The recent proliferation of real-world human mobility datasets has catalyzed geospatial and transportation research in trajectory prediction, demand forecasting, travel time estimation, and anomaly detection. However, these datasets also enable, more broadly, a descriptive analysis of intricate systems of human mobility. We formally define patterns of life analysis as a natural, explainable extension of online unsupervised anomaly detection, where we not only monitor a data stream for anomalies but also explicitly extract normal patterns over time. To learn patterns of life, we adapt Grow When Required (GWR) episodic memory from research in computational biology and neurorobotics to a new domain of geospatial analysis. This biologically-inspired neural network, related to self-organizing maps (SOM), constructs a set of "memories" or prototype traffic patterns incrementally as it iterates over the GPS stream. It then compares each new observation to its prior experiences, inducing an online, unsupervised clustering and anomaly detection on the data. We mine patterns-of-interest from the Porto taxi dataset, including both major public holidays and newly-discovered transportation anomalies, such as festivals and concerts which, to our knowledge, have not been previously acknowledged or reported in prior work. We anticipate that the capability to incrementally learn normal and abnormal road transportation behavior will be useful in many domains, including smart cities, autonomous vehicles, and urban planning and management.
翻译:最近真实世界人类流动数据集的扩散催化了地球空间和运输研究在轨迹预测、需求预测、旅行时间估计和异常探测方面的轨迹预测、需求预测、旅行时间估计和异常探测等方面的研究。然而,这些数据集还能够更广泛地对复杂的人类流动系统进行描述性分析。我们正式将生命分析模式定义为在线不受监督的异常现象探测的自然、可解释的延伸,我们不仅监测异常数据流,而且还明确从一段时间里提取正常模式。为了了解生活模式,我们把从计算生物学和神经机器人(GWWR)的研究中得出的“增长时需要”的记忆缩影改成一个新的地理空间分析领域。这个与自我组织地图(SOM)有关的生物激励型神经网络,随着GPS流的升级而逐步建立一套“情绪”或原型交通模式。然后将每一次新的观察都与其以往的经验进行比较,从而导致在线、不受监督的集群和数据上的异常现象探测。我们从Porto出租车数据集中获取的兴趣模式,包括主要公共假日和新发现的智能空间网络网络网络网络网络网络网络网络网络,以及与自我组织地图地图(SOM)相关的正常交通、我们以前认识的正常交通、以前认识的正常交通和演化和演化行为,我们以前认识和演化、以前认识的轨道和演化和演化过程,我们认识的轨道和演化过程,我们认识到许多和演化的轨道和演化的轨道,我们认识和演化的轨道和演化过程将学习到许多的变的轨道和演化过程。