Rapid advances in positioning and monitoring technologies have significantly enhanced the ability to track dynamic moving objects for studying movement patterns. As an emerging field in spatiotemporal data mining, a well-established taxonomy of movement patterns facilitates tasks that mine movement patterns. In this study, we proposed a novel classification framework with the 5W1H1R (who, what, when, where, why, how, and relationships) principle and a bottom-up multi-level cognitive model to support the taxonomy of movement patterns. Guided by first principles thinking and combinatorics, we differentiated basic patterns categorized along spatial, temporal, and motion attribute dimensions from compound patterns composed of these basic patterns. We summarized five key constraints to refine movement patterns over recently studied pattern types. We validated our framework in three domains and compared it with four existing frameworks in five aspects. The results demonstrate the broader coverage, extensibility, and adaptability of the framework. Our classification framework can adapt to various moving objects, application domains, and movement data at different scales and resolutions. It can serve as a conceptual and ontological foundation for guiding the mining and analysis of movement patterns and, especially, for developing models to detect multimodal movement patterns.

Trajectory clustering is a fundamental yet challenging data mining task that aims to group similar trajectories. Due to the inherent nature and implicit patterns of trajectories, existing methods often struggle to cluster trajectories with varying densities and noise, and automatically determine cluster numbers. We propose an adaptive two-stage trajectory cluster (ATSTC) algorithm considering the intra-cluster trajectory distance distributions. In the first stage, peak trajectories with the highest local densities, and their adaptively estimated k-nearest neighbors, are initialized as candidate clusters. Trajectories in multiple peak neighborhoods are assigned to clusters with minimal relative distances while remaining trajectories are merged with the nearest cluster or labeled as noise depending on the resultant changes in intra-cluster distance standard deviation. In the second stage, a hierarchical agglomerative strategy is employed to merge clusters by analyzing changes in the average distance and standard deviation of intra-cluster trajectories before and after merging. Experiments on four simulated datasets, with comparisons to eight baselines, demonstrate the superior performance (e.g. ARI) of ATSTC in detecting trajectory clusters under different scenarios. Case studies involving route extraction from ship trajectories and bird tracks with clusters of different sizes, densities, and noise underscore the potential and efficacy of ATSTC in real-world applications.

Recognizing common travel paths of crowds in a road network is valuable for understanding human mobility patterns and developing intelligent ride-sharing services. To achieve this, it is critical to measure the similarity of their trajectories. Although many measures have been proposed in the past decades, they often ignore movement consistency, exhibit one or more deficiencies in the face of noise and misaligned trajectories, or require extra parameters to tune predictions. In this paper, we propose an improved similarity measure called the directed segment path distance (DSPD), which considers the spatial proximity and movement consistency of trajectories. By integrating the spatial proximity distance and moving direction similarity between trajectories, the DSPD is a competitive parameter-free similarity measure that can effectively distinguish trajectories with different movement characteristics. To verify the effectiveness of the DSPD, we conducted a quantitative comparative study between the DSPD measure and 11 state-of-the-art trajectory similarity measures on six simulated trajectory datasets and applied the DSPD to two typical application scenarios: trajectory clustering for road network generation and retrieving common trajectories for ride-sharing path planning. The results demonstrate the effectiveness, robustness, and superiority of the DSPD and its great potential in trajectory search, clustering, and classification.

Road network selection plays a key role in map generalization for creating multi-scale road network maps. Existing methods usually determine road importance based on road geometric and topological features, few evaluate road importance from the perspective of road utilization based on human travel data, ignoring the functional values of roads, which leads to a mismatch between the generated results and people’s needs. This paper develops two functional semantic features (i.e. travel path selection probability and regional attractiveness) to measure the functional importance of roads and proposes an automatic road network selection method based on graph convolutional networks (GCN), which models road network selection as a binary classification. Firstly, we create a dual graph representing the source road network and extract road features including six graphical and two functional semantic features. Then, we develop an extended GCN model with connectivity loss for generating multi-scale road networks and propose a refinement strategy based on the road continuity principle to ensure road topology. Experiments demonstrate the proposed model with functional features improves the quality of selection results, particularly for large and medium scale maps. The proposed method outperforms state-of-the-art methods and provides a meaningful attempt for artificial intelligence models empowering cartography.