There is increasing demand for short-term urban link travel time prediction to build an advanced intelligent transportation system (ITS). With the development of data collection technology, probe data are receiving more attention but the penetration rate of probe vehicles capable of sending probe data is still limited. Most research pertaining to short-term travel time prediction tends to aggregate probe data to obtain useful samples when the penetration rate is low. However, as a result, the prediction can only provide a general description of the travel time and changes in travel time during a short time interval are neglected. To overcome this limitation, a non-parametric model using disaggregate probe data based on dynamic time warping (DTW) was developed in this study. Data from the crossing direction are introduced to separate the data into different signal phases instead of identifying the exact signal pattern. A classical k-nearest neighbor (KNN) model and a naïve model were compared with the proposed model. The models were tested in three scenarios: a computer simulation and two real cases from Nagoya, Japan. The results showed that the proposed model outperforms the other two models under different data penetration rates because it can reflect changes in travel time during a traffic signal cycle. Moreover, the proposed model has wider applicability than the KNN model because it is free from the equal time interval constraint. INDEX TERMS Travel time prediction, disaggregate probe data, short term, dynamic time warping, traffic signal cycle, penetration rate, urban link.

Short-term travel time prediction is an important consideration in modern traffic control and management systems. As probe data technology has developed, research interest has moved from highways to urban roads. Most research has only focused on improving the prediction accuracy on urban roads because it is the key index of evaluating a model. However, the low penetration rate of probe vehicles at urban networks may result in the low coverage rate which restricts prediction models from practical applications. This research proposed a non-parametric model based on Bayes’ theorem and a resampling process to predict short-term urban link travel time, which can enhance the coverage rate while maintaining the prediction accuracy. The proposed model used data from vehicles in both the target link and its crossing direction, so its coverage rate can be expanded, especially when the data penetration rate is low. In addition, the utilization of relationships between vehicles in both directions can reflect the influence of signal timing. The proposed model was evaluated in a computer simulation to test its robustness and reliability under different data penetration rates. The results implied that the proposed model has a high coverage rate, demonstrating stable and acceptable performance at different penetration rates.