Technology Category
Planning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsData Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal DataMachine Learning: Time-Series/Data Streams
Multi-granularity Spatiotemporal Flow Patterns
🤖 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
Application Category
📝 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
Research questions and friction points this paper is trying to address.
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
Methods, ideas, or system contributions that make the work stand out.
Bottom-up algorithm for ODT pattern enumeration
Optimizations reducing search space and computational cost
Approximate solution using generate-and-test approach
🔎 Similar Papers
No similar papers found.
