Unsupervised Machine Learning Based Anomaly Detection in Stockholm Road Traffic

Abstract: This thesis is a study of anomaly detection in vehicle traffic data in central Stockholm. Anomaly detection is an important tool in the analysis of traffic data for improved urban planing. Two unsupervised machine learning models are used, the DBSCAN clustering model and the LSTM deep learning neural network. A modified version of the models is also employed, incorporating adaptations that exploit diurnal traffic variations to improve the quality of the results. Subsequently, the model performance is analysed and compared. For evaluating the models, we employed two types of synthetic anomalies: a straightforward one and a more complex variant. The results indicate that all models show some ability to detect both anomalies. The models show better performance on the simpler anomaly, with both LSTM and DBSCAN giving comparable results. In contrast, LSTM outperforms DBSCAN on the more complex anomaly. Notably, the modified versions of both models consistently show enhanced performance. This suggest that LSTM outperforms DBSCAN as anomalies become more complex, presumably owing to LSTM’s proficiency in identifying intricate patterns. However, this relationship warrants further investigation in future research.