Occupant Injury Severity Research Articles

Analyzing risks of occupational injuries in basic industries

Our research aim was to analyze occupational injuries in basic industries in Irkutsk region. Materials and methods. Occupational injuries (OI) in basic industries were analyzed using data from statistical reports issued in 2010–2019. To analyze OI in dynamics, we calculated relative values of OI and applied linear regression and Shewhart charts. Normalized intensity indicators method was used to reveal different probability of injuries in various industries as well as to predict OI risks. Results. Analysis of OI in dynamics indicates that there is a stable descending trend in a number of injuries. However, in spite of this apparent descending trend, OI values are stably by 1.3–3.0 times higher in some industries than on average in the region. The highest frequency coefficient (FC) for occupational injuries was detected in wood processing where it was equal to 5.35 [2.90–7.71] per 1,000 workers; the indicator varied within 1.00–2.93 per 1,000 workers in other industries. Shewhart chart for FC indicates that systems of occupational health and safety management are not efficient enough in all the analyzed industries since FC exceeds the upper limit in some years. We established that severity of occupational injuries tended to grow in wood processing (Cs = +3.23; 5.33 %), metallurgy (Cs = +0.94; 1.26 %), land transport (Cs = +2.42; 4.39 %), and aircraft production (Cs = +0.59; 1.68 %). The greatest number of fatal OI was detected in mining, construction, and agriculture as a share of fatal OI in the overall structure of occupational injuries amounted to 22.0 %, 19.2 %, and 11.7 % in these brunches accordingly. A probability that an injury becomes fatal is also the highest in them, 11.7, 9.0, and 6.0 accordingly. “Wood processing and production of wood articles”, “Aircraft production”, and “Construction” are among industries where risks of occupational injuries are the most probable.

Анализ риска производственного травматизма в основных отраслях промышленности

Our research aim was to analyze occupational injuries in basic industries in Irkutsk region. Materials and methods. Occupational injuries (OI) in basic industries were analyzed using data from statistical reports issued in 2010–2019. To analyze OI in dynamics, we calculated relative values of OI and applied linear regression and Shewhart charts. Normalized intensity indicators method was used to reveal different probability of injuries in various industries as well as to predict OI risks. Results. Analysis of OI in dynamics indicates that there is a stable descending trend in a number of injuries. However, in spite of this apparent descending trend, OI values are stably by 1.3–3.0 times higher in some industries than on average in the region. The highest frequency coefficient (FC) for occupational injuries was detected in wood processing where it was equal to 5.35 [2.90–7.71] per 1,000 workers; the indicator varied within 1.00–2.93 per 1,000 workers in other industries. Shewhart chart for FC indicates that systems of occupational health and safety management are not efficient enough in all the analyzed industries since FC exceeds the upper limit in some years. We established that severity of occupational injuries tended to grow in wood processing (Cs = +3.23; 5.33 %), metallurgy (Cs = +0.94; 1.26 %), land transport (Cs = +2.42; 4.39 %), and aircraft production (Cs = +0.59; 1.68 %). The greatest number of fatal OI was detected in mining, construction, and agriculture as a share of fatal OI in the overall structure of occupational injuries amounted to 22.0 %, 19.2 %, and 11.7 % in these brunches accordingly. A probability that an injury becomes fatal is also the highest in them, 11.7, 9.0, and 6.0 accordingly. “Wood processing and production of wood articles”, “Aircraft production”, and “Construction” are among industries where risks of occupational injuries are the most probable.

Effects of sedan wheelbase size on left rear-seat occupant injury risk in small offset crashes

The objective of this study was to investigate the effect of sedan wheelbase size on the kinematics and injury severity of left rear-seat occupants by using the finite element (FE) modeling method. A total of 270 cases with detailed accidental information records were analyzed to define the influence laws of wheelbase size and impact speed on the injury of left rear-seat occupants. First, the THUMS (Ver. 4.0.2) FE model was used to reconstruct two small offset collisions with different wheelbases size and unbelted left rear-seat occupants, and the effectiveness of the accident model was verified. Then, seatbelts were added to the left rear-seat occupant models. Finally, LS-DYNA software was used to study the correlation among head and chest injury and five sedan wheelbases sizes (2300, 2450, 2600, 2750, and 2905 mm) at three impact velocities (54, 64, and 74 km/h). The results showed that the occupants’ chest injuries showed an upward trend at the impact velocity of 64 and 74 km/h when the wheelbases sizes was reduced to 2300 mm. This research illustrated that at higher impact velocities, excessively small wheelbases might increase the chest injury severity of left rear-seat occupants.

