A Decade Bibliometric Review of Road Traffic Accidents and Incidents: A Computational Perspective

Makaba, Tebogo; Gatsheni, Barnabas

doi:10.1109/csci49370.2019.00098

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…The avoidance of road traffic accidents has become a transportation management priority. Using various data mining techniques, initial and secondary collisions can be determined and studied to aid transport authorities and contribute to reducing the high number of roadway injuries and fatalities [ [ 9 , 10 , 11 , 12 ], and [ 13 ]].…”

Section: Introductionmentioning

confidence: 99%

Time series traffic collision analysis of London hotspots: Patterns, predictions and prevention strategies

Balawi,

Tenekeci

2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

Time series traffic collision analysis of London hotspots: Patterns, predictions and prevention strategies

Balawi,

Tenekeci

2024

Heliyon

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“…The prevention of RTAs has become a priority in transportation management. Initial and secondary accidents can be identified and investigated using different data mining techniques that can help support transport authorities and contribute to reducing the high number of road injuries and fatalities [6,[8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Machine Learning Classifiers for Modelling Road Traffic Accidents

2022

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Road traffic accidents (RTAs) are a major cause of injuries and fatalities worldwide. In recent years, there has been a growing global interest in analysing RTAs, specifically concerned with analysing and modelling accident data to better understand and assess the causes and effects of accidents. This study analysed the performance of widely used machine learning classifiers using a real-life RTA dataset from Gauteng, South Africa. The study aimed to assess prediction model designs for RTAs to assist transport authorities and policymakers. It considered classifiers such as naïve Bayes, logistic regression, k-nearest neighbour, AdaBoost, support vector machine, random forest, and five missing data methods. These classifiers were evaluated using five evaluation metrics: accuracy, root-mean-square error, precision, recall, and receiver operating characteristic curves. Furthermore, the assessment involved parameter adjustment and incorporated dimensionality reduction techniques. The empirical results and analyses show that the RF classifier, combined with multiple imputations by chained equations, yielded the best performance when compared with the other combinations.

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An Intelligent Forecasting Classification Model for Traffic Analysis using Machine Learning Techniques

Kishor,

Tiwari,

Patra

et al. 2024

2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)

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A Decade Bibliometric Review of Road Traffic Accidents and Incidents: A Computational Perspective

Cited by 4 publications

References 13 publications

Time series traffic collision analysis of London hotspots: Patterns, predictions and prevention strategies

Time series traffic collision analysis of London hotspots: Patterns, predictions and prevention strategies

Comparative Study of Machine Learning Classifiers for Modelling Road Traffic Accidents

An Intelligent Forecasting Classification Model for Traffic Analysis using Machine Learning Techniques

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