Road Traffic Accidents Injury Data Analytics

Nour, Mohamed K.; Naseer, Atif; Alkazemi, Basem Y.; Jamil, Muhammad Abid

doi:10.14569/ijacsa.2020.0111287

Cited by 7 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Comparison between each algorithm's performance in terms of accuracy, and loss proved that Random Forest is the best overall. The outcome of this paper is similar to (Nour et al 2020) where they found that Random Forest Algorithm shows better performance with 73% accuracy than Decision Tree. Hence Random Forest can be used to predict accident crash severity efficiently.…”

Section: Discussionsupporting

confidence: 78%

Improved Accuracy of Calculation of Vehicle Crash Severity in Highways using Random Forest over Decision Tree Algorithm

2022

pnr

View full text Add to dashboard Cite

To improve the accuracy rate of vehicle crash severity in highways using Random forest over Decision tree. Materials and Methods: Random forest and Decision tree with sample size of (N=10) is executed with varying training and testing splits for calculating the accuracy for accident crash severity with g power as 75%, threshold 0.000 and confidence interval 95%. The performance of the classifiers are evaluated based on their accuracy rate using accident severity dataset. Results: The accuracy for calculating accident crash severity in Random Forest(91%) and Decision Tree (85%) is obtained(P<0.005).Conclusion: Prediction of accident crash severity using Random Forest (RF) algorithm appears to be significantly better than Decision Tree (DT) with improved accuracy.

show abstract

Section: Discussionsupporting

confidence: 78%

Improved Accuracy of Calculation of Vehicle Crash Severity in Highways using Random Forest over Decision Tree Algorithm

2022

pnr

View full text Add to dashboard Cite

show abstract

“…Earlier conventional methods were used to find the blackspot locality in which only the number of accidents is considered. In most of the traditional methods, they used the statistical Poisson distribution, and the Empirical Bayes technique was utilized [9,31,39,[42][43][44][45][46][47][48][49][50][51][52][53]57]. e goal of the identification procedure is to pick a few sites from a large number of possibilities to increase safety.…”

Section: Identifying Blackspot On a National And Worldwide Level Rese...mentioning

confidence: 99%

Identifying Influencing Factors of Road Accidents in Emerging Road Accident Blackspots

Athiappan¹,

Chandran

Rajalakshmi³

et al. 2022

Advances in Civil Engineering

View full text Add to dashboard Cite

This study deals with identifying the accident blackspots and the influencing factors causing accidents using factored analysis in a medium-sized city (Tirunelveli) in India. From the literature review, the geospatial technique to identify the blackspots and the factors causing accidents was used for analysis. The most influencing factors driving the accidents were identified and ranked based on the repetitive occurrence of accidents in the blackspot area. The spearman ranking system showed the correlation among the factors causing accidents. The factor analysis technique was utilized to identify the key factors driving the repetitive accidents and group them. This study will help transportation planners understand the factors causing accidents and take appropriate measures to reduce the casualties in the road construction planning stage and existing conditions.

show abstract

“…Earlier conventional methods were used to find the black spot locality in which only the number of accidents is considered. In most of the traditional methods, they used the statistical Poisson distribution, and the Empirical Bayes technique was utilized [42,[55][56][57][58][59][60][61][62][63][64][65][66][67][68][69]. The goal of the identification procedure is to pick a few sites from a vast number of possibilities in order to increase safety.…”

Section: Identifying Black Spotmentioning

confidence: 99%

Identifying Influencing Factors of Road Accidents in Emerging Road Accident Black spots<strong> </strong><strong> </strong>

Gheisari¹

2022

Preprint

View full text Add to dashboard Cite

This paper deals with identifying the accident black spots and the influencing factors causing accidents using factored analysis in the medium-sized city (Tirunelveli) in India. From the literature review, the geospatial technique to identify the black spots and the factors causing accidents was used for analysis. The most influencing factors driving the accident were identified and ranked based on the repetitive occurrence of accidents in the black spot area. The spearman ranking system obtained the correlation among the factors causing accidents. The factor analysis technique was utilized to identify the key factors driving the repetitive accidents and group them. This study will help transportation planners to understand the factors causing accidents and take appropriate measures to reduce the casualties in the road construction planning stage and existing conditions.

show abstract

Road Traffic Accidents Injury Data Analytics

Cited by 7 publications

References 15 publications

Improved Accuracy of Calculation of Vehicle Crash Severity in Highways using Random Forest over Decision Tree Algorithm

Improved Accuracy of Calculation of Vehicle Crash Severity in Highways using Random Forest over Decision Tree Algorithm

Identifying Influencing Factors of Road Accidents in Emerging Road Accident Blackspots

Identifying Influencing Factors of Road Accidents in Emerging Road Accident Black spots<strong> </strong><strong> </strong>

Contact Info

Product

Resources

About