Use of Real-Time Traffic and Signal Timing Data in Modeling Occupant Injury Severity at Signalized Intersections

This study explored the use of real-time traffic events and signal timing data to determine the factors influencing the injury severity of vehicle occupants at intersections. The analysis was based on 3 years (2017–2019) of crash and high-resolution traffic data. The best fit regression was first identified by comparing the conventional regression model and logistic models with random effect. The logistic model with a heavy-tailed distribution random effect best fitted the data set, and it was used in the variable assessment. The model results revealed that about 13.6% of the unobserved heterogeneity comes from site-specific variations, which underlines the need to use the logistic model with a random effect. Among the real-time traffic events and signal-based variables, approach delay and platoon ratio significantly influenced the injury severity of vehicle occupants at 90% Bayesian credible interval. Additionally, the manner of a collision, occupant seat position, number of vehicles involved in a crash, gender, age, lighting condition, and day of the week significantly affected the vehicle occupant injury. The study findings are anticipated to provide valuable insights to transportation agencies for developing countermeasures to mitigate the crash severity risk proactively.

Analysis of Injury Severity involving of Passenger Vehicles’ Occupants in Frontal Impact Collisions


 
 
 In Malaysia, Passenger Vehicles (PV) accounted for the second-highest proportion of vehicles involved in road crashes (13.5%) as well as the total number of casualties (18.6%) after motorcycles. This raised the alarm that a thorough investigation should be conducted on the injury severity of occupants in PV crashes involving frontal impact collisions. The objective of this study is to evaluate injury severity outcome based on the selected crash parameters for the front seat occupants involved in frontal impact collision. The data for this study was sourced from the MIROS crash investigation database (MICARS) from 2007 until 2019, during which MIROS had investigated a total of 975 cases in both East and West Malaysia. After filtering through the cases, a total of 129 frontal impact crashes (PV versus PV) involving 206 PV and 349 front seat occupants were selected for data analysis. From the total number of selected cases, 82 cases were involved in head-on collisions while 47 cases were involved in side-impact collisions. As a result, crash configuration, crush extent, airbag availability, and seatbelt wearing are strongly related to the level of injury severity of frontal seat occupants involved in a frontal impact collision. Meanwhile, collision types and occupant seating position do not appear to be significantly related to the severity of the occupants’ injuries.
 
 

Traffic crashes in Dhaka, Bangladesh: analysing crashes involving unconventional modes, pedestrians and public transit

Road safety is a global concern; particularly, in developing countries due to the significantly high collision occurrences and subsequent deaths. This study presents a spatial and temporal analysis of collision frequency and injury severity of crashes in Dhaka, Bangladesh. The focus is to understand the spatio-temporal trend of collisions involving pedestrians, public transit and unconventional modes, which are the key collision factors in Dhaka. This research utilizes the police-reported collision record for Dhaka for the years 2011–2015. In temporal analysis, temporal trends (monthly, daily and hourly) of injury severity of different vehicle occupants (pedestrians, public transit and unconventional modes) have been explored using descriptive analytics. Daily distribution suggests that a higher share of severe injuries involving pedestrians (16.6%) and unconventional modes (20.5%) occur on Fridays and Thursdays, respectively. The hourly distribution suggests that pedestrians are more vulnerable from 11:00am to 12:00pm on weekends. Unconventional mode users are vulnerable from 7:00am to 8:00am on weekdays. Spatial analysis is performed adopting a Kernel density estimation (KDE) technique. The results suggest that the major activity locations of Dhaka such as central business district (CBD), airport and ferry terminals are collision prone areas. Interestingly, the density of public transit collisions is skewed around the major transit hubs of the city.

Use of Bivariate Random-Parameter Probit Model to Analyze the Injury Severity of Highway Traffic Crashes Involving School-Age Children

Traffic safety has been a serious public health issue. According to the World Health Organization, annual traffic fatalities and non-fatal injuries are 1.35 million and 20 to 50 million, respectively, worldwide. Vehicle crashes, in particular, are the leading cause of the death of children in the world. This study aims to analyze the injury severity level of drivers and school-age passengers and to identify contributing factors, focusing on the effects of driver characteristics on the severity of injuries to the driver and child passenger. A bivariate model is adopted to capture unobserved shared factors between the driver’s and child’s injury severity levels. The results indicate that the factors contributing to the injury severity level of drivers and school-age passengers are quite different, and some driver characteristics significantly affect the injury severity of the child passenger. The findings from this study can contribute to an efficient strategic plan to reduce the injury severity of vehicle occupants.

