Short-term traffic prediction under normal and abnormal conditions

Intelligent Transport Systems (ITS) is a field that has developed rapidly over the last two decades, driven by the growing need for better transport network management strategies and by continuing improvements in computing power. However, a number of ITS applications, such as Advanced Traveller Information Systems (ATIS), Dynamic Route Guidance (DRG) and Urban Traffic Control (UTC) need to be proactive rather than reactive, and consequently require the prediction of traffic state variables into the short-term future. Similarly, individual travellers can use this predictive information to plan their mobility more efficiently. This PhD thesis develops models that are able to accurately predict short-term traffic variables such as link travel time and traffic flow on urban arterial roads under both normal and abnormal traffic conditions. This research first reviews the state of the art in data prediction applications in engineering domains especially traffic engineering and presents existing statistical and machine learning methods and their applications in relation to short-term traffic prediction. This review establishes that most existing work has focused on the apparent superiority of one individual statistical or machine learning method over another. Little attention has been paid, however, to the issues surrounding the overall structure of prediction models, in particular in relation to data smoothing and error feedback. In developing a short-term traffic prediction model, therefore, a 3-stage framework including a data smoothing step and an error feedback mechanism is proposed. This proposed framework is applied in conjunction with five different machine learning methods to develop a range of short-term traffic prediction methods. The proposed prediction framework is then tested under different traffic conditions using traffic data generated from a traffic simulation model of a corridor in Southampton. The prediction results show that the proposed 3-stage prediction framework can improve the accuracy of traffic prediction, regardless of the machine learning method used under both normal and abnormal traffic conditions. After demonstrating the effectiveness of predicting traffic variables using simulated data, the proposed methodology is then applied to real-world traffic data collected from different sites in London and Maidstone. These results also show that the framework can improve the accuracy of prediction regardless of the machine learning tool used. The prediction accuracy comparison shows that the proposed 3-stage prediction framework can improve the prediction accuracy for either travel time or traffic flow data under both normal and abnormal traffic conditions. In addition, the results indicate that the kNN based prediction method, when applied through the proposed framework, outperforms other selected machine learning methods under abnormal traffic conditions on urban roads. The findings suggest that, in order to arrive at a robust and accurate prediction model, attention should be paid to combining data smoothing, model structure and error feedback elements.