Injuries Associated with Auto-Tricycle Crashes in an African City: Incidence and Pattern

Purpose: The aim of this study was to determine the incidence and pattern of injuries resulting from auto-tricycle crashes among patients in a tertiary referral centre in Ghana. Methods: Data were retrospectively extracted from hospital records of patients who got involved in auto-tricycle crashes and presented to the Accident and Emergency Centre of the Komfo Anokye Teaching Hospital (KATH), over a one-year period using a structured questionnaire. The gathered data were then entered into an electronic database and then analysed with SPSS version 20.0. Results: The incidence of injury following auto-tricycle crashes over the one-year period was 5.9% (95% CI: 4.9% - 7.0%) with a case fatality rate (FR) of 3.8% (95% CI: 1.3% - 8.7%). All the mortalities resulted from head and neck injuries and none of the patients involved wore a crash helmet. Only 5% of those studied wore crash helmets and were all drivers. Closed fractures accounted for 58% of the injuries, followed by open fractures, 28%. The most commonly fractured bones were the tibia/fibula, followed by the femur and then radius/ulna. The most common mechanism of injury was auto-tricycle toppling over (29%). Passengers were the most injured (48%), followed by drivers (37%) and pedestrians (15%). Most (72%) injuries among participants involved a single body part. On the injury severity scale, most (61%) of patients had minor trauma and 38% had major trauma. Conclusion: Auto-tricycle crashes account for 5.9% of injuries at the study site with a case fatality rate of 3.8%. Passengers had a higher injury rate (48%) than drivers (37%). Fractures of the tibia/fibula were most commonly associated with auto-tricycle crashes. Injuries to the head and neck were responsible for the deaths in the study participants and non-use of a crash helmet was associated with mortalities.

A Multilayer Perceptron Artificial Neural Network Study of Fatal Road Traffic Crashes

This paper examines the relationship between fatal road traffic accidents and potential predictors using multilayer perceptron artificial neural network (MLANN) models. The initial analysis employed twelve potential predictors, including traffic volume, prevailing weather conditions, roadway characteristics and features, drivers’ age and gender, and number of lanes. Based on the output of the model and the variables’ importance factors, seven significant variables are identified and used for further analysis to improve the performance of models. The model is optimized by systematically changing the parameters, including the number of hidden layers and the activation function of both the hidden and output layers. The performances of the MLANN models are evaluated using the percentage of the achieved accuracy, R-squared, and Sum of Square Error (SSE) functions.

Traffic Flow Characteristics in Work Zone and Non-Work Zone Environment and Its Impact on Road Crashes at the Segment Level

This study investigates relationships between congestion and travel time performance metrics and crashes on road segments. The study focuses on work zone routes in Iowa, utilizing 2021 commercially-available probe vehicle data and crash data. Travel time performance metrics were derived from the probe vehicle data, and crash counts were obtained from the crash data. Additional variables included road characteristics (traffic volume, road type, segment length) and a categorical variable for the presence of a work zone. A mixed effect linear regression model was employed to identify relationships between road segment crash counts and the selected performance metrics. This was accomplished for two sets of models that include congestion performance measures at different defining threshold values, along with travel time performance measures. The study results indicate that the congestion indicators, certain travel time performance measures, and traffic counts were statistically significant and positively correlated with crash counts. Indicator variables for rural interstate locations and non-active work zones have a stronger influence on crash count than those for municipal interstate locations and active work zones. These findings can inform decision-makers on work zone safety strategies and crash mitigation planning, especially in high traffic volume areas prone to congestion and queues.

Understanding the potential key risk factors associated with teen driver crashes in the United States: a literature review

This article is a compilation of teen driver crash contributing factors typically extractable from the crash data collection system in the United States.Tremendous research effort has been undertaken over the decades to comprehend teen driver crash risks,as teen drivers continue to be over-involved in crashes even when accounting for the driving exposure.This article presents the contexts of crash factors related to operating conditions,roadway,vehicle,and driver and their unique influences on teen driver crashes in terms of estimated risk,prevalence,and estimated likelihood mainly from descriptive and analytical studies.The key variables are selected based on the number of studies that considered each risk factor for analysis.The understanding of crash factors could be translated into graduated driver licensing and other teen driver safety programs.While the discussions were grounded in crash studies carried out in the United States,the insights gleaned from these studies hold the potential to offer valuable guidance to other countries.For example,the insights and discussions can serve as a catalyst for the development and improvement of driver education programs tailored to address the specific requirements and difficulties confronted by their teenage drivers.

