HTTE: A Hybrid Technique For Travel Time Estimation In Sparse Data Environments
This addresses travel time prediction for urban applications, but appears incremental as it builds on existing hybrid methods.
The paper tackles travel time estimation in sparse data environments by developing a hybrid algorithm that combines historical and real-time trajectory data, achieving effective results as demonstrated in experimental evaluation.
Travel time estimation is a critical task, useful to many urban applications at the individual citizen and the stakeholder level. This paper presents a novel hybrid algorithm for travel time estimation that leverages historical and sparse real-time trajectory data. Given a path and a departure time we estimate the travel time taking into account the historical information, the real-time trajectory data and the correlations among different road segments. We detect similar road segments using historical trajectories, and use a latent representation to model the similarities. Our experimental evaluation demonstrates the effectiveness of our approach.