A Personalised Online Travel Time Prediction Model

Congestion slows road traffic. This has become a prominent urban road traffic problem. For commuters about to travel, or on route, accurate travel forecasts enable them to choose the right routes in a timely manner to avoid travel delays. In this paper, a personalised online travel time prediction model is proposed. The novelty of the work is threefold. First, commuters' travel status according to their movement status, OD (origin-destination) status and plan status can be identified. Second, a traffic data critical factor evaluator system is proposed to extract critical factors from raw traffic data that can predict travel time episodically. Third, travel information can be personalised to the individual commuter's current travel status. The evaluation of the proposed model is conducted with a Google Android mobile application prototype and traffic data from the city of Enschede. The results suggest that the model can provide commuters with accurate travel time prediction (>93%) by leveraging machine learning techniques such as a M5 tree model.