Estimating the severity levels of road traffic crashes in Bahrain with crash costs estimated with different approaches

An important issue in analyzing accident blackspots is the estimation of severity levels of different types of accidents.This study aims to estimate the severity level of accidents in Bahrain using crash costs.These crash costs were calculated by the Human Capital Approach(HCA)and total reported costs from the victims.The data was collected from the General Directorate of Traffic,insurance companies,Ministry of Works(MoW)and Ministry of Health.It was found,from the survey responses,that there was no significant effect of victim characteristics on the total cost of the accidents.The severity levels were found to be higher than those found in previous literature or adopted by local authorities which could be attributed to the economic conditions of Bahrain.Moreover,the weights found by both approaches were different from each other.Therefore,it is recommended to use the HCA approach due to its comprehensive calculations involving future costs.

A Novel Hybrid Method for Measuring the Spatial Autocorrelation of Vehicular Crashes: Combining Moran’s Index and Getis-Ord G_i<sup style='margin-left:-7px;'>*</sup>Statistic

Spatial autocorrelation is a measure of the correlation of an observation with other observations through space. Most statistical analyses are based on the assumption that the values of observations are independent of one another. Spatial autocorrelation violates this assumption, because observations at near-by locations are related to each other, and hence, the consideration of spatial autocorrelations has been gaining attention in crash data modeling in recent years, and research have shown that ignoring this factor may lead to a biased estimation of the modeling parameters. This paper examines two spatial autocorrelation indices: Moran’s Index;and Getis-Ord Gi* statistic to measure the spatial autocorrelation of vehicle crashes occurred in Boone County roads in the state of Missouri, USA for the years 2013-2015. Since each index can identify different clustering patterns of crashes, therefore this paper introduces a new hybrid method to identify the crash clustering patterns by combining both Moran’s Index and Gi*?statistic. Results show that the new method can effectively improve the number, extent, and type of crash clustering along roadways.

Spatial Regression Analysis of Pedestrian Crashes Based on Point-of-Interest Data

Pedestrian safety has recently been considered as one of the most serious issues in the research of traffic safety. This study aims at analyzing the spatial correlation between the frequency of pedestrian crashes and various predictor variables based on open source point-of-interest (POI) data which can provide specific land use features and user characteristics. Spatial regression models were developed at Traffic Analysis Zone (TAZ) level using 10,333 pedestrian crash records within the Fifth Ring of Beijing in 2015. Several spatial econometrics approaches were used to examine the spatial autocorrelation in crash count per TAZ, and the spatial heterogeneity was investigated by a geographically weighted regression model. The results showed that spatial error model performed better than other two spatial models and a traditional ordinary least squares model. Specifically, bus stops, hospitals, pharmacies, restaurants, and office buildings had positive impacts on pedestrian crashes, while hotels were negatively associated with the occurrence of pedestrian crashes. In addition, it was proven that there was a significant sign of localization effects for different POIs. Depending on these findings, lots of recommendations and countermeasures can be proposed to better improve the traffic safety for pedestrians.

Crash Severity Analysis of Single Vehicle Run-off-Road Crashes

Run-off-road crashes in the United States have become a major cause of serious injuries and fatalities. A significant portion of run-off-road crashes are single vehicle crashes that occur due to collisions with fixed objects and overturning. These crashes typically tend to be more severe than other types of crashes. Single vehicle run-off-road crashes that occurred between 2004 and 2008 were extracted from Kansas Accident Reporting System (KARS) database to identify the important factors that affected their severity. Different driver, vehicle, road, crash, and environment related factors that influence crash severity are identified by using binary logit models. Three models were developed to take different levels of crash severity as the response variables. The first model taking fatal or incapacitating crashes as the response variable seems to better fit the data than the other two developed models. The variables that were found to increase the probability of run-off-road crash severity are driver related factors such as driver ejection, being an older driver, alcohol involvement, license state, driver being at fault, medical condition of the driver;road related factors such as speed, asphalt road surface, dry road condition;time related factors such as crashes occurring between 6 pm and midnight;environment related factors such as daylight;vehicle related factors such as being an SUV, motorcycles, vehicle getting destroyed or disabled, vehicle maneuver being straight or passing;and fixed object types such as trees and ditches.

