Spatio-temporal flow patterns

TL;DR

This paper presents algorithms for efficiently detecting significant passenger movement patterns in large-scale transportation data, aiding applications like marketing and scheduling.

Contribution

It introduces novel algorithms that reduce computational complexity for extracting important spatio-temporal flow patterns from transportation data.

Findings

Algorithms significantly reduce search space and computation time.

Effective detection of constrained and top-k movement patterns.

Applicable to various transportation analysis tasks.

Abstract

Transportation companies and organizations routinely collect huge volumes of passenger transportation data. By aggregating these data (e.g., counting the number of passengers going from a place to another in every 30 minute interval), it becomes possible to analyze the movement behavior of passengers in a metropolitan area. In this paper, we study the problem of finding important trends in passenger movements at varying granularities, which is useful in a wide range of applications such as target marketing, scheduling, and travel intent prediction. Specifically, we study the extraction of movement patterns between regions that have significant flow. The huge number of possible patterns render their detection computationally hard. We propose algorithms that greatly reduce the search space and the computational cost of pattern detection. We study variants of patterns that could be useful to different problem instances, such as constrained patterns and top-k ranked patterns.