Development of an Artificial Neural Network Model for Predicting Speeding Behaviour: A Case Study from Indonesia

Abstract

Traffic accidents are the third leading cause of death in Indonesia, with speeding behavior being the predominant human factor responsible for most fatal outcomes. Early detection of drivers’ propensity to speed is therefore essential for effective prevention strategies. This study develops an Artificial Neural Network (ANN) model to predict the tendency of drivers to speed on intercity roads using a labeled questionnaire dataset comprising 14 input variables. The dataset was divided into training, validation, and testing subsets, where the validation set was used for hyperparameter tuning, while the testing set was used for final evaluation on unseen data. The model was trained using the Adam optimizer with a binary cross-entropy loss function. The optimal configuration consists of a single hidden layer with 12 neurons using ReLU activation, a Sigmoid output layer, 750 training epochs, and a learning rate of 0.03. The final model achieved an accuracy of 86.67% and a Cohen’s kappa value of 0.7339, which indicates strong predictive reliability. These findings demonstrate the model’s potential as a valuable tool for relevant stakeholders to identify high-risk drivers and design targeted interventions. As a result, the model can be used to proactively reduce speeding-related traffic accidents and improve road safety on intercity routes.