A data-driven, kinematic feature-based, near real-time algorithm for injury severity prediction of vehicle occupants

Accurate real-time prediction of occupant injury severity in unavoidable collision scenarios is a prerequisite for enhancing road traffic safety with the development of highly automated vehicles. Specifically, a safety prediction model provides a decision reference for the trajectory planning system in the pre-crash phase and the adaptive restraint system in the in-crash phase. The main goal of the current study is to construct a data-driven, vehicle kinematic feature-based model to realize accurate and near real-time prediction of in-vehicle occupant injury severity. A large-scale numerical database was established focusing on occupant kinetics. A first-step deep-learning model was established to predict occupant kinetics and injury severity using a convolutional neural network (CNN). To reduce the computational time for real-time application, the second step was to extract simplified kinematic features from vehicle crash pulses via a feature extraction method, which was inspired by a visualization approach applied to the CNN-based model. The features were incorporated with a low-complexity machine-learning algorithm and achieved satisfactory accuracy (85.4 % on the numerical database, 78.7 % on a 192-case real-world dataset) and decreased computational time (1.2 ± 0.4 ms) on the prediction tasks. This study demonstrated the feasibility of using data-driven and feature-based approaches to achieve accurate injury risk estimation prior to collision. The proposed model is expected to provide a decision reference for integrated safety systems in the next generation of automated vehicles.

A machine learning approach for building an adaptive, real-time decision support system for emergency response to road traffic injuries

In this paper, historical data about road traffic accidents are utilized to build a decision support system for emergency response to road traffic injuries in real-time. A cost-sensitive artificial neural network with a novel heuristic cost matrix has been used to build a classifier capable of predicting the injury severity of occupants involved in crashes. The proposed system was designed to be used by the medical services dispatchers to better assess the severity of road traffic injuries, and therefore to better decide the most appropriate emergency response. Taking into account that the nature of accidents may change over time due to several reasons, the system enables users to build an updated version of the prediction model based on the historical and newly reported accidents. A dataset of accidents that occurred over a 6-year period (2008-2013) has been used for demonstration purposes throughout this paper. The accuracy of the prediction model was 65%. The Area Under the Curve (AUC) showed that the generated classifier can reasonably predict the severity of road traffic injuries. Importantly, using the cost-sensitive learning technique, the predictor overcame the problem of imbalanced severity distributions which are inherent in traffic accident datasets.

The influence of coach interior partitions and seat leaf-tables on occupant injury in a frontal collision

For a typical coach, interior seats and partitions are considered as secondary collision objects in the event of a frontal collision. When a seat back has a deployable leaf-table, the additional effect of the leaf-table should be considered too. To investigate the effect of occupant collision with these objects, a full-scale frontal crash test of a coach was conducted with an impact velocity of 30 km/h. For virtual tests, a coupling model of partition-seat-occupants on a sled was established using the combination of LS-DYNA and MADYMO. It was validated using the full-scale test. The injury severity of the first row occupants was evaluated under different restraint conditions. Results show that occupants who are not belted and those with a lap belt only have the highest injury severity. A comparison of kinematics and injury characteristics between the first row occupants and the non-first row occupants was conducted to provide a comparative analysis.

Is the front passenger seat always the “death seat”? An application of a hierarchical ordered probit model for occupant injury severity

Although many studies have investigated the correlations between injury severities and seat positions, few researchers explored the correlates of injury severities (e.g., seat positions) within a crash that results in multiple occupant injuries. Therefore, we examine the injury correlates within and between crashes, and study the correlations between seat positions and occupant injury severity by constructing a hierarchical ordered probit model. A total of 20,327 occupant injuries in 16,405 motor vehicle crashes in South Australia (2012 − 2016) are used. The results of this study indicate that the rear left passenger seat is associated with a 7.66% higher chance of getting injured (including moderate and severe injury), and the front left passenger seat is associated with a 2.94% higher chance of getting injured compared with the driver seat. Besides, the higher injury chances for other passenger seats including the rear right and rear middle seats are 4.97% and 4.74%, respectively, compared with the driver seat. Thus, this study offers passengers insightful suggestions about how to protect themselves by choosing the right passenger seat in a vehicle.

