Personalized travel time estimation for urban road networks: A tensor-based context-aware approach

Urban travel time estimation is of significant importance at many levels of traffic operation and transportation management. This paper develops a tensor-based context-aware approach to dynamically provide personalized travel time estimation from a citywide perspective, using sparse and large-scale GPS trajectories. This novel model is comprised of four major components: map matching, travel time tensor construction, context-aware feature extraction, and travel time tensor factorization. First, GPS trajectories are map-matched onto the road network. Then, travel times of different drivers on different road segments in different time slots are modeled with a 3-order tensor. Following these, three categories of context features, i.e., historical, geographical and spatial-temporal features, are extracted to capture the contextual information of travel time and traffic condition in the road network. Finally, an objective function is devised to factorize travel time tensors with context features collaboratively. In addition, a gradient-based algorithm is developed to find an optimal solution for the context-aware estimation model. The novel model incorporates both the spatial correlation between different road segments and the deviation between different drivers, as well as the fine-grain temporal correlation between different time slots and the coarse-grain temporal correlation between recent and historical traffic conditions. The proposed model is applied in a real case on the urban road network of Beijing, China, based on the sparse and large-scale GPS trajectories collected from over 32,000 drivers in a period of 2 months. Empirical results on extensive experiments demonstrate that the proposed model provides an effective and robust approach for citywide personalized travel time estimation, and outperforms the competing methods.