scholarly journals Analysis of Intersection Traffic Safety in the City of San Antonio, 2013–2017

2021 ◽  
Vol 13 (9) ◽  
pp. 5296
Author(s):  
Khondoker Billah ◽  
Qasim Adegbite ◽  
Hatim O. Sharif ◽  
Samer Dessouky ◽  
Lauren Simcic
Keyword(s):  
High Risk ◽  
Traffic Safety ◽  
Traffic Control ◽  
Injury Severity ◽  
San Antonio ◽  
Binary Logistic Regression ◽  
Bivariate Analysis ◽  
Crash Severity ◽  
Contributing Factors ◽  
Late Night

An understanding of the contributing factors to severe intersection crashes is crucial for developing countermeasures to reduce crash numbers and severity at high-risk crash locations. This study examined the variables affecting crash incidence and crash severity at intersections in San Antonio over a five-year period (2013–2017) and identified high-risk locations based on crash frequency and injury severity using data from the Texas Crash Record and Information System database. Bivariate analysis and binary logistic regression, along with respective odds ratios, were used to identify the most significant variables contributing to severe intersection crashes by quantifying their association with crash severity. Intersection crashes were predominantly clustered in the downtown area with relatively less severe crashes. Males and older drivers, weekend driving, nighttime driving, dark lighting conditions, grade and hillcrest road alignment, and crosswalk, divider and marked lanes used as traffic control significantly increased crash severity risk at intersections. Prioritizing resource allocation to high-risk intersections, separating bicycle lanes and sidewalks from the roadway, improving lighting facilities, increasing law enforcement activity during the late night hours of weekend, and introducing roundabouts at intersections with stops and signals as traffic controls are recommended countermeasures.

scholarly journals Urban Intersections and Traffic Safety in the City of San Antonio

2019 ◽  
Vol 271 ◽  
pp. 06003
Author(s):  
Qasim Adegbite ◽  
Khondoker Billah ◽  
Hatim Sharif ◽  
Samer Dessouky
Keyword(s):  
Traffic Safety ◽  
Traffic Control ◽  
San Antonio ◽  
Binary Logistic Regression ◽  
Crash Severity ◽  
Binary Logistic Regression Model ◽  
Crash Frequency ◽  
Crash Rates ◽  
Traffic Control System ◽  
The City

Intersections are high-risk locations on roadways and often experience high incidence of crashes. Better understanding of the factors contributing to crashes and deaths at intersections is crucial. This study analyzed the factors related to crash incidence and crash severity at intersections in San Antonio for crashes from 2013 to 2017 and identified hotspot locations based on crash frequency and crash rates. Binary logistic regression model was considered for the analysis using crash severity as the response variable. Factors found to be significantly associated with the severity of intersection crashes include age of driver, day of the week, month, road alignment, and traffic control system. The crashes occurred predominantly in the highdensity center of the city (downtown area). Overall, the identification of risk factors and their impact on crash severity would be helpful for road safety policymakers to develop proactive mitigation plans to reduce the frequency and severity of intersection crashes.

scholarly journals Identifying the Factors Contributing to the Severity of Truck-Involved Crashes in Shanghai River-Crossing Tunnel

2020 ◽  
Vol 17 (9) ◽  
pp. 3155 ◽  
Author(s):  
Shengdi Chen ◽  
Shiwen Zhang ◽  
Yingying Xing ◽  
Jian Lu
Keyword(s):  
Environmental Factor ◽  
High Speed ◽  
Injury Severity ◽  
Control Measures ◽  
Crash Severity ◽  
Contributing Factors ◽  
Safety Control ◽  
Late Night ◽  
Single Vehicle ◽  
The Impact