Comparative Analysis of Fatal Pedestrian Crashes between Kansas and USA

Due to the increasing trend of population growth and urbanization, pedestrians form one of the largest single road user groups. However, they are the most neglected group among all road users. Pedestrian safety is now a growing concern in the USA. Identifying the factors associated with fatal pedestrian crashes plays a key role in developing efficient and effective strategies to enhance pedestrian safety. This study addresses safety issues by identifying contributory factors associated with fatal pedestrian crashes in Kansas and the USA. For Kansas, the study uses KARS (Kansas Accident Reporting System) database while for the USA FARS (Fatality Analysis Reporting System) database has been used. Different variables considered in this study are human variables (age, and gender), environmental variables (atmospheric condition and light condition), time (time of day, day of week, and crash month), location (intersection vs. mid-block), and roadway variables (speed limit). Different factors that are found to have an association with fatal pedestrian crashes are male pedestrians, older pedestrians, weekend, off peak hours, winter months, dark hours, non-intersection, clear atmospheric conditions, higher speed limit. The findings from Kansas have been compared with that from the USA. This study helps to implement potential countermeasures by identifying the factors that have an association with fatal pedestrian crashes.

Application of multinomial and ordinal logistic regressionto model injury severity of truck crashes, using violationand crash data

In 2016 alone, around 4000 people died in crashes involving trucks in the USA, with 21% of these fatalities involving only single-unit trucks. Much research has identified the underlying factors for truck crashes.However, few studies detected the factors unique to single and multiple crashes, and none have examined these underlying factors to severe truck crashes in conjunction with violation data. The current research assessed all of these factors using two approaches to improve truck safety.The first approach used ordinal logistic regression to investigate the contributory factors that increased the odds of severe single-truck and multiple-vehicle crashes, with involvement of at least one truck. The literature has indicated that past violations can be used to predict future violations and crashes. Therefore, the second approach used risky violations, related to truck crashes, to identify the contributory factors to the risky violations and truck crashes. Driver actions of failure to keep proper lane following too close and driving too fast for conditions accounted for about 40% of all the truck crashes. Therefore, the same violations as the aforementioned driver actions were included in the analysis. Based on ordinal logistic regression, the analysis for the first approach indicated that being under non-normal conditions at the time of crash, driving on dry-road condition and having a distraction in the cabin are some of the factors that increase the odds of severe single-truck crashes. On the other hand,speed compliance, alcohol involvement, and posted speed limits are some of the variables that impacted the severity of multiple-vehicle, truck-involved crashes. With the second approach, the violations related to risky driver actions,which were underlying causes of severe truck crashes, were identified and analysis was run to identify the groups at increased risk of truck-involved crashes. The results of violations indicated that being nonresident, driving offpeak hours, and driving on weekends could increase the risk of truck-involved crashes. This paper offers an insight into the capability of using violation data, in addition to crash data, in identification of possible countermeasures to reduce crash frequency.

Examining the Risk Factors of Rear-End Crashes at Signalized Intersections

Rear-end crashes are among the most common crash types at signalized intersections. To examine the risk factors for the occurrence of this crash type, this study involved the analysis of nine years of intersection crash records in the state of Wyoming. With that, the contributing factors related to crash, driver, environmental, and roadway characteristics, including pavement surface friction, were investigated. A binomial logistic regression modeling approach was applied to achieve the study’s objective. The results showed that three factors related to crash and driver’s attributes (commercial vehicle involvement, speeding, and driver’s age) and four factors related to environmental and roadway characteristics (lighting, weather conditions, area type, whether urban or rural and pavement friction) are associated with the risk of rear-end crash occurrence at signalized intersections. This study provides insights into the mitigation measures to implement concerning rear-end crashes at signalized intersections.

Mapping Road Traffic Crashes in Sfax Governorate (Tunisia)

Sfax is one of the Tunisian governorates with a large number of road accidents, injuries and fatalities every year. This study aimed to analyze and map traffic accidents in this governorate. We analyzed the spatial distribution of accidents, their distribution by cause, by type of road, by size of traffic, by months of the year and days of the week. Accidents were correlated with several variables such as population numbers and densities, motorization rate, length and structure of the road network, and the amount of traffic. On the cartographic level, we have built a database, through which we have produced a series of thematic maps to argue this analysis. Through cartographic production, we also aimed to help road users, decision-makers and researchers in this area and in the field of transport. This work showed that Sfax occupies, among the other Tunisian governorates, an advanced position in gravity. Various human, climatic and technical factors explained this situation, of which human factors were the most important, and contributed to almost 90% of accidents. The current situation of accidents in Sfax requires a series of measures and actions to alleviate and mitigate the gravity of this phenomenon.

