Preparing for Mobility Data Exploration

Pelekis, Nikos; Theodoridis, Yannis

doi:10.1007/978-1-4939-0392-4_6

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…Similarity measures can be roughly categorized as two types: spatial similarity and spatiotemporal similarity [14]. Spatial similarity views trajectories with similar geometric shapes as similar and ignores the information of time information contained in the trajectory data, such as Euclidean distance (ED) [15], Closest pair distance (CPD) [16], Ordered weighted distance (OWD) [17], Angle measurement of Hausdorff distance (HD) and shape similarity [18]. Spatiotemporal similarity takes into account both the spatial and temporal dimensions of trajectories, such as Dynamic time distortion (DTW) [19], Edit distance on real sequence (EDR) [20], and the Longest common subsequence (LCSS) [21].…”

Section: Similarity-measure-based Methodsmentioning

confidence: 99%

A Framework with Elaborate Feature Engineering for Matching Face Trajectory and Mobile Phone Trajectory

et al. 2023

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The advances in positioning techniques have generated massive trajectory data that represent the mobility of objects, e.g., pedestrians and mobile phones. It is important to integrate information from various modalities for subject tracking or trajectory prediction. Our work attempts to match a face with a corresponding mobile phone based on the heterogeneous trajectories. We propose a framework which associates face trajectories with their corresponding mobile phone trajectories using elaborate and explainable features. Our solution includes two stages: an initial selection of phone trajectories for a given face trajectory and a subsequent identification of which phone trajectory provides an exact match to the given face trajectory. In the first stage, we propose a Multi-Granularity SpatioTemporal Window Searching (MGSTWS) algorithm to select candidate mobile phones that are spatiotemporally close to a given face. In the second stage, we first build an affinity function to score face–phone trajectory point pairs selected by MGSTWS, and construct a feature set for building a face–phone trajectory matching determinator which determines whether a phone trajectory matches a given face trajectory. Our well-designed features guarantee high model simplicity and interpretability. Among the feature set, BGST intelligently leverages disassociation between a face and a mobile phone even if there exists some co-occurence for a non-matching face–phone pair. Based on the feature set, we represent the face–phone matching task as a binary classification problem and train various models, among which LightGBM achieves the best performance with 92.6% accuracy, 96.9% precision, 88.5% recall, and 92.5% F1. Our framework is acceptable in most application scenarios and may benefit some downstream tasks. The preselection-refining architecture of our framework guarantees the applicability and efficiency of the face–phone trajectory pair matching frame.

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