Road Accident Analysis using Machine Learning

Patil, Jayesh; Prabhu, Mandar; Walavalkar, Dhaval; Lobo, Vivian Brian

doi:10.1109/punecon50868.2020.9362403

Cited by 26 publications

(9 citation statements)

References 6 publications

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“…(Vasavi 2016) Classification algorithm and Novel Criteria Based Method is used widely to improve the detection of accidents. (Patil et al 2020) proposed Novel Criteria Based Method of the system's performance is measured in terms of classification accuracy, and the results reveal that it has a lot of potential for forecasting the detection of accidents accurately. (Chen and Chen 2020) proposed a criteria based spatial grouping of applications as Novel Criteria Based Method with Decision Tree performed by other models by achieving 78.8% to predict accidents.…”

Section: )mentioning

confidence: 99%

Wrong-Lane Accidents Detection using Random Forest Algorithm in comparison with Decision Tree for Improved Accuracy

2022

pnr

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The proposed study aims to perform detection of wrong-lane accidents using Decision Tree (DT) algorithm and compare accuracy with Random Forest (RF) algorithm. Materials and Methods: Decision Tree is applied on a road accident dataset that consists of 1834 records. A Machine learning technique for the detection of wrong-lane accidents which compares Decision Tree and Random Forest has been proposed and developed. Sample size was calculated as 21 in each group using G power. Sample size was calculated using clinical analysis, with alpha and beta values of 0, 05 and 0.5, 95% confidence, 80% pre-test power and enrolment ratio is 1. The accuracy of the detection of wrong-lane accidents was evaluated and recorded. Results: The accuracy was maximum in detection of wrong-lane accidents using Decision Tree (89.88%) with minimum mean error when compared with Random Forest (89.77%) and attained significance value of p = 0.02 (p<=0.05). Conclusion: The study proves that Decision Tree Algorithm exhibits better accuracy than Random Forest in detection of wrong-lane accidents

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confidence: 99%

Wrong-Lane Accidents Detection using Random Forest Algorithm in comparison with Decision Tree for Improved Accuracy

2022

pnr

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“…Patil et al [2] analyze the road accident data in one of the most popular metropolitan cities in India, namely Bengaluru, using kmeans algorithm and machine learning. In their studies, they examined the accident prone areas or hot spots and their root causes.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting the Severity of the Road Accidents in Senegal: An Empirical Study

Dia

Faty

Sall³

et al. 2022

2022 the 5th International Conference on Data Storage and Data Engineering (DSDE)

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Road accidents are a major problem, the consequences of which have negative impacts on the economy, health and development of a country in general. The recrudescence of road accidents in Senegal during these last years has aroused a great debate in which certain responsibilities have been pointed out. A serious road traffic accident constitutes in most cases an emotional and economic disaster, hence the need to find sustainable solutions for the improvement of road safety. The objective of this work is to identify the major risk factors of road accidents in Senegal in order to set up a training corpus for the predictive study of the gravity of these accidents. According to our research on the bibliography, there is no study on the implementation of a corpus adapted to the data of accidents in Senegal. In this research, we used an exploratory study on the collected data to determine the factors most likely to cause accidents and supervised learning for predictive modeling of accident severity. The results showed that modelling with supervised learning can be used as an accident severity prediction system with promising accuracy.

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“…(Vasavi 2016) Classification algorithm and Innovative Kernel Based Approach is used widely to improve the detection of accidents. (Patil et al 2020) proposed Innovative Kernel Based Approach of the system's performance is studied in terms of the classification accuracy, and the results reveal that it has a lot of potential for forecasting the detection of accidents accurately. (Chen and Chen 2020) proposed a criteria based spatial grouping of applications as Innovative Kernel Based Approach with Support Vector Machine performed by other models by achieving 77.3% to predict accidents.…”

Section: )mentioning

confidence: 99%

Awareness of Wrong-Lane Accident Detection using Random Forest Compared with SVM Algorithm with Increased Accuracy

2022

pnr

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The proposed study aims to perform detection of wrong-lane accidents utilizing the Support Vector Machine (SVM) algorithm and compare accuracy with the Random Forest (RF) algorithm. Materials and Methods: Support Vector Machine is applied on a road accident dataset that consists of 1834 records. A machine learning strategy for detecting wrong-lane accidents has been suggested and developed that compares Support Vector Machine with Random Forest. Sample size was calculated as 21 in each group using G power. Sample size was calculated using clinical analysis, with alpha and beta values of 0, 05 and 0.5, 95% confidence, 80% pre-test power and enrolment ratio is 1. The accuracy of the detection of wrong-lane accidents was evaluated and recorded. Results: The accuracy was maximum in detection of wrong-lane accidents using Support Vector Machine (89.90%) with minimum mean error when compared with Random Forest (89.77%) and attained significance value of p = 0.02. Conclusion: The study proves that Support Vector Machine Algorithm exhibits better accuracy than Random Forest in detection of wrong-lane accidents .

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Road Accident Analysis using Machine Learning

Cited by 26 publications

References 6 publications

Wrong-Lane Accidents Detection using Random Forest Algorithm in comparison with Decision Tree for Improved Accuracy

Wrong-Lane Accidents Detection using Random Forest Algorithm in comparison with Decision Tree for Improved Accuracy

Predicting the Severity of the Road Accidents in Senegal: An Empirical Study

Awareness of Wrong-Lane Accident Detection using Random Forest Compared with SVM Algorithm with Increased Accuracy

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