A Comparative Analysis of Traffic Accident Frequency Models for Motorway Tunnels Using Machine Learning

Motorway tunnels are critical elements of the motorway network where traffic accidents, although less frequent than on open sections, often have more severe consequences. This paper develops a model for expected accident frequency in motorway tunnels based on a traditional Negative Binomial (NB) regression model and two machine-learning–based models: Neural Networks (NN) and Random Forest (RF). The study uses historical accident and traffic data for all motorway tunnels between 2013 and 2024, combined with key infrastructural characteristics. The analysis considers all recorded traffic accidents in motorway tunnels, including accidents with material damage only as well as injury-related accidents of varying severity. A stepwise procedure was used to determine the optimal NB model, resulting in a final specification with tunnel length and Annual Average Daily Traffic (AADT) as predictors. Machine-learning–based models were trained on the same input set and evaluated against the NB model using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and log-likelihood. The NN model achieved the lowest MAE (1.03 accidents/year), followed by RF (1.07) and NB (1.13), confirming that machine-learning-based (ML-based) models slightly improved predictive accuracy while maintaining interpretability at the network level. Compared to the NB model, the NN model achieved a reduction in Mean Absolute Error of 9.2%, while the RF model achieved a reduction of 5.1%. A detailed case study of the Trojane tunnel demonstrates that all models reproduce long-term accident trends, while machine-learning-based models better capture variations between years. The proposed modelling framework provides a practical decision–support tool for tunnel operators and policy makers by supporting tunnel risk classification, prioritization of safety investments, and medium-term safety planning. By supporting proactive tunnel safety planning and evidence-based prioritization of safety investments, the proposed framework contributes to sustainable transport infrastructure management. Improved prediction of accident frequency enables more efficient allocation of resources, reduction in accident-related social and economic costs, and enhanced long-term resilience of motorway tunnel systems.