A Scalable Framework for Trajectory Prediction

Abstract

Trajectory prediction (TP) is of great importance for a wide range of location-based applications in intelligent transport systems, such as location-based advertising, route planning, traffic management, and early warning systems. In the last few years, the widespread use of GPS navigation systems and wireless communication technology enabled vehicles has resulted in huge volumes of trajectory data. The task of utilizing these data employing spatio-temporal techniques for TP in an efficient and accurate manner is an ongoing research problem. Existing TP approaches are limited to the short-term predictions. Moreover, they cannot handle a large volume of trajectory data for long-term prediction. To address these limitations, we propose a scalable clustering and Markov chain-based hybrid framework, called Traj-clusiVAT-based TP, for both short- and long-term TPs, which can handle a large number of overlapping trajectories in a dense road network. Traj-clusiVAT can also determine the number of clusters, which represent different movement behaviors in input trajectory data. In our experiments, we compare our proposed approach with a mixed Markov model-based scheme and a trajectory clustering, NETSCAN-based TP method for both short- and long-term TPs. We performed our experiments on two real, vehicle trajectory datasets, including a large-scale trajectory dataset consisting of 3.28 million trajectories obtained from 15 061 taxis in Singapore over a period of one month. The experimental results on two real trajectory datasets show that our proposed approach outperforms the existing approaches in terms of both short- and long-term prediction performances, based on the prediction accuracy and distance error (in km).