Abstract

Road traffic flow produces an undesirable externality since it distorts the ambient environmental noise, especially in cities. Such nuisance noise poses a risk to the health of the inhabitants. Globally, the combined concert of the forces of urbanization and road transport motorization has intensified the noise pollution challenge, yet, locally adapted predictive tools remain limited. In Nairobi, the capital city of Kenya, Road Traffic Noise (RTN) remains a less understood environmental nuisance. To date, no predictive RTN models have been developed, while established models such as CoRTN and RLS-90 lack applicability to Nairobi’s traffic and environmental conditions. This study aimed to develop an accurate smart model leveraging artificial neural networks (ANNs) to forecast RTN levels using traffic information data. Traffic data, including audio recordings using a Samsung Galaxy A12 Model SM-A127F/DS Android Smartphone, equivalent noise levels (Leq) using a Lutron SL-4033SD Class 1 Sound Level Meter (SLM), vehicular volume using a manual tally form, and speed using a speed gun, was collected across 42 locations within Nairobi. Using this data, an Artificial Neural Network (ANN), Multi-Layer Perceptron (MLP) model, was developed with two hidden layers. Hyperparameter tuning via grid search was done to optimize model performance. The model achieved a Mean Absolute Error (MAE) of 0.97 dBA and an R2 value of 0.90, outperforming traditional statistical models like CoRTN with a MAE of 5.0 dBA and RLS-90 with a MAE of 11.0 dBA. These results highlight the model’s high accuracy in predicting Nairobi’s RTN. The model’s deployment on a web-based dashboard enables real-time noise monitoring and stakeholder engagement. This pioneering smart predictive model for Nairobi offers a scalable solution for urban noise management, with potential applications in traffic planning and policy implementation.