论文标题
基于传感器的时空事件检测方法
A Sensor-Based Simulation Method for Spatiotemporal Event Detection
论文作者
论文摘要
城市地区的人类运动对于了解人类环境相互作用至关重要。但是，由于城市的复杂性，活动和相关的运动充满了不确定性。在本文中，我们提出了一种基于离散的经验插值方法的新型传感器方法，用于时空事件检测。具体而言，我们首先识别定义为“传感器”的关键位置，它们与整个数据集具有最强的相关性。然后，我们使用这些关键LO-cations的观察数据点模拟了常规的平稳场景。通过比较模拟和观察方案，在空间和时间上提取事件。我们在纽约市使用出租车旅行记录数据应用这种方法。结果表明，此方法有效地检测发生事件的何时何地。
Human movements in urban areas are essential to understand human-environment interactions. However, activities and associated movements are full of uncertainties due to the complexity of a city. In this paper, we propose a novel sensor-based approach for spatiotemporal event detection based on the Discrete Empirical Interpolation Method. Specifically, we first identify the key locations, defined as 'sensors' , which have the strongest correlation with the whole dataset. We then simulate a regular uneventful scenario with the observation data points from those key lo-cations. By comparing the simulated and observation scenarios, events are extracted both spatially and temporally. We apply this method in New York City with taxi trip record data. Results show that this method is effective in detecting when and where events occur.