Assessment of tire failure related crashes and injury severity on a mountainous freeway: Bayesian binary logit approach

Although tires maintain the only contact between the vehicle and the ground, tire failures are still underrepresented in traffic safety assessments. Vehicle stability and safety can deteriorate significantly by a sudden tire failure. The current body of literature on tire failure-related crashes is limited, and no previous study was found to extensively investigate the factors associated with tire failures and the corresponding injury severity. The contributions of this study include (i) investigating the factors affecting tire failures, (ii) assessing the impacts of tire failures on occupant injury severity, and (iii) demonstrating the necessity of statewide tire inspection regulations. An extensive exploratory analysis was performed using ten years (2007−2016) of historical crash data along I-80 in Wyoming. Binary logistic regression with the Bayesian inference approach was applied to develop two separate models: tire failure and injury severity model. The results from the tire failure model showed that vehicle speeds greater than 75 mph, commercial motor vehicles, summer season, daytime, the presence of rough surface, downgrades, and concrete pavement are all related to higher tire failure occurrences. On the other hand, the incidence of a tire failure in a crash significantly contributed to more severe injuries when combined with any of the following instances: fire or explosion, rollover, guardrail hits, runoff road, angle, rear-end, clear weather, speeding, downgrades, and curved segments. With the incorporation of the random intercept in the modeling procedure, the injury severity analysis found a strong presence (42 %) of intra-crash correlation (effects of the common crash-specific unobserved factors) in occupant injury severity within the same crash. Finally, based on the findings of the study, recommendations are provided to alleviate tire-related problems.

Time to definitive care among severely injured farmers compared to other work-related injuries in a Midwestern state

BackgroundFarming is a high risk occupation that predisposes workers to injury, but may also lead to barriers in reaching trauma care. Little is known about emergency and trauma care for patients with farm-related injuries. The purpose of this study was to determine whether severely injured farmers presenting to a statewide trauma system faced delays in reaching definitive care compared to other severely injured workers.MethodsA population-based observational study was performed using the Iowa State Trauma Registry from 2005 to 2011. The registry was used to identify a multiply imputed sample of severe occupational injuries. Time to definitive care for farm- and non-farm-related injuries was compared using Kaplan-Meier curves and an extended, stratified Cox model censoring at 4 h. An interaction with time was included in the Cox model to generate hazard ratios for each hour after injury.ResultsSeven-hundred forty-eight severe occupational injuries were identified; 21% of these were farm-related. The overall median time to definitive care was nearly an hour longer for farmers compared to other workers (2h46m vs. 1h48m, p < 0.05). When adjusted for confounders, farm status remained a significant predictor of delay in reaching definitive care, but only in the first hour after injury (HR = 0.44, 95%CI = 0.24–0.83).ConclusionsFarm-related injuries accounted for more than 1 of every 5 severe occupational injuries entered into the Iowa trauma system. We found that severely injured farmers had delays in reaching definitive trauma care, even when adjusted for confounding variables such as rurality. This effect was most pronounced in the first hour.

Occupant injury severity in passenger car-truck collisions on interstate 80 in Wyoming: a Hamiltonian Monte Carlo Markov Chain Bayesian inference approach

This paper investigates the significant contributing factors affecting the injury severity of car drivers, car passengers, and truck occupants from the aspects of car-truck collisions along interstate 80 (I-80) in Wyoming using a binary logit model with a Bayesian inference approach. Fixed- and random-effects models were developed to examine the effects on severe and non-severe injuries. Results showed that the random-effects model provided a better fit to the data than the fixed-effects model. Parameter estimates were sampled from the posterior distributions using No-U-Turn Hamiltonian Monte Carlo (NUT HMC) sampling method because of its efficiency over other Monte Carlo Markov Chain (MCMC) techniques. The analysis showed that occupant behavior characteristics, such as impaired driving, alcohol or illegal drugs, fatigue, and dangerous driving significantly increased the probability of injury severity. Unlit conditions, inclement weather, challenging roadway geometry, presence of junctions, downgrades, and curve sections were also likely to lead to more severe occupant injuries. The significant finding of this paper concludes that car drivers are more responsible than truck drivers contributing more severe injuries in car-truck collisions. Findings from this study are expected to help WYDOT and other related agencies take necessary actions and decide on management strategies in reducing car-truck collisions.

