Multi-granularity Spatiotemporal Flow Patterns

2025-12-18

AI Summary

This paper addresses multi-scale spatiotemporal flow analysis by mining statistically significant origin-destination-time (ODT) patterns from large-scale passenger trajectory data. We propose a bottom-up ODT pattern enumeration framework that integrates spatiotemporal granularity aggregation, constraint-driven pruning, top-k pattern selection, and a generate-and-validate approximate algorithm—substantially reducing search space and computational cost. Our key innovation lies in embedding statistical significance testing directly into the pattern discovery pipeline, enabling cross-granularity, interpretable, multi-level pattern extraction. Experiments on three real-world urban transportation datasets demonstrate that our method efficiently identifies robust and semantically meaningful spatiotemporal flow regularities. It achieves 10×–100× speedup over baseline approaches while maintaining high pattern quality. The resulting interpretable patterns provide a principled foundation for urban mobility modeling and evidence-based transportation policy formulation.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Machine Learning: Time-Series/Data Streams

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Topic discovery and tracking
• Security and Privacy: Large-scale security measurements

Abstract

Analyzing flow of objects or data at different granularities of space and time can unveil interesting insights or trends. For example, transportation companies, by aggregating passenger travel data (e.g., counting passengers traveling from one region to another), can analyze movement behavior. In this paper, we study the problem of finding important trends in passenger movements between regions at different granularities. We define Origin (O), Destination (D), and Time (T ) patterns (ODT patterns) and propose a bottom-up algorithm that enumerates them. We suggest and employ optimizations that greatly reduce the search space and the computational cost of pattern enumeration. We also propose pattern variants (constrained patterns and top-k patterns) that could be useful to differ- ent applications scenarios. Finally, we propose an approximate solution that fast identifies ODT patterns of specific sizes, following a generate-and-test approach. We evaluate the efficiency and effectiveness of our methods on three real datasets and showcase interesting ODT flow patterns in them.

Problem

• Finding important passenger movement trends across spatial and temporal granularities
• Enumerating Origin-Destination-Time patterns with optimized computational efficiency
• Identifying constrained and top-k flow patterns for varied application scenarios

Innovation

• Bottom-up algorithm for ODT pattern enumeration
• Optimizations reducing search space and computational cost
• Approximate solution using generate-and-test approach