The impact that trucks have on crash severity has long been a concern in crash analysis literature. Furthermore, if a truck crash happens in a tunnel, this would result in more serious casualties due to closure and the complexity of the tunnel. However, no studies have been reported to analyze traffic crashes that happened in tunnels and develop crash databases and statistical models to explore the influence of contributing factors on tunnel truck crashes. This paper summarizes a study that aims to examine the impact of risk factors such as driver factor, environmental factor, vehicle factor, and tunnel factor on truck crashes injury propensity based on tunnel crashes data obtained from Shanghai, China. An ordered logit model was developed to analyze injury crashes and property damage only crashes. The driver factor, environmental factor, vehicle factor, and tunnel factor were explored to identify the relationship between these factors and crashes and the severity of crashes. Results show that increased injury severity is associated with driver factors, such as male drivers, older drivers, fatigue driving, drunkenness, safety belt used improperly, and unfamiliarity with vehicles. Late night (00:00–06:59) and afternoon rushing hours (16:30–18:59), weekdays, snow or icy road conditions, combination truck, overload, and single vehicle were also found to significantly increase the probability of injury severity. In addition, tunnel factors including two lanes, high speed limits (≥80 km/h), zone 3, extra-long tunnels (over 3000 m) are also significantly associated with a higher risk of severe injury. So, the gender, age of driver, mid-night to dawn and afternoon peak hours, weekdays, snowy or icy road conditions, the interior zone of a tunnel, the combination truck, overloaded trucks, and extra-long tunnels are associated with higher crash severity. Identification of these contributing factors for tunnel truck crashes can provide valuable information to help with new and improved tunnel safety control measures.

scholarly journals Analysis of Bicycle-Motor Vehicle Crashes in San Antonio, Texas

2021 ◽  
Vol 18 (17) ◽  
pp. 9220
Author(s):  
Khondoker Billah ◽  
Hatim O. Sharif ◽  
Samer Dessouky
Keyword(s):  
High Risk ◽  
Injury Risk ◽  
Injury Severity ◽  
Motor Vehicle ◽  
San Antonio ◽  
Bivariate Analysis ◽  
Vehicle Crashes ◽  
Lighting Condition ◽  
Downtown Area ◽  
Stop Sign

Bicycling is inexpensive, environmentally friendly, and healthful; however, bicyclist safety is a rising concern. This study investigates bicycle crash-related key variables that might substantially differ in terms of the party at fault and bicycle facility presence. Employing 5 year (2014–2018) data from the Texas Crash Record and Information System database, the effect of these variables on bicyclist injury severity was assessed for San Antonio, Texas, using bivariate analysis and binary logistic regression. Severe injury risk based on the party at fault and bicycle facility presence varied significantly for different crash-related variables. The strongest predictors of severe bicycle injury include bicyclist age and ethnicity, lighting condition, road class, time of occurrence, and period of week. Driver inattention and disregard of stop sign/light were the primary contributing factors to bicycle-vehicle crashes. Crash density heatmap and hotspot analyses were used to identify high-risk locations. The downtown area experienced the highest crash density, while severity hotspots were located at intersections outside of the downtown area. This study recommends the introduction of more dedicated/protected bicycle lanes, separation of bicycle lanes from the roadway, mandatory helmet use ordinance, reduction in speed limit, prioritization of resources at high-risk locations, and implementation of bike-activated signal detection at signalized intersections.

Analysis of Severity of Young Driver Crashes: Sequential Binary Logistic Regression Modeling

2002 ◽  
Vol 1784 (1) ◽  
pp. 108-114 ◽  
Author(s):  
Sunanda Dissanayake ◽  
John Lu
Keyword(s):  
Logistic Regression ◽  
Injury Severity ◽  
Selection Process ◽  
Model Development ◽  
Weather Condition ◽  
Binary Logistic Regression ◽  
The United States ◽  
Crash Severity ◽  
Young Drivers ◽  
Single Vehicle

