Retrieving Similar Trajectories from Cellular Data at City Scale
This work addresses the challenge of mobility analysis using cellular data, which offers broader coverage than GPS, but is incremental in improving accuracy for specific applications like transportation planning.
The paper tackles the problem of retrieving similar trajectories from cellular data at city scale, developing a system called cellSim that integrates map matching and similarity search to handle large localization errors and low sample rates, achieving high accuracy with 62.4% precision and 89.8% recall on real-world data.
Retrieving similar trajectories from a large trajectory dataset is important for a variety of applications, like transportation planning and mobility analysis. Unlike previous works based on fine-grained GPS trajectories, this paper investigates the feasibility of identifying similar trajectories from cellular data observed by mobile infrastructure, which provide more comprehensive coverage. To handle the large localization errors and low sample rates of cellular data, we develop a holistic system, cellSim, which seamlessly integrates map matching and similar trajectory search. A set of map matching techniques are proposed to transform cell tower sequences into moving trajectories on a road map by considering the unique features of cellular data, like the dynamic density of cell towers and bidirectional roads. To further improve the accuracy of similarity search, map matching outputs M trajectory candidates of different confidence, and a new similarity measure scheme is developed to process the map matching results. Meanwhile, M is dynamically adapted to maintain a low false positive rate of the similarity search, and two pruning schemes are proposed to minimize the computation overhead. Extensive experiments on a large-scale dataset and real-world trajectories of 1701 km reveal that cellSim provides high accuracy (precision 62.4% and recall of 89.8%).