Modelling the Injury Severity of Heavy Vehicle Crashes in Australia

Vajari, Mohammad Abrari; Aghabayk, Kayvan; Sadeghian, Mohammad; Moridpour, Sara

doi:10.1007/s40996-021-00673-0

Cited by 9 publications

(7 citation statements)

References 56 publications

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“…A study using data on large truck crashes in Los Angeles over 7 years examined the effect of time-of-day and periods on resulting injury severities in large-truck crashes. In Australia, the finding of a multinomial logit model showed that factors such as occupant variables (e.g., driver age and gender), collision characteristics (e.g., collisions with fixed objects and truck overturns), temporal characteristics (e.g., early morning, midnight), spatial characteristics (e.g., urban or rural areas), environmental factors (e.g., lights condition) increase the probability of fatal/serious injuries in heavy vehicle crashes [15]. In another study, two regression models were developed to study both the maximum injury severity from a crash (over all involved individuals) and the maximum injury severity for occupants in all involved vehicles.…”

Section: Previous Literaturementioning

confidence: 99%

Investigation of Factors Associated with Heavy Vehicle Crashes in Iran (Tehran–Qazvin Freeway)

2023

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With the growing demand for transportation and cargo between cities, the proportion of heavy vehicles in freeway traffic has been increasing in Iran and worldwide during the past decade. The impact of heavy vehicles on crash severity has long been a concern in the crash analysis literature for the prevalence of crashes in freeway traffic. The purpose of this study is to investigate the contribution of heavy vehicles to freeway crashes and uncover other causal factors. Using the comprehensive crash and traffic data from the Qazvin–Tehran freeway in Iran, from 2013 to 2018, 1350 crashes involving heavy vehicles were extracted regarding the weather conditions, weekday, main cause of the crash, driver gender, and culprit side. Considering crash severity calculation, the applied coefficient weights in this study for a person were considered as 3 for an accident resulting in injury and 5 for a fatal crash. A binary logit model was estimated using the data to determine if there was a significant correlation between recognized factors and the likelihood of the crash. The logit modeling results clearly illustrate important relationships between various risk factors and occupant injury, in which heavy vehicles were recognized as one of the most important factors in this study. Other variables associated with crash severity were weather conditions and driver attention. Results indicate that the number of crashes is simultaneously dependent on the total vehicle volume and average speed of heavy vehicles.

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Section: Previous Literaturementioning

confidence: 99%

Investigation of Factors Associated with Heavy Vehicle Crashes in Iran (Tehran–Qazvin Freeway)

2023

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“…We divided the age group into three categories, as in previous classifcations [40,68]: under 26 years old, between 26 and 59 years old, and above 59 years old. Age is linked to a person's physical characteristics, as well as their reaction times, risk-taking behavior, and other factors that may impact the severity of an injury.…”

Section: Age Groupmentioning

confidence: 99%

“…Based on earlier research [68], we classifed the time period of the crash into fve groups based on the volume of trafc: early morning or midnight hours (00: 00-6:29 A.M), morning peak hours (6:30−8:59 A.M), day ofpeak hours (9:00 A.M-14:59 P.M), evening peak hours (15: 00-18:29 P.M), and night of-peak hours (18:30−23:59 P.M).…”

Section: Time Of the Daymentioning

confidence: 99%

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Modeling the Motorcycle Crash Severity on Nonintersection Urban Roadways in the Australian State of Victoria Using a Random Parameters Logit Model

Seyfi

Aghabayk

Mamaghan

et al. 2023

Journal of Advanced Transportation

Self Cite

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Due to a lack of physical protection and balance, motorcycle riders are one of the most vulnerable road users and are more likely to suffer severe injuries than motorists. Between 2009 and 2020, about 60% of motorcycle crashes occurred on nonintersection urban roadways in Victoria, Australia. While considerable research on intersections and their influence on the severity of motorcycle crashes has been conducted, there are limited studies on motorcycle crashes on nonintersection roadways. Since gathering all information from every motorcycle crash may not be possible, heterogeneity can arise from unobserved factors and cause problems in developing reliable crash severity models. Therefore, this study aims to investigate the factors contributing to motorcycle crash severity on Victorian nonintersection urban roadways while considering the heterogeneity of factors. A total of 10,897 nonintersection motorcycles crash data from the beginning of 2009 to November 2020 in the State of Victoria, Australia, were analyzed. A random parameters (mixed) logit model (RPL) was used for evaluating motorcycle crashes. The severity of motorcycle crashes was divided into three categories: fatal injury, serious injury, and minor injury. Also, marginal effects were calculated to see how each parameter estimate affects crash severity outcomes. The RPL model results showed that some factors increased the likelihood of fatal injuries. These factors included not wearing a helmet, being in the older rider age group, riding during the early morning or midnight hours, weekend motorcycle use, riding in the early morning or midnight hours (00:00–6:29 A.M), and insufficient lighting (dark and dusk/dawn). Also, the following factors enhanced the probability of serious injuries: having a pillion passenger, having a motorcycle age of more than 7 years, riding at higher speed limits (more than 50 km/h) or during peak hours in the morning (6:30−8:59 A.M), and being in the younger age group (less than 26 years old). The findings from this study are valuable resources for road safety policy managers to develop effective strategies for improving motorcyclists’ safety at nonintersections. This may include improving the light conditions at nonintersection, encouraging the motorcyclist to maintain motorcycles regularly, and educating the motorcyclist to wear a helmet, avoid distractions, and ride responsibly on the weekends.

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“…Many studies had discussed and explained the injury severity among heavy vehicle drivers by either human, vehicle or environmental settings. This research either focused on specific types of heavy vehicles such as trucks [2][3][4][5][6][7][8][9][10][11][12][13][14] and buses [15,16] or considered all types of heavy vehicles in the data [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Classification of Driver Injury Severity for Accidents Involving Heavy Vehicles with Decision Tree and Random Forest

Azhar¹,

Ariff

Bakar

et al. 2022

Sustainability

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Accidents involving heavy vehicles are of significant concern as it poses a higher risk of fatality to both heavy vehicle drivers and other road users. This study is carried out based on the heavy vehicle crash data of 2014, extracted from the MIROS Road Accident and Analysis and Database System (M-ROADS). The main objective of this study is to identify significant variables associated with categories of injury severity as well as classify and predict heavy vehicle drivers’ injury severity in Malaysia using the classification and regression tree (CART) and random forest (RF) methods. Both CART and RF found that types of collision, driver errors, number of vehicles involved, driver’s age, lighting condition and types of heavy vehicle are significant factors in predicting the severity of heavy vehicle drivers’ injuries. Both models are comparable, but the RF classifier achieved slightly better accuracy. This study implies that the variables associated with categories of injury severity can be referred by road safety practitioners to plan for the best measures needed in reducing road fatalities, especially among heavy vehicle drivers.

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Modelling the Injury Severity of Heavy Vehicle Crashes in Australia

Cited by 9 publications

References 56 publications

Investigation of Factors Associated with Heavy Vehicle Crashes in Iran (Tehran–Qazvin Freeway)

Investigation of Factors Associated with Heavy Vehicle Crashes in Iran (Tehran–Qazvin Freeway)

Modeling the Motorcycle Crash Severity on Nonintersection Urban Roadways in the Australian State of Victoria Using a Random Parameters Logit Model

Classification of Driver Injury Severity for Accidents Involving Heavy Vehicles with Decision Tree and Random Forest

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