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：交通运输公司和机构 routinely 收集大量乘客运输数据。通过聚合这些数据（例如，每30分钟间隔统计从一个地点到另一个地点的乘客数量），可以分析大都市区域内乘客的移动行为。本文研究了在不同粒度下发现乘客移动重要趋势的问题，这在目标营销、调度和出行意图预测等广泛应用中具有重要价值。具体而言，我们研究了具有显著流量的区域间移动模式的提取。由于可能模式数量庞大，其检测在计算上具有挑战性。我们提出了能够大幅缩减搜索空间和模式检测计算成本的算法。同时，我们研究了针对不同问题实例有用模式的变体，例如约束模式和前k排序模式。