Young drivers have the highest fatality involvement rates of any driver age group within the United States driving population. They also experience a higher percentage of single-vehicle crashes compared with others. When looking at the methods of improving this alarming death rate of young drivers, it is important to identify the determinants of higher crash and injury severity. With that intention, the study developed, using the Florida Traffic Crash Database, a set of sequential binary logistic regression models to predict the crash severity outcome of single-vehicle fixed-object crashes involving young drivers. Models were organized from the lowest severity level to the highest and vice versa to examine the reliability of the selection process, but it was found that there was no considerable impact based on this selection. The developed models were validated and the accuracy was tested by using crash data that were not utilized in the model development, and the results were found to be satisfactory. Factors influential in making a crash severity difference to young drivers were then identified through the models. Factors such as influence of alcohol or drugs, ejection in the crash, point of impact, rural crash locations, existence of curve or grade at the crash location, and speed of the vehicle significantly increased the probability of having a more severe crash. Restraint device usage and being a male clearly reduced the tendency of high severity, and some other variables, such as weather condition, residence location, and physical condition, were not important at all.

scholarly journals Analysis of Anxiety-related factors amongst frontline dental staff during the COVID-19 Pandemic in Yichang, China

2020 ◽  
Author(s):  
Suli Zhao ◽  
Jing Cao ◽  
Lin Zhang ◽  
Beibei Liu ◽  
Rongcan Sun
Keyword(s):  
Anxiety Disorder ◽  
Anxiety Disorders ◽  
General Public ◽  
Binary Logistic Regression ◽  
Bivariate Analysis ◽  
Anxiety State ◽  
Contributing Factors ◽  
Related Factors ◽  
Protective Measures ◽  
Dental Staff

Abstract Background: Dental staff were characterized with the tolerance of enduring stress and they were at high risk to respiratory infectious disease. This study compared the anxiety level of frontline dental staff (FDS) to the general public in Yichang, Hubei Province, and examined potential explanatory factors for the differences. Methods: Two online questionnaires were used separately for collecting data from FDS and the general public. The Chinese version of Becker Anxiety Inventory (BAI) was included for the assessment of anxiety. Firstly, A Chi-square test was conducted to see the difference of the anxiety state between these two groups. Then, bivariate analysis using Cramer’s V and Eta squared was conducted for screening potential factors. Lastly, a binary logistic regression was performed to explore potential contributing factors towards the anxiety disorder of dental staff. Results: In general, the FDS were 4.342 (95% CI: 2.427-7.768) times more likely to suffer from anxiety disorder than the general public in Yichang. The bivariate analysis showed that age, Level Three protective measures (PM-3), the conflict with patients and/or colleagues were moderately associated with the anxiety state of FDS. But the knowledge of coronavirus disease of 2019 (COVID-19) and the treatment to suspected or confirmed cases had a weak association with the anxiety perceived among FDS. Conversely, workload, the exposure to potential infectious substance and conducting aerosol generated performance were not significantly related to the anxiety of FDS. As the model indicated, an elder age and PM-3 protective measures could lower the anxiety state of the FDS, whereas the conflict with patients or/and colleagues would worsen it. Conclusions: During the COVID-19 pandemic, FDS were more likely to suffer from anxiety disorders than the general public. Sufficient personal protective measures and good relationships with colleagues and patients helped them to maintain mental health. Keywords COVID-19, Anxiety disorders, Becker Anxiety Inventory, Frontline dental staff

scholarly journals Determinants and Prediction of Injury Severities in Multi-Vehicle-Involved Crashes

2021 ◽  
Vol 18 (10) ◽  
pp. 5271
Author(s):  
Xiuguang Song ◽  
Rendong Pi ◽  
Yu Zhang ◽  
Jianqing Wu ◽  
Yuhuan Dong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traffic Safety ◽  
Statistical Models ◽  
Injury Severity ◽  
Safety Information ◽  
Temporal Stability ◽  
Prediction Performance ◽  
Transportation Safety ◽  
Contributing Factors ◽  
Injury Severities

