Retrieving Similar Trajectories from Cellular Data at City Scale

TL;DR

This paper presents cellSim, a system that enables retrieval of similar trajectories from cellular data at city scale by addressing localization errors and low sampling rates through innovative map matching and similarity measures.

Contribution

The paper introduces a holistic system, cellSim, that effectively transforms cellular data into accurate trajectories and improves similarity search accuracy with novel techniques.

Findings

High accuracy in trajectory similarity search (precision 62.4%, recall 89.8%)

Effective handling of cellular data's unique features for map matching

Reduced computation overhead with pruning schemes

Abstract

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%).