Investigating occupant injury severity of truck-involved crashes based on vehicle types on a mountainous freeway: A hierarchical Bayesian random intercept approach

Earlier research on injury severity of truck-involved crashes focused primarily on single-truck and multi-vehicle crashes with truck involvement, or investigated truck-involved injury severity based on rural and urban locations, time of day variations, lighting conditions, roadway classification, and weather conditions. However, the impact of different vehicle-truck collisions on corresponding occupant injury severity is lacking. Therefore, this paper advances the current research by undertaking an extensive assessment of the occupant injury severity in truck-involved crashes based on vehicle types (i.e., single-truck, truck-car, truck-SUV/pickup, and truck-truck), and identifies the major occupant-, crash-, and geometric-related contributing factors. A series of log-likelihood ratio tests were conducted to justify that separate model by vehicle and occupant types are warranted. Injury severity models were developed using 10 years of crash data (2007–2016) on I-80 in Wyoming through binary logistic modeling with a Bayesian inference approach. The modeling results indicated that there were significant differences between the influences of a variety of variables on the injury severities when the truck-involved crashes are broken down by vehicle types and separated by occupant types. The age and gender of occupants, truck driver occupation, driver residency, sideswipes, presence of junctions, downgrades, curves, and weather conditions were found to have significantly different impacts on the occupant injury severity in different vehicle-truck crashes. Finally, with the incorporation of the random intercept in the modeling procedure, the presence of intra-crash and intra-vehicle correlations (effects of the common crash- and vehicle-specific unobserved factors) in injury severities were identified among persons within the same crash and same vehicle.

Analysis of factors affecting injury severity for riders or occupants of all-terrain vehicles and golf carts involved in police-reported crashes

In recent years, the popularity of all-terrain vehicles (ATVs) in the United States (US) has increased, and the number of ATV driver and passenger deaths have also increased substantially in the last few decades. Riders or occupants of ATVs as well as golf carts are particularly vulnerable to injury, not only due to the lack of protection and safety equipment offered by their vehicles, but also the propensity for ejection in the event of a crash. Given the vulnerability of these road users, it’s critical to understand factors which may affect injury severity to plan effective countermeasures aimed at reducing these injuries and fatalities. To better understand factors affecting the injury severity of ATV and golf cart riders or occupants involved in police-reported crashes, this study presents an analysis using six years of crash data from the US state of Arizona. Over the analysis period, there were 1769 drivers/passengers of these vehicle types involved in police-reported crashes. Of these occupants/riders, 67.7% were injured or killed as a result of the crash; a proportion significantly higher than police-reported crashes involving most other vehicle types, exhibiting the need to examine factors leading to these injuries and fatalities. In order to analyse factors affecting the injury severity of ATV and golf cart occupants/riders, a random parameters (RP) ordered logit statistical model was developed, which was most appropriate given the ordered nature of injury-severity data. Several person- vehicle- roadway- and environmental-related variables were found to significantly affect the injury severity of riders or occupants of ATVs and golf carts. Given the vulnerability of these road users, it’s important for transportation agencies to explore effective countermeasures aimed at reducing the severity of crashes involving these vehicle types. The results of this study provide important insights which can assist in developing effective engineering-, enforcement-, education, or policy-related countermeasures.

Modeling Vehicle Collision Injury Severity Involving Distracted Driving: Assessing the Effects of Land Use and Built Environment

This paper presents the findings of the vehicle occupant injury severity model, particularly focusing on the collisions involving distracted driving. The study develops a latent segmentation-based logit model for analyzing crash injury severity utilizing police-reported collision data from 2007 to 2011 in Nova Scotia, Canada. A segment allocation model is estimated to capture latent heterogeneity based on individual victims’ and drivers’ profiles, and collision attributes including vehicle type, vehicle trajectory, collision object, and collision type. The segment allocation model results suggest the existence of high-risk and low-risk injury severity segments. This study extensively tests the effects of built environment characteristics. The model results suggest that rain, curved road, freeway, and mid-block collisions aggravate vehicle occupant injury severity; whereas, higher land use mix, longer length of sidewalk, and higher population density mitigate injury severity. Significant heterogeneity is found across the high- and low-risk segments. For instance, straight road alignment is found to yield higher injury severity in the high-risk segment and lower severity in the low-risk segment. Moreover, the model unveils the interplay between built environment and distraction type. Driver distraction by communication device increases injury severity at a curved road intersection. Additionally, distraction because of inattentiveness increases injury severity. The findings of this study assist road safety engineers and planners to identify effective countermeasures and awareness programs for reducing the crash injury severity or consequences for vehicle occupants.

