Vehicle-related Factors Research Articles

Magnitude and determinants of road traffic accidents in North Gondar Zone; Amhara Region, Ethiopia

Background: Despite efforts that the government has made to minimize the number of casualties or injury levels in Ethiopia, road traffic accidents remain a severe health concern throughout the country and in the study area as well. This study aims to examine the primary contributing variables of road traffic accidents in the North Gondar zone, under 24 administrative. Methods: The functional class of roads were directly examined on the site to evaluate the existing conditions of the roads which includes the geometric design constraints, functional class, daily traffic, and surfacing materials. Secondary source of data types, previously recorded road traffic accident information was collected from North Gondar zone traffic police departments. Road inventory information was collected from Gondar branch Ethiopian road authority under the specified horizon lines. Results: The magnitude and frequency of traffic accidents caused by driver-related factors( age, sex, level of education, driving experience, ownership responsibility of a vehicle and driver responsibility, time and day-wise distribution of traffic accidents), vehicle-related factors (failures of mechanical parts and its service age), road related factors( geometric design and construction, inventory information, road surfacing materials), pedestrians, and environmental factors were critically analysed. The result shows that a higher proportion (67%) of traffic accidents are mainly due to driver behaviour related factors than other causative factors. Conclusions: According to the results of the study, bad driving behaviour’s (exceeding the design speed, using a phone while driving, dangerous overtaking, excessive braking, loading above capacities, illegally parking out of public stations should be controlled to reduce the road traffic accident in the study area. In addition, successive up-to-date advanced computerized traffic accident safety training and recording systems should be given at least once a year through regional-based or at a central zone administrative zones for the traffic officers.

A Cluster-Based Approach for Analysis of Injury Severity in Interstate Crashes Involving Large Trucks

The significance of large trucks for the expansion and well-being of the economy is a well-established fact. However, crashes involving large trucks significantly threaten the overall safety on the roads. Moreover, a significant proportion of fatal crashes involving large trucks occurs on interstate roadways in the United States. However, not many studies have focused on the heterogeneous effects of the contributory factors on injury outcomes of interstate crashes involving large trucks. The current study explores the application of a k-prototypes clustering-based mixed logit model to identify and analyze the heterogeneous effects of contributory factors on injury outcomes in different scenarios of interstate crashes involving large trucks. Data from six years of crashes involving large trucks that occurred on interstate roadways in the state of Pennsylvania, US, were used in this study. The list of contributory factors included the following: drivers’ demographics and behaviors; crash characteristics; vehicle-related factors; location and roadway attributes; and environmental factors. The results indicated that some of the contributory factors were significant for all scenarios of interstate crashes involving large trucks. However, the magnitude of those factors’ effects varied across scenarios. Moreover, some of the contributory factors were exclusive to certain scenarios of interstate crashes involving large trucks. Lastly, the identification of random parameters in the cluster-based models indicated that a cluster-based mixed logit model is a more effective approach for accurately estimating the effects of contributory factors on injury outcomes in large-truck interstate crashes. The empirical findings of this study can be used to develop more robust traffic laws and safety measures to reduce the frequency and severity of injury in different scenarios of interstate crashes involving large trucks.

An exploratory study of a law enforcement model for vehicle speed in Tshwane Metropolitan Municipality, South Africa

This study explores the law enforcement model for vehicle speed in Tshwane municipality of South Africa. South Africa has comparatively high road traffic crashes and casualty rates. Most of these crashes are associated with illegal speeding. This study was designed against the background of protracted road traffic crashes the triple causative factors as well as existing traffic law enforcement systems and processes aimed at managing illegal speeding. Data were collected from a sample of 28 senior law enforcement officials from the Tshwane Metropolitan Police Department (TMPD) traffic division, the Gauteng Traffic Police and the National Traffic Police. Qualitative data were analysed using thematic analysis’ open and axial coding processes on Atlas.ti.8. In its findings, the study developed a model that shows that illegal speeding and consequential road crashes were caused by an interaction of multiple factors. These were poor human behavioural elements as the main factor, road-environment factors and vehicle-related factors in the presence of inadequate and uncoordinated traffic law enforcement, weak penal systems for offenders, corruption, poor driver training and low road safety education.

