Computing Trajectory Similarity in Linear Time: A Generic Seed-Guided Neural Metric Learning Approach

• Published in: Data engineering pp. 1358 - 1369
• Main Authors: Yao, Di, Cong, Gao, Zhang, Chao, Bi, Jingping
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2019
• Subjects: Acceleration; Approximation algorithms; deep metric learning; Indexing; linear time; Measurement; Task analysis; Trajectory; trajectory similarity
• ISSN: 2375-026X
• DOI: 10.1109/ICDE.2019.00123

Trajectory similarity computation is a fundamental problem for various applications in trajectory data analysis. However, the high computation cost of existing trajectory similarity measures has become the key bottleneck for trajectory analysis at scale. While there have been many research efforts for reducing the complexity, they are specific to one similarity measure and often yield limited speedups. We propose NeuTraj to accelerate trajectory similarity computation. NeuTraj is generic to accommodate any existing trajectory measure and fast to compute the similarity of a given trajectory pair in linear time. Furthermore, NeuTraj is elastic to collaborate with all spatial-based trajectory indexing methods to reduce the search space. NeuTraj samples a number of seed trajectories from the given database, and then uses their pair-wise similarities as guidance to approximate the similarity function with a neural metric learning framework. NeuTraj features two novel modules to achieve accurate approximation of the similarity function: (1) a spatial attention memory module that augments existing recurrent neural networks for trajectory encoding; and (2) a distance-weighted ranking loss that effectively transcribes information from the seed-based guidance. With these two modules, NeuTraj can yield high accuracies and fast convergence rates even if the training data is small. Our experiments on two real-life datasets show that NeuTraj achieves over 80% accuracy on Fre chet, Hausdorff, ERP and DTW measures, which outperforms state-of-the-art baselines consistently and significantly. It obtains 50x-1000x speedup over bruteforce methods and 3x-500x speedup over existing approximate algorithms, while yielding more accurate approximations of the similarity functions.