Towards a Tighter Bound on Possible-Rendezvous Areas: Preliminary Results

TL;DR

This paper introduces novel algorithms, TGARD and DC-TGARD, to improve the accuracy and efficiency of detecting potential rendezvous areas in trajectory data with gaps, reducing manual effort in analysis.

Contribution

It presents a new method for tightening spatial bounds on rendezvous areas and an efficient bi-directional pruning algorithm for large-scale trajectory analysis.

Findings

DC-TGARD is more scalable than TGARD.

The proposed bounds are tighter than existing space-time prism bounds.

Experimental results validate improved efficiency and accuracy.

Abstract

Given trajectories with gaps, we investigate methods to tighten spatial bounds on areas (e.g., nodes in a spatial network) where possible rendezvous activity could have occurred. The problem is important for reducing the onerous amount of manual effort to post-process possible rendezvous areas using satellite imagery and has many societal applications to improve public safety, security, and health. The problem of rendezvous detection is challenging due to the difficulty of interpreting missing data within a trajectory gap and the very high cost of detecting gaps in such a large volume of location data. Most recent literature presents formal models, namely space-time prism, to track an object's rendezvous patterns within trajectory gaps on a spatial network. However, the bounds derived from the space-time prism are rather loose, resulting in unnecessarily extensive post-processing manual effort. To address these limitations, we propose a Time Slicing-based Gap-Aware Rendezvous Detection (TGARD) algorithm to tighten the spatial bounds in spatial networks. We propose a Dual Convergence TGARD (DC-TGARD) algorithm to improve computational efficiency using a bi-directional pruning approach. Theoretical results show the proposed spatial bounds on the area of possible rendezvous are tighter than that from related work (space-time prism). Experimental results on synthetic and real-world spatial networks (e.g., road networks) show that the proposed DC-TGARD is more scalable than the TGARD algorithm.