Impact of vehicle-related factors on speed selection and accident severity rates

Impact of vehicle-related factors on speed selection and accident severity rates

Impact of vehicle-related factors on speed selection and accident severity rates

Impact of vehicle-related factors on speed selection and accident severity rates

Prevalence of and Factors Associated with Driving a Vehicle with Timed-Out Inspection Certificate in Spain.

This study aimed to estimate the prevalence of vehicles on the road with a timed-out vehicle inspection certificate (TOVIC) and the associations of driver, vehicle, and environmental factors with this infraction. A quasi-induced exposure approach was used in this cross-sectional study to analyze a case series comprising 51,305 drivers passively involved in clean collisions (only one infractor driver involved) between two or more vehicles registered in the Spanish National Register of Road Crashes with Victims from 2014 to 2017. The prevalence of TOVIC was estimated in the whole sample and in subgroups defined by the variables considered. Multivariate logistic regression modeling was used to obtain adjusted odds ratios for the association between TOVIC and each category of the variables. The prevalence of TOVIC was low, although significant differences were found for certain subcategories of drivers, vehicles, and environmental factors. Significant positive adjusted associations were found between TOVIC and license-related infractions, vans (compared to cars), vehicle age, and vehicle defects. Several vehicle-related factors potentially associated with a high risk of involvement in a crash were clearly related with TOVIC, which suggests the need for measures to control this non-negligible number of high-risk vehicles on the road.

Characterizing the literature surrounding transportation of young dairy calves: A scoping review

Transportation is a stressful event for cattle, as it may involve various handling practices, commingling, deprivation of food and water, and fluctuating temperatures. Calves are particularly susceptible to these stressors because their physiological and immune systems are still developing. There has been no formal synthesis of the scientific literature evaluating the effect of transportation on young dairy calf health and performance; the aim of this scoping review is to describe and characterize this body of work. We targeted both descriptive and analytic studies examining transport of calves, including listing how the effect of transport has been evaluated. Eight databases were searched for relevant articles with eligible studies being primary research articles investigating transportation of calves of either sex who were younger than 60 d of age or weighed less than 100 kg. Two reviewers independently screened the title and abstracts of 6,859 articles with 361 potentially relevant articles screened at full text. Of these, 46 were relevant and had data extracted. Articles reporting study location were conducted in the United States (n = 5), Australia (n = 3), Japan (n = 3), and New Zealand (n = 3). Common transport-related variables evaluated included time in transit (n = 13), distance of transportation (n = 8), vehicle-related factors (n = 8), and age at time of transportation (n = 4). Outcome measures varied greatly, including blood parameters (n = 28), health assessments (n = 20), weight (n = 17), behavioral metrics (n = 14), mortality (n = 7), feed intake following transportation (n = 4), salivary cortisol concentrations (n = 3), morbidity (n = 3), and isolation of Salmonella Dublin in fecal samples (n = 2). Outcome parameters were measured during transport or ranged from immediately after to one year following transportation. As the transport-related risk factors and outcomes measured assessed varied widely between studies, future quantitative synthesis (e.g., meta-analysis) in this area may be limited. Several knowledge gaps were identified, including methods to prepare calves for transportation, such as improving nutrition, administering medication, or transporting calves at an older age or weight. Further research could also focus on consistent and clear reporting of key items related to study conduct and analysis, as well as the development of a core outcome set for calf transport studies.

Risk Factors for Road Traffic Injury-Related Mortality in Iran; a Systematic Review and Meta-Analysis.

