Analysis of road accident factors using Decision Tree Algorithm: a case of study Algeria

Nedjmedine, Ouennoughi; Mehenni, Tahar

doi:10.1109/isia55826.2022.9993530

2022 5th International Symposium on Informatics and Its Applications (ISIA) 2022

DOI: 10.1109/isia55826.2022.9993530

|View full text |Cite

Analysis of road accident factors using Decision Tree Algorithm: a case of study Algeria

Ouennoughi Nedjmedine

Tahar Mehenni

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Book1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Hybrid Railway Safety Optimization With Neural Networks and Random Forests

Kaviyalakshmi,

Chithra,

Anusuya

et al. 2024

Advances in Wireless Technologies and Telecommunication

View full text Add to dashboard Cite

Enhancing safety protocols in train stations requires proactive approaches to foresee and reduce any mishaps. In order to maximize safety in railroad environments, this study presents a novel machine learning framework that combines neural networks with random forest techniques. This hybrid model departs from traditional single-algorithm approaches by utilizing a variety of methods to enhance accident investigation and prediction. The chapter focuses on using this hybridized method, showcasing the functions of random forests and neural networks in predicting future events and assessing accident data from the past. The strategy of incorporating neural networks (multi-layer perceptron (MLP)) and random forests into the accident analysis framework is covered in the study. It also investigates if this model can be implemented in real-time and at scale within railway station safety systems. The results of the research reveal that the hybridized machine learning approach is a promising tool for improving safety standards in railroad stations.

show abstract

Hybrid Railway Safety Optimization With Neural Networks and Random Forests

Kaviyalakshmi,

Chithra,

Anusuya

et al. 2024

Advances in Wireless Technologies and Telecommunication

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Analysis of road accident factors using Decision Tree Algorithm: a case of study Algeria

Cited by 1 publication

References 13 publications

Hybrid Railway Safety Optimization With Neural Networks and Random Forests

Hybrid Railway Safety Optimization With Neural Networks and Random Forests

Contact Info

Product

Resources

About