Identifying patterns under both normal and abnormal traffic conditions for short-term traffic prediction

Abstract

Abstract: In this paper we propose a model for accurate traffic prediction under both normal and abnormal conditions. The model is based on the identification of the traffic patterns shown under both normal and abnormal conditions using the density-based clustering algorithm DBSCAN, and the use of different prediction models for each separate cluster that represents a traffic pattern. The k- Nearest Neighbor and the Support Vector Regression algorithms from the machine learning field and the ARIMA model from time series analysis were trained and tested. Preliminary experimental results indicate that the proposed model outperforms typical traffic prediction models from the relevant literature in terms of prediction accuracy under both normal and abnormal conditions.