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首页> 外文会议>International conference on social computing, behavioral-cultural modeling and prediction and behavior representation in modeling and simulation >Stigmergy-Based Modeling to Discover Urban Activity Patterns from Positioning Data
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Stigmergy-Based Modeling to Discover Urban Activity Patterns from Positioning Data

机译：基于Stigmergy的建模可从定位数据中发现城市活动模式

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Positioning data offer a remarkable source of information to analyze crowds urban dynamics. However, discovering urban activity patterns from the emergent behavior of crowds involves complex system modeling. An alternative approach is to adopt computational techniques belonging to the emergent paradigm, which enables self-organization of data and allows adaptive analysis. Specifically, our approach is based on stigmergy. By using stigmergy each sample position is associated with a digital pheromone deposit, which progressively evaporates and aggregates with other deposits according to their spatiotemporal proximity. Based on this principle, we exploit positioning data to identify high-density areas (hotspots) and characterize their activity over time. This characterization allows the comparison of dynamics occurring in different days, providing a similarity measure exploitable by clustering techniques. Thus, we cluster days according to their activity behavior, discovering unexpected urban activity patterns. As a case study, we analyze taxi traces in New York City during 2015.
机译：定位数据为分析人群的城市动态提供了重要的信息来源。但是，从人群的突发行为中发现城市活动模式涉及复杂的系统建模。一种替代方法是采用属于新兴范式的计算技术，该技术可实现数据的自组织并允许自适应分析。具体来说，我们的方法是基于耻辱感。通过使用电离能，每个样本位置都与数字信息素沉积物关联，数字信息素沉积物会根据其时空邻近性逐渐与其他沉积物蒸发并聚集在一起。基于此原理，我们利用定位数据来识别高密度区域（热点）并描述其随时间变化的活动。此特征允许比较不同日期发生的动态，从而提供可用于聚类技术的相似性度量。因此，我们根据天的活动行为对日进行聚类，发现意想不到的城市活动模式。作为案例研究，我们分析了2015年纽约市的出租车痕迹。

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