Complementary parametric probit regression and nonparametric classi?cation tree modeling approaches to analyze factors affecting severity of work zone weather-related crashes

Identifying risk factors for road traffic injuries can be considered one of the main priorities of transportation agencies. More than 12,000 fatal work zone crashes were reported between 2000 and 2013. Despite recent efforts to improve work zone safety, the frequency and severity of work zone crashes are still a big concern for transportation agencies. Although many studies have been conducted on different work zone safety-related issues, there is a lack of studies that investigate the effect of adverse weather conditions on work zone crash severity. This paper utilizes probit–classification tree, a relatively recent and promising combination of machine learning technique and conventional parametric model, to identify factors affecting work zone crash severity in adverse weather conditions using 8 years of work zone weatherrelated crashes (2006–2013) in Washington State. The key strength of this technique lies in its capability to alleviate the shortcomings of both parametric and nonparametric models. The results showed that both presence of traffic control device and lighting conditions are significant interacting variables in the developed complementary crash severity model for work zone weather-related crashes. Therefore, transportation agencies and contractors need to invest more in lighting equipment and better traffic control strategies at work zones, specifically during adverse weather conditions.

Analyzing Crash-Prone Drivers in Multiple Crashes for Better Safety Educational and Enforcement Strategies

Crash-prone drivers should be effectively targeted for various safety education and regulation programs because their over-involvement in crashes presents a big adverse effect on highway safety. By analyzing seven years of crash data from Louisiana, this paper investigates crash-prone drivers’ characteristics and estimates their risk to have crashes in the seventh year based on these drivers’ crash history of the past six years. The analysis results show that quite a few drivers repeatedly had crashes;seven drivers had 13 crashes in seven years;and the maximum number of crashes occurring in a single year to a single driver is eight. The probability of having crash(es) in any given year is closely related to a driver’s crash history: less than 4% for drivers with no crash in the previous six years;and slightly higher than 30% for drivers with nine or more crashes in the previous six years. Based on the results, several suggestions are made on how to improve roadway safety through reducing crashes committed by drivers with much higher crash risk as identified by the analysis.

Driver Sleepiness and Risk of Car Crashes in Shenyang, a Chinese Northeastern City: Population-based Case-control Study

Objective To estimate the association of driver sleepiness with the risk of car crashes. Methods A population-based case-control study was conducted in Shenyang, a northeastern city in China, between November 2001 and July 2002. The case group comprised 406 car drivers involved in crashes, and 438 car drivers recruited at randomly selected sites, and on the day of week, and the time of day when they were driving on highways in the study region during the study period were used as control groups. Face-to-face interviews with drivers were conducted according to a well-structured questionnaire covering the circumstances of their current trip and their background information. Stanford sleepiness scale and Epworth sleepiness scale were used to quantify acute sleepiness and chronic sleepiness respectively. Results There was a strong association between chronic sleepiness and the risk of car crash. Significantly increased risk of crash was associated with drivers who identified themselves as sleepy (Epworth sleepiness score≥10 vs <10; adjusted odds ratio 2.07, 95% confidence interval 1.30 to 3.29), but no increased risk was associated with measures of acute sleepiness. Conclusions Chronic sleepiness in car drivers significantly increases the risk of car crash. Reductions in road traffic injuries may be achieved if fewer people drive when they are sleepy.

Evaluation of vehicle damage involved in road crashes based on quantificated model

Based on economics theory,social value loss caused by vehicle involved in crashes as well as various factors influencing on it were analyzed,the corresponding mi- cro-econometrics model was theoretically given.Moreover,the practicability of the model, the veracity and rationality of quantification were analyzed.Based on probability theory and mathematical statistical theory,macro approach to evaluating vehicle damage in crashes was presented,and the corresponding macro-econometrics model was con- structed.In addition,the macro-econometrics model was utilized to assess economic loss from statistical data of vehicle damaged in crashes,which has shown that the model can meet the demand of quantification analysis of vehicle damage,and be applied to the evaluation of economic loss caused by crashes.The results in this paper will be of practic- al significance for scientific,comprehensive and rational evaluating socio-economic loss caused by road crashes.