Introduction:Gathering information regarding the risk factors of mortality and disability due to road traffic injuries can provide evidence for adopting effective interventions to reduce the burden of the injury. Therefore, the present study intends to identify the most important risk factors of road accident-related mortality in Iran by conducting a systematic review and meta-analysis. Methods:Search was done in English and Persian electronic databases, for articles published until the end of 2020. Cross-sectional, cohort and case-control studies were included. Risk factors were divided into age and sex, road related factors, exceeding speed limit, road user behaviors, vehicle related factors, weather condition, and light condition. Data were reported as adjusted odds ratio (OR) of death with 95% confidence interval (95% CI). Results:20 studies were included (2,682,434 traffic accident victims and 23,272 deaths; mortality rate=1.28%). The risk of death in road traffic injuries in men was 1.66 times higher than women (OR = 1.66; 95% CI: 1.03, 2.68) and with each year increase in age, the risk increased by 1% (OR = 0.01; 95% CI: 1.00, 1.01). In addition, accident in urban streets (OR = 1.76; 95% CI: 1.08, 2.88), roadway defects (OR = 2.15, 95% CI: 1.59, 2.91), and not driving on a flat and straight road (OR = 1.60; 95% CI: 1.14, 2.24) were the most important road-related risk factors for mortality. Exceeding the speed limit was another risk factor of death (OR = 3.16; 95% CI: 2.83, 3.54). However, regarding exceeding safe speed, only three studies have been included, which greatly reduces the power of analysis. Not maintaining focus on the road (OR = 2.99; 95% CI: 1.49, 6.04), not fastening seatbelt (OR = 3.11; 95% CI: 1.08, 8.91), and reckless overtaking (OR = 4.04; 95% CI: 3.34, 4.89) were independent road user-related risk factors for mortality. Risk of pedestrian mortality in comparison with drivers and passengers is 2.07 times higher (OR = 2.07; 95% CI: 1.53, 2.58). In addition, risk of death in accidents occurring during daylight hours (OR = 0.26; 95% CI: 0.18, 0.37) is lower than that of other hours. No significant relationship was present between mortality and vehicle types (four-wheeled vehicle: OR = 0.99; 95% CI: 0.050, 1.97; two-wheeled vehicle: OR = 0.75; 95% CI: 0.48, 1.16). In the case of vehicle-related factors, only 2 studies were included, which also dealt only with the type of vehicle (two-wheeled/four-wheeled). Vehicle-related factors such as the car model, its safety rating, and safety standards were not mentioned in any study. Conclusion:Low to very low-level evidence shows that there is a significant relationship between factors related to age, sex, road, road user, exceeding the speed limit, and light condition with the mortality of traffic accident victims. However, all studies included in the present study were retrospectively designed and the analyses were not adjusted for most of the key potential confounders. Therefore, it seems that despite years of effort by researchers in the field of traffic accidents in Iran, there is still no comprehensive and reliable picture of the most important risk factors for road accident mortalities in Iran.

Heat Waves and Road Traffic Collisions in Alabama, United States

The effects of heat waves on traffic collisions require investigation to improve traffic safety during extreme heat events. A time-stratified case-crossover design was used to examine associations between heat waves and traffic collisions in Alabama between May and September from 2009 to 2018. We derived a heat wave index, defined as the daily mean temperature greater than the 95th percentile for two or more consecutive days, by meteorological data from Phase 2 of the North American Land Data Assimilation System. We obtained traffic collision records from the Alabama Department of Transportation. A nonsignificant and negative association between traffic collisions and heat waves was noted, with a 1.4 percent decrease (95 percent confidence interval [CI] [−3.1 percent, 0.4 percent]) in traffic collisions on heat wave days compared to non–heat wave days. Similar results were found when the analysis was stratified by driver-related factors (i.e., gender, age, race, employment status, and driver residence distances), vehicle-related factors (i.e., vehicle usage), and collision-related factors (i.e., rural or urban roads, speed limits, and intersections). A significant and positive association was observed on heat wave days without precipitation, however (23.5 percent increase; 95 percent CI [7.3 percent, 42.3 percent]). In conclusion, traffic collisions were not associated with heat waves in many collision-related conditions in Alabama.

Influence of Weather Features in Determining Sudden Braking

Understanding conditions and situations causing abnormal driving behaviors like sudden braking or sudden acceleration is important for preventing traffic accidents. Previous studies have used probe vehicle data to detect risky situations where sudden braking frequently occurred. However, they have mainly focused on location and vehicle-related factors. In this paper, we build models which discriminate sudden braking using a machine learning method. The models use weather-related information as well as probe data. To investigate how weather-related factors help to determine sudden braking, we conducted extensive experiments using probe data obtained from dashboard cameras and two types of weather-related information obtained from meteorological observatories (MO) and AMeDAS. Experimental results illustrate that using weather-related information improves performance in determining sudden braking and that the temporally and spatially denser characteristics of weather-related factors from AMeDAS help to compensate for insufficiencies in the model with MO data.