Multi-vehicle (MV) crashes, which can lead to great damages to society, have always been a serious issue for traffic safety. A further understanding of crash severity can help transportation engineers identify the critical reasons and find effective countermeasures to improve transportation safety. However, studies involving methods of machine learning to predict the possibility of injury-severity of MV crashes are rarely seen. Besides that, previous studies have rarely taken temporal stability into consideration in MV crashes. To bridge these knowledge gaps, two kinds of models: random parameters logit model (RPL), with heterogeneities in the means and variances, and Random Forest (RF) were employed in this research to identify the critical contributing factors and to predict the possibility of MV injury-severity. Three-year (2016–2018) MV data from Washington, United States, extracted from the Highway Safety Information System (HSIS), were applied for crash injury-severity analysis. In addition, a series of likelihood ratio tests were conducted for temporal stability between different years. Four indicators were employed to measure the prediction performance of the selected models, and four categories of crash-related characteristics were specifically investigated based on the RPL model. The results showed that the machine learning-based models performed better than the statistical models did when taking the overall accuracy as an evaluation indicator. However, the statistical models had a better prediction performance than the machine learning models had considering crash costs. Temporal instabilities were present between 2016 and 2017 MV data. The effect of significant factors was elaborated based on the RPL model with heterogeneities in the means and variances.

scholarly journals Analysis of Pedestrian–Motor Vehicle Crashes in San Antonio, Texas

2021 ◽  
Vol 13 (12) ◽  
pp. 6610
Author(s):  
Khondoker Billah ◽  
Hatim O. Sharif ◽  
Samer Dessouky
Keyword(s):  
High Risk ◽  
San Antonio ◽  
Speed Limit ◽  
Vehicle Crashes ◽  
Contributing Factors ◽  
Hotspot Analysis ◽  
Lighting Condition ◽  
Downtown Area ◽  
Pedestrian Injury ◽  
Pedestrian Crashes

Pedestrian safety is becoming a global concern and an understanding of the contributing factors to severe pedestrian crashes is crucial. This study analyzed crash data for San Antonio, TX, over a six-year period to understand the effects of pedestrian–vehicle crash-related variables on pedestrian injury severity based on the party at fault and to identify high-risk locations. Bivariate analysis and logistic regression were used to identify the most significant predictors of severe pedestrian crashes. High-risk locations were identified through heat maps and hotspot analysis. A failure to yield the right of way and driver inattention were the primary contributing factors to pedestrian–vehicle crashes. Fatal and incapacitating injury risk increased substantially when the pedestrian was at fault. The strongest predictors of severe pedestrian injury include the lighting condition, the road class, the speed limit, traffic control, collision type, the age of the pedestrian, and the gender of the pedestrian. The downtown area had the highest crash density, but crash severity hotspots were identified outside of the downtown area. Resource allocation to high-risk locations, a reduction in the speed limit, an upgrade of the lighting facilities in high pedestrian activity areas, educational campaigns for targeted audiences, the implementation of more crosswalks, pedestrian refuge islands, raised medians, and the use of leading pedestrian interval and hybrid beacons are recommended.

scholarly journals Prediction of Fatalities in Vehicle Collisions in Canada

2021 ◽  
Vol 33 (5) ◽  
pp. 661-669
Author(s):  
Liza Babaoglu ◽  
Ceni Babaoglu
Keyword(s):  
Traffic Safety ◽  
Traffic Control ◽  
Prediction Models ◽  
Machine Learning Techniques ◽  
Lasso Regression ◽  
Contributing Factors ◽  
Vehicle Collisions ◽  
Road Design ◽  
The World ◽  
The Individual