Understanding the Contributing Factors to Nighttime Crashes at Freeway Mainline Segments

This study investigated the crash contributing factors to the injury outcomes and the characteristics of the night time crashes at freeway mainline segments. Multinomial logit model (MNL) was selected to estimate the explanatory variables at a 95% confidence level. The six-year crash data (2005-2010) were obtained in the State of Florida, USA and five injury level outcomes, no injury, possible injury, non-incapacitating injury, capacitating injury, and fatal injury, were considered. The no injury level was selected as the baseline category.

Motor-vehicle crashes during pregnancy: a retrospective cohort study

Background: The purpose of this study was to assess the risk of motor-vehicle pregnant driver crashes in Pennsylvania using vital statistics linked to police and ambulance reports. This was supplemented with a review of national age and sex specific crash and fertility data to put this risk into perspective and rank the likelihood for pregnancy-related crashes in other states. Methods: Motor vehicle police crash reports from the Pennsylvania Department of Transportation were probabilistically linked to four years of birth and fetal death data and five years of infant death records and ambulance reports. State specific motor-vehicle traffic injury rates (fatal and non-fatal) were compared to birth rates, by age, for women ages 15 - 34. Results: 5929 (1.1%) of the women with a birth or fetal death linked to a police reported motor vehicle driver crash during pregnancy. One-third (32.5%) of these crashes resulted in minor maternal injuries and 7.5% resulted in moderate to fatal maternal injuries. Crashes were evenly distributed across gestational ages. Young drivers (20 - 24) were at highest risk. Police reported non-belt use was 10%. Conclusions: This study quantifies the risk of motor vehicle crashes during pregnancy in Pennsylvania and offers a perspective on potential variations in other states. Pregnancy related crashes occur at a higher rate than infant related crashes with a concomitant threat to the fetus and new-born not usually tracked within current crash data systems.

Restraint Usage Characteristics and Other Factors Associated with Safety of Children Involved in Motor Vehicle Crashes

Involvement in road traffic crashes as vehicle occupants is a leading cause of death and serious injury among children. The objective of this study was to investigate crash severity factors and child safety restraint use characteristics in order to identify effective countermeasures to increase children＇s highway safety. Characteristics and percentages of restraint use among child passengers aged 4-13 years were examined using highway crash data from Kansas. The association between restraint use, injury severity and characteristics of children involved in crashes were investigated using OR （odds ratios） and a logistic regression model, which was used to identify risk factors. Results showed that children, who were unrestrained, were seated in the front seat, traveling with drunk drivers and on rural roads, and traveling during nighttime was more vulnerable to severe injury in the case of motor vehicle crashes. The most frequent contributing causes related to crashes involving children included driver＇s inattention while driving, failure to yield right-of-way, driving too fast, wet roads and animals in the road. Based on identified critical factors, general countermeasure ideas to improve children＇s traffic safety were suggested, including age-appropriate and size-appropriate seat belt restraints and having children seated in the rear seat. Parents and children must gain better education regarding these safety measures in order to increase child safety on the road.

Alcohol, Tobacco and Tramadol Daily Consumption and Road Traffic Crashes among Motorcycle Taxi Drivers in Cotonou (Benin)

Background: In Benin, motorcycle taxi drivers make up a significant portion of road transport. The use of psychoactive substances, a behavior recognized as being at risk of road crashes, is frequent in this group. This study aimed to analyze the association between the consumption of psychoactive substances and the occurrence of road crashes among motorcycle taxi drivers. Methods: This was a cross-sectional study involving 430 motorcycle taxi drivers selected from motorcycle taxi stands in Cotonou (Benin) using a two-stage cluster sampling technique. An interview with a structured questionnaire was used to collect data, which took place in March and April 2019. Alcohol, tramadol, and tobacco were the psychoactive substances considered in this study and their daily use was investigated. A binary logistic regression was used for the analysis. Results: Among the motorcycle taxi drivers surveyed, 27.79% (95% CI = 23.71 - 32.25) were involved in a road crash. The prevalence of psychoactive substances daily use was: 30.41% (95% CI = 26.17 - 35.00) for alcohol, 28.50% (95% CI = 24.37 - 33.02) for tramadol and 4.03% (95% CI = 2.51 - 6.42) for tobacco. Alcohol daily consumption (AOR = 2.09, 95% CI = 1.27 - 3.45, p = 0.004) was associated with a significant risk of road crashes. Conclusion: Interventions aiming to reduce psychoactive substances use should, therefore, be included in road safety policies, especially for this group.