Evaluation of Most Influential Factors Affecting Road Traffic Accidents in Sidon, Lebanon

Enlargement of the transportation industry in developing countries has had a tremendous impact on the advancement of those countries. This advancement has had both positive and negative impacts on society. Drawbacks primarily include the emission of harmful gases from vehicles, traffic overcrowding, and most importantly, injuries, and deaths by traffic accidents, which have increased significantly. This paper focuses on and investigates the human, vehicle and environmental factors affecting road traffic accidents in Lebanon in order to increase the knowledge surrounding how the elements have impacted the increase in accidents in Lebanon. A quantitative method (questionnaire) was used to collect data to understand the behavior of road users, while the relative importance index (RII) was used to analyze the data. The results illustrate that the top three vehicle-related factors that lead to an increase in the accidents in Lebanon according to the respondents are steering wheel defects with an RII of 3.807, mechanical faults with an RII of 3.489 and 2.934 for lack of vehicles among all the groups. Furthermore, the human factors were recognized to have a slight impact on road traffic accidents with the highest RII of 1.719 for failure to follow driving codes. In conclusion, a country’s development not only depends on economic measure, but it is also determined based on how effective the traffic system infrastructure is and the steps taken to minimize injuries.

Developing artificial neural networks to estimate real-time onboard bus ride comfort

The ride comfort of bus passengers is a critical factor that is recognised to attract greater ridership towards a sustainable public transport system. However, it is challenging to estimate bus passenger comfort onboard while travelling due to the complex non-linear interaction among various factors. A practicable method to collect real-time comfort ratings by passengers is also not readily available. This study developed an artificial neural network (ANN) model with three layers to precisely estimate real-time ride comfort of bus passengers. The inputs are vehicle-related parameters (speed, acceleration and jerk), passenger-related features (posture, location, facing, gender, age, weight and height), ride comfort index in ISO 2631-1997 (vibration dose value and maximum transient vibration value), and output is passenger rating (collected from a specialised mobile application). The ANN model provided a satisfactory performance and good correlation between inputs and output with an average MSE = 0.03 and R-value = 0.83, respectively. Sensitivity analysis was also conducted to quantify the relative contribution of each variable in the ANN model, revealing similar contributions among all influencing factors in the range of 4–6%. On average, passenger-related factors contribute slightly higher than vehicle-related factors to the ride comfort estimation based on the connection weight approach. The development of ANN model which can precisely estimate bus ride comfort is important as a considerable amount of machine learning and artificial intelligence are utilised to guide autonomous bus (AB). The present findings can help AB designers and engineers in improving AB technology to achieve a higher level of passengers’ onboard comfort.

Evaluation of Most Influential Factors Affecting Road Traffic Accidents in Sidon, Lebanon

Enlargement of the transportation industry in developing countries has had a tremendous impact on the advancement of those countries. This advancement has had both positive and negative impacts on society. Drawbacks primarily include the emission of harmful gases from vehicles, traffic overcrowding, and most importantly, injuries, and deaths by traffic accidents, which have increased significantly. This paper focuses on and investigates the human, vehicle and environmental factors affecting road traffic accidents in Lebanon in order to increase the knowledge surrounding how the elements have impacted the increase in accidents in Lebanon. A quantitative method (questionnaire) was used to collect data to understand the behavior of road users, while the relative importance index (RII) was used to analyze the data. The results illustrate that the top three vehicle-related factors that lead to an increase in the accidents in Lebanon according to the respondents are steering wheel defects with an RII of 3.807, mechanical faults with an RII of 3.489 and 2.934 for lack of vehicles among all the groups. Furthermore, the human factors were recognized to have a slight impact on road traffic accidents with the highest RII of 1.719 for failure to follow driving codes. In conclusion, a country’s development not only depends on economic measure, but it is also determined based on how effective the traffic system infrastructure is and the steps taken to minimize injuries.

A Crash Severity Prediction Method Based on Improved Neural Network and Factor Analysis

Crash severity prediction has been raised as a key problem in traffic accident studies. Thus, to progress in this area, in this study, a thorough artificial neural network combined with an improved metaheuristic algorithm was developed and tested in terms of its structure, training function, factor analysis, and comparative results. Data from I5, an interstate highway in the Washington State during the period of 2011–2015, were used for fitting and prediction, and after setting the theoretical three-layer neural network (NN), an improved Particle Swarm Optimization (PSO) method with adaptive inertial weight was offered to optimize the NN, and finally, a comparison among different adaptive strategies was conducted. The results showed that although the algorithms produced almost the same accuracy in their predictions, a backpropagation method combined with a nonlinear inertial weight setting in PSO produced fast global and accurate local optimal searching, thereby demonstrating a better understanding of the entire model explanation, which could best fit the model, and at last, the factor analysis showed that non-road-related factors, particularly vehicle-related factors, are more important than road-related variables. The method developed in this study can be applied to a big data analysis of traffic accidents and be used as a fast-useful tool for policy makers and traffic safety researchers.

