pedestrians. Practical applications: These findings are expected to provide recommendations for promoting the use of active transportation, which will improve the safety of vulnerable road users in future.