Complementary Modeling of Gravel Road Traffic-Generated Dust Levels Using Bayesian Regularization Feedforward Neural Networks and Binary Probit Regression

Albatayneh, Omar; Moomen, Milhan; Farid, Ahmed; Ksaibati, Khaled

doi:10.1007/s42947-020-0261-3

Cited by 9 publications

(7 citation statements)

References 23 publications

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“…With the same spirit of computing the likelihood of an event to happen, Albatayneh et al [ 20 ] have used a mix of parametric and non-parametric models. In fact, classical non-parametric Bayesian regularization artificial neural network (BRANN) and parametric probit regression models were applied to gain insight about the data and the likelihood of each event.…”

Section: Knowledge and Background On Modelingmentioning

confidence: 99%

Prediction of geopolymer pumpability and setting time for well zonal isolation - Using machine learning and statistical based models

Hoayek,

Khalifeh,

Hamie

et al. 2023

Heliyon

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Section: Knowledge and Background On Modelingmentioning

confidence: 99%

Prediction of geopolymer pumpability and setting time for well zonal isolation - Using machine learning and statistical based models

Hoayek,

Khalifeh,

Hamie

et al. 2023

Heliyon

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“…The cost estimate of the environmental damage and the negative impact on human and animal health of the dust from gravel roads was USD 1.510/km/year ( 40 ). Dust also reduces visibility and contributes to road crashes on gravel roads ( 70 ). Potholes are concave-shaped depressions on the road surface, measuring between 30 and 80 cm at the road surface level with a depth of between 3 and 7 cm, formed when the surface material is washed away by water or removed by forces from traffic action ( 26 ).…”

Section: Gravel Roads and Their Current Maintenance Practicesmentioning

confidence: 99%

Literature Review on Gravel Road Maintenance: Current State and Directions for Future Research

Mbiyana

Kans

Campos

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Gravel roads form a significant share of the global road network, usually in sparsely populated rural areas. They are important, especially in agriculture, tourism, and forestry, connecting rural to urban areas. This systematic literature study comprises 105 reviewed publications on gravel road maintenance. Review articles on maintenance management practices, especially concerning objective condition assessment and data-driven methods (DDMs), are lacking. Therefore, this review provides a concise overview of current gravel road maintenance practices and ongoing research on objective condition assessment and DDMs for gravel road maintenance. It offers researchers in gravel road maintenance and other related fields a clear indication of where to focus their research efforts, as it suggests the direction for future research. Visual assessment methods are predominant for monitoring the condition of gravel roads, while objective methods and DDMs are not common. Research on gravel roads and their maintenance has increased in the last two decades, especially in North America and Northern Europe. Condition assessment is shifting from subjective to objective methods, utilizing knowledge from technological advancements in image processing, vibration and acoustics analysis, and so forth. There are some excellent research initiatives for objectively assessing the condition of gravel roads and DDMs, but the practical implementation is limited. Implementing objective assessment methods and DDMs generally improves the management of gravel roads with regard to decision-making, maintenance costs, safety, and the stability and comfort of the ride. Objective condition assessment and DDMs have the potential to enhance maintenance practices in the maintenance of gravel roads.

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“…Figure 1 represents the graph of traffic accidents categorized by three parameters i.e., road accidents, the person dies and injuries that happened on roadways from 2014 to 2019, the data provided by the Government of India (ministry of road transport and highways research wing, New Delhi). The descriptive model was developed using clustering techniques and association learning techniques [5] [6]. Around 140 lives have been lost across the country due to road crashes.…”

Section: Introductionmentioning

confidence: 99%

Predicting Traffic Accidents and their Injury Severities Using Machine Learning Techniques

Gururaj¹,

Janhavi²,

Tanuja³

et al. 2022

Int. J. TDI

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Traffic accidents are among the most censorious issues confronting the world as they cause numerous deaths, wounds and fatalities just as monetary misfortunes consistently. According to the world health organization (WHO) reports, 5,18,3626 accidents took place in India in the year 2019. Factors that contribute to these road crashes/ traffic accidents and resulting injuries include inattentive drivers, unenforced traffic laws, poor road infrastructure, driving in bad weather conditions and others. This investigation effort establishes models to select a set of influential factors and to build up a model for classifying the severity of injuries. Machine learning models can be applied to model and predict the severity of injury that occurs during road accidents. One such way is to apply unsupervised learning models such as Apriori, Apriori TID (transaction id), SFIT (set operation for frequent itemset using transaction database) and ECLAT (equivalence class clustering and bottom-up lattice traversal) which analyze the unlabeled traffic accidents dataset and determine the relationship between traffic accidents and injury. This research work is helpful for traffic departments to decrease the number of accidents and to distinguish the injury's seriousness extensive simulations were carried out to demonstrate the unsupervised learning algorithms for predicting the injury severity of traffic accidents. Apriori algorithm predicts the patterns in 962 milliseconds, Apriori TID (transaction id) algorithm predicts the pattern in 557 milliseconds, SFIT algorithm predicts the pattern in 516 milliseconds and ECLAT algorithm predicts the pattern in 124 milliseconds. ECLAT algorithm took less time compared to all the other algorithms.

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Complementary Modeling of Gravel Road Traffic-Generated Dust Levels Using Bayesian Regularization Feedforward Neural Networks and Binary Probit Regression

Cited by 9 publications

References 23 publications

Prediction of geopolymer pumpability and setting time for well zonal isolation - Using machine learning and statistical based models

Prediction of geopolymer pumpability and setting time for well zonal isolation - Using machine learning and statistical based models

Literature Review on Gravel Road Maintenance: Current State and Directions for Future Research

Predicting Traffic Accidents and their Injury Severities Using Machine Learning Techniques

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