Managing Road Traffic Accidents Using a Systems Approach: Case of Botswana - Empirical Review.

Road traffic accidents are a menace to human deaths and a single life lost on Botswana roads is one too many. The road traffic injuries have long been considered to be inevitable and caused by random, unpredictable events; documented success stories in road safety are needed to demonstrate that road traffic accidents need not be inevitable and unpredictable, but are avoidable. The systems approach to road traffic accidents (RTAs) acknowledges that there are three factors that contribute to road accidents and these are; road users, vehicles and road system. Specifically, the C-3 systems approach with three phases (creation, cultivation, and conduct) was adopted as a theoretical framework underpinning the study. The major findings from this study indicates that most RTAs are caused by road users through speeding, unlicensed driving, using cell phones whilst driving, alcohol and drug abuse, bad state of mind and healthy, non-use of safety belts and deliberate failure to observe road regulations amongst others. The findings also indicate that mechanically faulty vehicles, unmaintained vehicles, old vehicles, and tyre blowouts are vehicle related factors causing RTAs. Road system conditions involve potholes, stray livestock and road design attributes amongst others. The study employed the desktop research approach. The study came up with a number of recommendations which are important to reduce RTAs and these include educating the public on safe driving habits at both basic education and tertiary education levels as well as punitive policies on road users breaking road traffic laws and regulations has been identified as another recommendation. Stringent measures must be taken against livestock owners who leave stock straying in highways and public roads. In addition, regular road maintenance and vehicle maintenance were found to be of paramount importance in reducing RTAs in Botswana. Key words : Road traffic accident, Casualty, Traffic safety, Botswana, Drunken driving, Systems approach DOI: 10.7176/JESD/11-10-21 Publication date: May 31 st 2020

Fatigue Risk Management: Assessing and Ranking the Factors Affecting the Degree of Fatigue and Sleepiness of Heavy-Vehicle Drivers Using TOPSIS and Statistical Analyses

This descriptive–analytic study identified the factors affecting the degree of fatigue and sleepiness of heavy-vehicle drivers, assessed their effects, and ranked them according to extent of influence by using statistical analysis and the technique for order of preference by similarity to ideal solution (TOPSIS). Data were collected through interviews guided by a questionnaire, through which three main categories of factors that contribute to crashes caused by fatigue and sleepiness were discussed. These categories are (I) human, (II) road and environmental conditions, and (III) vehicle-related factors. The results showed that human and road and environmental conditions exert the strongest and weakest effects, respectively. The statistical and TOPSIS results revealed that the first four factors that exert the strongest effects are inappropriate behaviors of passengers and goods owners, non-standard roads, inappropriate behaviors of police, and economic problems of heavy-vehicle drivers.

Environmental Related Risk Factors to Road Traffic Accidents in Ilam, Iran.

Introduction:Road traffic injuries (RTIs) are the most serious health issue in the world and the main cause of death after the first year of birth. Environmental factors are among the most important and effective aspects of RTIs occurrences and their human consequences.Aim:This study aimed to identify the environmental factors associated with road traffic accidents (RTAs) in Ilam province.Methods:All crash data in Ilam province in 2012 were investigated. RTI’s data was collected using COM 114 form which included some human, environmental and vehicle related factors. Through Chi-Square test and logistic regression model, the association between environmental factors and RTIs was examined. For all statistical analyses we used the package Stata 13.1.Results:A total of 2314 traffic accidents were investigated. The highest 1659 (72.3%) and lowest 19 (0.83%) frequency of RTAs occurred during day and sunrise respectively. The majority of RTAs took place in July and September. The maximum number of RTAs was when the day was clear (91.7%) 2103 and the minimum when it was cloudy (3.5%) 81. A significant relationship was found between fatal RTAs and factors such as; the sort of the road, the hindered visibility, the location of the accident, the accidents’ place, the climate, and lighting of the day (P<0.05). The adjusted chance of traffic accidents causing death or injuries in main streets was 9.7 times more than in highways; in sidetracks it was 3.54 times more. And when it was cloudy, the chance was 2.60 times more than when was clear (P<0.05).Conclusion:Regarding the standards of road construction, development of the roads, and educating drivers how to adjust their driving behavior to the environment and road conditions could have a great role in decreasing RTAs causing death or injury.

