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.

翻译：交通运输公司及机构通常会收集海量的乘客运输数据。通过对这些数据进行聚合处理（例如，统计每30分钟间隔内从一个地点前往另一地点的乘客数量），我们得以分析大都市区域内的乘客移动行为。本文研究在不同粒度下发现乘客移动重要趋势的问题，该问题在目标营销、调度优化及出行意图预测等广泛领域具有重要应用价值。具体而言，我们聚焦于提取具有显著流量的区域间移动模式。由于可能存在海量模式，其检测在计算上具有挑战性。本文提出可大幅缩减搜索空间及模式检测计算成本的算法。我们研究适用于不同问题实例的模式变体，例如约束模式与Top-K排序模式。