User perceptions of automated Truck-Mounted attenuators: Implications on work zone safety

Pourfalatoun, Shiva; Miller, Erika E.

doi:10.1080/15389588.2021.1925116

Cited by 22 publications

(12 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to Table 2, the BF for the Flat distribution is 66, which is the highest number compared to the other distributions. In this study, the flat distribution, a non-informative distribution, was considered the prior distribution [27,28].…”

Section: Results Of Bayesian Binary Probit Modelmentioning

confidence: 99%

Investigating the Factors Affecting the Severity of Single-Vehicle Crashes on Urban Roads using Bayesian Binary Probit Regression

Asil¹,

Bargegol²

2022

CRPASE

View full text Add to dashboard Cite

Traffic crashes are a global challenge, and many people lose their lives in crashes every year. Single-vehicle accidents have high injury and death rates and are therefore a major concern. In this study, the effect of various factors on the severity of this type of crash on urban roads of Rasht, in Iran, has been investigated. Rasht is the capital of Guilan province and is one of the cities that many tourists visit every year. Investigating Single-vehicle crashes and predicting the factors affecting them facilitates the possibility of prevention and provision of medical facilities. In this paper, a Bayesian Binary Probit Regression model was used for this purpose. The results of this study showed that Single-vehicle crashes during non-peak traffic hours, which are from 21 to 6 o'clock, increase the severity of the collision and also the results stated that spring and summer seasons, Iranian-made passenger cars and motorcycles, male drivers, and inability to control vehicles increase the severity of a single-vehicle crash.

show abstract

Section: Results Of Bayesian Binary Probit Modelmentioning

confidence: 99%

Investigating the Factors Affecting the Severity of Single-Vehicle Crashes on Urban Roads using Bayesian Binary Probit Regression

Asil¹,

Bargegol²

2022

CRPASE

View full text Add to dashboard Cite

show abstract

“…Deep learning models can also obtain more accurate results [72][73][74]. By understanding users' perceptions regarding the improvement of facilities, these methods, in conjunction with a survey analysis, can also improve work zone safety [75]. In addition, researchers can present other effective parameters on the severity of accidents [76].…”

Section: Discussionmentioning

confidence: 99%

Application of Artificial Neural Network Approaches for Predicting Accident Severity on Rural Roads (Case Study: Tehran-Qom and Tehran-Saveh Rural Roads)

Habibzadeh

Ameri

Haghighi

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Traffic accidents occur due to a combination of factors that lead to casualties and injuries. By identifying the most effective factors, it is possible for safety authorities to provide appropriate solutions for decreasing the accident severity and implementing preventive measures. The aim of this research was to present models to predict the accident severity on two-lane two-way (TLTW) rural highways in Iran over a one-year period from 2019 to 2020. Therefore, the occurrence probability of any type of accident was determined by artificial neural network (ANN)-based prediction models using nine independent variables affecting the accident severity. This study developed numerous ANN structures using back-propagation to model the potential nonlinear relationship between the accident severity and accident-related factors. Results indicated that among the models, the multilayer perceptron neural network (MLPNN) model with 6-2-2 partition had the best performance and prediction power. This model was developed using the standardized rescaling method for covariates and batch for training. Also, 9, 5, and 2 units were considered automatically for the input, hidden, and output layers, respectively, and the hyperbolic tangent and softmax were used as an activation function in the hidden and output layers, respectively. This model had the lowest cross-entropy error of 39.6 and the highest correct percentage of 82.5%, and the area under the receiver operating characteristic (ROC) curve was 0.852. Moreover, among all the effective variables, pavement condition index, roadside hazard, shoulder width, and passing zone ratio had the greatest impact on the accident severity. Finally, safety strategies were proposed to increase safety and reduce accidents along these roads.

show abstract

“…One of the most effective factors whose effect may greatly influence other factors in a crash is the lighting condition. This study aimed to investigate the factors affecting the severity of urban two-vehicle crashes in different lighting conditions [18]. Three scenarios, including the crashes that occurred at daylights, nights with enough lighting, and nights without lighting, were defined.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Factors Associated with Traffic Injury Severity on Urban Roads in Different Lighting Conditions

Asil¹,

Toroghi²,

Bargegol³

2022

CRPASE

View full text Add to dashboard Cite

This research has studied the severity of vehicle-to-vehicle accidents in Rasht City in various lighting conditions. In the present study, the target variable is the severity of an accident, which is split into two classes of no injury and injury/death. The effects of independent variables, including environmental, human, and roadway-related factors, on the vehicle-tovehicle accident severity, have been investigated. The classification and regression tree (CART) method has been used for modeling purposes. The results showed that on daylight accidents if the not-at-fault driver is a motorcycle, the accident tends to be fatal or injury with the probability of 95.6%. At night with enough lighting, if the at-fault vehicle is a motorcycle and the not-at-fault vehicle is not a motorcycle, there is a probability of 72% of injury or death in case of inattention to the front. Also, if the accident reason is inattention to the front and the not-at-fault driver is route familiar, there is a 95.1% probability of injury and death. In addition, at night accidents without enough lighting, if the not-at-fault vehicle is a motorcycle and the accident does not occur in the summer, with the 100% probability, the accident leads to injury and death.

show abstract

User perceptions of automated Truck-Mounted attenuators: Implications on work zone safety

Cited by 22 publications

References 11 publications

Investigating the Factors Affecting the Severity of Single-Vehicle Crashes on Urban Roads using Bayesian Binary Probit Regression

Investigating the Factors Affecting the Severity of Single-Vehicle Crashes on Urban Roads using Bayesian Binary Probit Regression

Application of Artificial Neural Network Approaches for Predicting Accident Severity on Rural Roads (Case Study: Tehran-Qom and Tehran-Saveh Rural Roads)

Analysis of Factors Associated with Traffic Injury Severity on Urban Roads in Different Lighting Conditions

Contact Info

Product

Resources

About