Forecasting Urban Traffic Patterns in London Using Hybrid AI Techniques

Abstract

Accurately forecasting traffic incident severity is crucial for urban mobility planning and real-time traffic management. This thesis explores a hybrid approach to classifying traffic severity levels using statistical and machine learning techniques. The dataset includes road segment-level hourly traffic observations in London, enriched with engineered features such as recent severity history, weather conditions, and baseline severity probabilities. We evaluate a range of models, from simple baselines to advanced classifiers, with a focus on Random Forest and XGBoost. After extensive experimentation, a tuned Random Forest model using balanced subsampling and moderate tree depth outperformed all other approaches in terms of macro-averaged F1-score and minority class recall. Detailed evaluation through time-based cross-validation, SHAP analysis, and visual diagnostics demonstrates the robustness of this model and highlights key predictive factors. The findings suggest that combining short-term temporal features with baseline statistical probabilities significantly improves performance, particularly for under-represented severity classes. The report also discusses limitations related to data coverage, class imbalance, and the potential of incorporating external signals such as incidents or public transport disruptions in future work. The corresponding python notebooks, scripts and data for this thesis are located in this GitHub repository: https://github.com/theol-10/datascience-thesis/.