A Machine Learning Approach to Analyze Road Accidents

Goel, Amit; Khan, Kashaf; Kushwaha, Anmol; Srivastava, Vartika; Malik, Sharyar; Singh, Anjali

doi:10.1109/icbds53701.2022.9935867

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Artificial Neural Network1

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2024

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“…Many of these studies suggest that artificial intelligence techniques exhibit considerably high accuracy. Numerous researchers employed the M5P model tree, artificial neural network, random forest model [6][7][8], with these approaches yielding the best-suited results. This research aims to predict the total VRU Accident frequency by comparing it using GP, SVM, and ANN methods.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Estimation of Vulnerable Road User Accident Frequency through the Soft Computing Models

Jaglan,

Kumari,

Aggarwal

2024

Communications

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Accident prediction models are mathematical expressions or algorithms used to determine the causal factors for road accidents and road safety engineers are using these models, as well. Modelling this kind of accident is quite challenging and required good quality of data. The results of the artificial neural network model, Gaussian processes model, and support vector machine model are compared for vulnerable road accident frequency in this study. The accident frequency dataset comprises 218 records, with 146 designated for training purposes and 72 reserved for testing. The model's accuracy was contingent on: the mean absolute error, root mean square error and coefficient of correlation. The findings suggest that for predicting the vulnerable road user accidents on roads, the artificial neural network gives better correlation results as (0.912) that the support vector machine (0.879) and Gaussian processes (0.853).

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Section: Artificial Neural Networkmentioning

confidence: 99%