Road traffic accident

Predictive modelling

Random forest

Algorithm

Road accident

Engineering

Philosophy

Transport engineering

Interpretation (philosophy)

Computer science

Accident (philosophy)

Epistemology

Artificial intelligence

Programming language

Road traffic

Machine learning

SN Applied Sciences

Road accident severity is a major concern of the world, particularly in underdeveloped countries. Understanding the primary and contributing factors may combat road traffic accident severity. This study identified insights and the most significant target specific contributing factors for road accident severity. To get the most determinant road accident variables, a hybrid K-means and random forest (RF) approaches developed. K-means extract hidden information from road accident data and creates a new feature in the training set. The distance between each cluster and the joining line of k1 and k9 calculated and selected maximum value as k. k is an optimal value for the partition of the training set. RF employed to classify severity prediction. After comparing with other classification techniques, the result revealed that among classification techniques, the proposed approach disclosed an accuracy of 99.86%. The target-specific model interpretation result showed that driver experience and day, light condition, driver age, and service year of the vehicle were the strong contributing factors for serious injury, light injury, and fatal severity, respectively. The outcome demonstrates the predictive supremacy of the approach in road accident prediction. Road transport and insurance agencies will be benefited from the study to develop road safety strategies.

Road accident prediction and model interpretation using a hybrid K-means and random forest algorithm approach