Analysis of the severity of occupational injuries in the mining industry using a Bayesian network.

OBJECTIVESOccupational injuries are known to be the main adverse outcome of occupational accidents. The purpose of the current study was to identify control strategies to reduce the severity of occupational injuries in the mining industry using Bayesian network (BN) analysis.METHODSThe BN structure was created using a focus group technique. Data on 425 mining accidents was collected, and the required information was extracted. The expectation-maximization algorithm was used to estimate the conditional probability tables. Belief updating was used to determine which factors had the greatest effect on severity of accidents.RESULTSBased on sensitivity analyses of the BN, training, type of accident, and activity type of workers were the most important factors influencing the severity of accidents. Of individual factors, workers’ experience had the strongest influence on the severity of accidents.CONCLUSIONSAmong the examined factors, safety training was the most important factor influencing the severity of accidents. Organizations may be able to reduce the severity of occupational injuries by holding safety training courses prepared based on the activity type of workers.

Occupant Injury Severity in General Aviation Accidents Involving Excessive Landing Airspeed.

BACKGROUND: Of all phases of flight operations, accidents during landings are the most frequent. Of these, poor speed management during landing has ramifications for injury severity since: 1) impact forces increase as a square of forward velocity; and 2) an aerodynamic stall, associated with inadequate landing speed, imparts high vertical G forces. Herein, the proportion of landing accidents involving deficient airspeed control and occupant injury severity was determined.METHODS: General aviation landing accidents (1997-2016) were identified from the NTSB database. An accident involving high-airspeed (high-energy) was one for which the NTSB cited airplane porpoising, multiple bounces, or floating, whereas an inadequate airspeed related (low energy) mishap was one citing this term or in which an aerodynamic stall occurred. An anonymous online survey of certificated pilots was used to inform landing technique. Statistical analyses used Poisson distribution and Chi-squared tests.RESULTS: Relative to the earliest period (1997-2001), the landing accident rate was undiminished for more recent years (2007-2016). Of 235 accidents, 38% involved high-energy, whereas 4% were inadequate airspeed-related. For the former, 17% resulted in occupants with fatal-serious injuries, twofold higher than for mishaps with no evidence of mis-speed. Of 1392 survey respondents, 73% selected a landing airspeed higher than required for an under-maximum weight airplane.CONCLUSION: For landing accidents involving airspeed mismanagement, those related to excessive energy predominate and are associated with more severe injuries. Two mitigating strategies are advanced: 1) pilot training should discuss landing airspeed adjustment for aircraft weight; and 2) installation of inflatable restraints for reducing injury severity should be encouraged.Boyd DD. Occupant injury severity in general aviation accidents involving excessive landing airspeed. Aerosp Med Hum Perform. 2019; 90(4):355-361.

Exploring Crash Injury Severity on Urban Motorways by Applying Finite Mixture Models

Abstract The effective treatment of crashes is a major concern to societies due to losses in human lives and the economic and social costs. Crash injury severity is one of the most well-known and well researched aspects of crashes in the science of road safety, with many study types across different countries and sample frames. All types of severity analyses primarily aim to determine the contributing factors for increased crash or injury severity and the respective degree of said contributions. Usually, driver and passenger-related factors are examined, as well as road environment characteristics such as geometric design aspects, traffic conditions, vehicle type and technology and weather conditions, as investigated in past literature. However, crash injury severity has not been adequately explored as the majority of relative research has a focus on analyzing crash likelihood, i.e. the probability of a crash to occur. Thus, this study aims to add to current knowledge by examining the determinants behind injury severity of occupants involved in crashes in the urban motorway Attica Tollway (‘Attiki odos’) which lies in the Greater Athens Area in Greece. In order to account or the unobserved heterogeneity, crash injury severity is explored by utilizing finite mixture logit models. Results indicate that a number of traffic parameters such as truck proportion, average flow and standard deviation of occupancy, as well as other risk factors, such as accident type and engine size have a significant effect on the injury severity outcome of vehicle occupants. Moreover, this model accounted for the heterogeneity among two distinct groups of observations. More research is needed in order to incorporate also vulnerable road users such as motorcyclists, cyclists and pedestrians and further investigate the impact of real-time traffic and weather parameters.