对城市热点地区进行基于污名的分析，以发现城市交通使用的趋势和异常情况
A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage
摘要：
可持续城市交通系统的一个关键方面是交通政策的有效性。为了有效，政策必须考虑广泛的因素，例如污染排放、交通流量和人员流动性。由于这些因素在城市地区的复杂性和多变性，制定有效的政策仍然是一项非常具有挑战性的任务。随着智慧城市模式的引入，可以在城市空间中生成大量可用的数据。这些数据可以成为改进政策的基本知识来源，因为它们可以反映城市的可持续性问题。在这种情况下，我们提出了一种方法来利用城市定位数据的基础上stigmergy，一个生物启发的机制，提供标量和时间聚集的样本。通过使用stigmergy，彼此接近的样本被聚集成一个功能结构，称为trail。线索总结了数据中的相关动态，并允许匹配它们，提供了它们相似性的度量。此外，这种机制可以专门用于展开特定的动态。具体来说，我们识别高密度的城市地区（即热点），分析它们随时间的活动，并展开异常。此外，通过匹配活动模式，提供了相对于典型活动模式的相异性的连续测量。该指标可供政策制定者用来评估政策的效果，并动态地改变政策。作为案例研究，我们分析了2013年至2015年在曼哈顿收集的出租车出行数据。
英文摘要：
A key aspect of a sustainable urban transportation system is the effectiveness of transportation policies. To be effective, a policy has to consider a broad range of elements, such as pollution emission, traffic flow, and human mobility. Due to the complexity and variability of these elements in the urban area, to produce effective policies remains a very challenging task. With the introduction of the smart city paradigm, a widely available amount of data can be generated in the urban spaces. Such data can be a fundamental source of knowledge to improve policies because they can reflect the sustainability issues underlying the city. In this context, we propose an approach to exploit urban positioning data based on stigmergy, a bio-inspired mechanism providing scalar and temporal aggregation of samples. By employing stigmergy, samples in proximity with each other are aggregated into a functional structure called trail. The trail summarizes relevant dynamics in data and allows matching them, providing a measure of their similarity. Moreover, this mechanism can be specialized to unfold specific dynamics. Specifically, we identify high-density urban areas (i.e hotspots), analyze their activity over time, and unfold anomalies. Moreover, by matching activity patterns, a continuous measure of the dissimilarity with respect to the typical activity pattern is provided. This measure can be used by policy makers to evaluate the effect of policies and change them dynamically. As a case study, we analyze taxi trip data gathered in Manhattan from 2013 to 2015.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
