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

Abstract

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).

Keywords

• vulnerable road user
• accident frequency
• artificial neural network
• support vector machine and gaussian processes