Characteristics and Profiles of Moped Crashes in Urban Areas: An In-Depth Study

In this in-depth study, 167 severe injury crashes involving a moped in urban areas in Belgium in 2013 are analyzed. The study provides an overview of the general crash characteristics of and contributing factors to these moped crashes, and identifies typical crash profiles and their characteristics. The representation of class A mopeds (maximum speed 25 km/h [16 mph]; no driving license needed) and class B mopeds (maximum speed 45 km/h [28 mph]; driving license required) in these crashes is approximately fifty-fifty, which seems to imply an over-representation of mopeds class A. The involved moped riders have an average age of 33 and three-quarters are men. Human factors are by far the largest category of contributing factors in moped crashes, factors related to infrastructure and environment play a moderate role, and vehicle-related factors play only a minor role. The most important human factors are psychological factors and risk assessment errors, the main non-human contributing factors are sight obstructions owing to infrastructure or other vehicles. The following crash profiles, grouping similar types of crashes, were identified: crashes between a vehicle turning off and a moped going straight through (18%); crashes caused by the moped rider’s risky behavior (17%); crashes between a moped and another vulnerable road user (13%); crashes resulting from entering or exiting destinations alongside the road (12%); crashes at intersections (other than vehicles turning off) (11%); single-vehicle crashes with loss of control (9%); crashes caused by an error during overtaking (8%); rear-end crashes (3%); and other (9%).

Vehicle Safety Analysis based on a Hybrid Approach Integrating DEMATEL, ANP and ER

Vehicle related safety is the principal focus of many agencies and organizations targeting road safety issues. There are many conflicting factors contributing to safety in motor vehicle operations. Unfortunately, the lack of reliable crash data makes it extremely difficult to evaluate the crash causations associated with defective vehicles. Furthermore, clarifying the interrelationships among vehicle related factors causing accidents is very complicated when the conventional decision techniques are used. This study aims to employ a hybrid decision method based on DEMATEL (decision-making trial and evaluation laboratory) and ANP (Analytic Network Process) to construct an Influence Relations Map (IRM) as well as obtain the influential weight of vehicle-related criteria. An empirical case study is provided to demonstrate the implementation process. This paper also employs an Evidential Reasoning algorithm (so-called ER) for assessing the inter-city buses in terms of vehicle safety in order to deal with the uncertainties associated with incomplete input data. The ER improves decision making process by using a belief decision matrix and the Demspster-Shafer theory of evidence. The results and findings reveal that the braking system is the most important criterion impacting vehicle safety, followed by fuel supply and electrical systems.

Epidemiology of Multiple Casualty Incidents from Road Accidents in Thailand, 2006-2011

Investigations that focus on multiple casualty incidents (MCIs) can identify factors which can determine a very high burden of injury and death. Five MCI investigations were carried out in Thailand using surveillance data, physical investigation data from the scenes and vehicles, in-depth interviews with survivors and witnesses, and extraction of medical information from hospital records. Haddon’s matrix was utilized to structure results in three phases (pre-event, event and post-event) which were stratified into four agents (human, vehicle, physical and socio-economic environment). The five MCIs involved 113 people, nine pickup trucks, four sidecar motorcycles and one each of a car, bus, prime mover truck and prime mover truck with a flatbed semi-trailer. Ten (8.8%) people died and 81 (71.7%) people were injured. Many amenable risk factors were human-related (inexperience, falling sleep, dangerous driving, non-use of seat belts, riding in the cargo area or on the rear platform). Vehicle-related factors were also present (poor tire treads and lack of safety devices), and environmental factors were prominent (wet and slippery roads, poor signage and lighting). Other notable facts included delay in contacting emergency services, lack of cutting equipment and limited first aid support. Many modifiable risk factors were identified, highlighting the need to reform roads and vehicles, and educate passengers and drivers.