Traffic collisions affect millions around the world and are the leading cause of death for children and young adults. Thus, Canada’s road safety plan is to reduce collision injuries and fatalities with a vision of making the safest roads in the world. We aim to predict fatalities of collisions on Canadian roads, and to discover causation of fatalities through exploratory data analysis and machine learning techniques. We analyse the vehicle collisions from Canada’s National Collision Database (1999–2017.) Through data mining methodologies, we investigate association rules and key contributing factors that lead to fatalities. Then, we propose two supervised learning classification models, Lasso Regression and XGBoost, to predict fatalities. Our analysis shows the deadliness of head-on collisions, especially in non-intersection areas with lacking traffic control systems. We also reveal that most collision fatalities occur in non-extreme weather and road conditions. Our prediction models show that the best classifier of fatalities is XGBoost with 83% accuracy. Its most important features are “collision configuration” and “used safety devices” elements, outnumbering attributes such as vehicle year, collision time, age, or sex of the individual. Our exploratory and predictive analysis reveal the importance of road design and traffic safety education.

Examining Prominent Causes of Traffic Injury Severity in Louisiana with Multinomial Logistic Models

2020 ◽  
pp. 036119812096478
Author(s):  
Onyumbe Enumbe B. Lukongo
Keyword(s):  
Traffic Safety ◽  
Injury Severity ◽  
Motor Vehicle ◽  
Logistic Models ◽  
Crash Severity ◽  
Fatal Accidents ◽  
Vehicle Accidents ◽  
The Road ◽  
Young Drivers ◽  
Safety Research

Accidents rank third among the top 10 leading causes of death in Louisiana, claiming more than 2,000 lives out of a total of almost 33,000 deaths. Drivers’ characteristics (age and gender), the geometry of the roadways, driving on the major roadways, the day of the week, and the wet or dry condition or the road have been associated with crash severity. This study applies unordered multinomial logistic models to investigate causes leading to crash severity in Louisiana. Several models were estimated and the best results were retained for presentation and discussion. Consistent with previous research, findings suggest that drivers’ gender and age matter for traffic safety. Individually, male and older drivers are too risky. Major roads, weekdays, dry surfaces, and road geometry increase the risk of fatal accidents. Male drivers are prone to severe and fatal accidents while old drivers are vulnerable to all types of accidents. Young drivers and female drivers feature among cases of injury and moderate accidents. Evidence suggests that crash severity is not ethnicity specific, contrary to some studies. This study is relevant because it builds a new dataset for safety research, identifies risk factors, and informs the aim of public safety policy to reduce loss of life, injuries, and costs resulting from motor vehicle accidents.

Neighbourhood Influences on Vehicle-Pedestrian Crash Severity

2019 ◽  
pp. 102-121
Keyword(s):  
High Risk ◽  
Socioeconomic Factors ◽  
Crash Severity ◽  
Relative Importance ◽  
Contributing Factors ◽  
Counter Measures ◽  
Road Users ◽  
Pedestrian Crashes ◽  
Crash Modelling ◽  
Partial Dependence

Socioeconomic factors are known to be contributing factors to vehicle-pedestrian crashes. Although several studies have examined the socioeconomic factors related to the locations of crashes, few studies have considered the socioeconomic factors of the neighbourhoods where road users live in vehicle-pedestrian crash modelling. In vehicle-pedestrian crashes in the Melbourne metropolitan area, 20% of pedestrians, 11% of drivers, and only 6% of both drivers and pedestrians had the same postcode for the crash and residency locations. Therefore, an examination of the influence of socioeconomic factors of their neighbourhoods, and their relative importance will contribute to advancing knowledge in the field, as very limited research has been conducted on the influence of socioeconomic factors of both the neighbourhoods where crashes occur and where pedestrians live. In this chapter, neighbourhood factors associated with road users' residents and location of crash are investigated using BDT model. Furthermore, partial dependence plots are applied to illustrate the interactions between these factors. The authors found that socioeconomic factors account for 60% of the 20 top contributing factors to vehicle-pedestrian crashes. This research reveals that socioeconomic factors of the neighbourhoods where road users live and where crashes occur are important in determining the severity of crashes, with the former having a greater influence. Hence, road safety counter-measures, especially those focussing on road users, should be targeted at these high-risk neighbourhoods.

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