Zaidi Syed Mohammed Arshad, Chandola Varun, Yoo Eun-Hye
Computer Science and Engineering, University at Buffalo-SUNY, Buffalo, NY 14260, USA.
Department of Geography, University at Buffalo-SUNY, Buffalo, NY 14260, USA.
IEEE Access. 2021;9:167592-167604. doi: 10.1109/access.2021.3134586. Epub 2021 Dec 10.
Predicting spatial behaviors of an individual (e.g., frequent visits to specific locations) is important to improve our understanding of the complexity of human mobility patterns, and to capture anomalous behaviors in an individual's spatial movements, which can be particularly useful in situations such as those induced by the COVID-19 pandemic. We propose a system called (DST-Predict), that can predict the future visit frequency of an individual based on one's past mobility behaviour patterns using GPS trace data collected from mobile phones. Predicting such spatial behavior is challenging, primarily because individuals' patterns of location visits for each individual consists of both systematic and random components, which vary across the spatial and temporal scales of analysis. To address these issues, we propose a novel multi-view sequence-to-sequence model that uses Convolutional Long-short term memory (ConvLSTM) where the past history of frequent visit patterns features is used to predict individuals' future visit patterns in a multi-step manner. Using the GPS survey data obtained from 1,464 participants in western New York, US, we demonstrated that the proposed system is capable of predicting individuals' frequency of visits to common places in an urban setting, with high accuracy.
预测个体的空间行为（例如，频繁访问特定地点）对于增进我们对人类移动模式复杂性的理解，以及捕捉个体空间移动中的异常行为非常重要，这在诸如由新冠疫情引发的情况下可能特别有用。我们提出了一个名为（DST - Predict）的系统，它可以根据从手机收集的GPS轨迹数据，基于个体过去的移动行为模式来预测个体未来的访问频率。预测这种空间行为具有挑战性，主要是因为每个个体的位置访问模式由系统和随机成分组成，这些成分在分析的空间和时间尺度上各不相同。为了解决这些问题，我们提出了一种新颖的多视图序列到序列模型，该模型使用卷积长短期记忆（ConvLSTM），其中频繁访问模式特征的过去历史被用于多步预测个体未来的访问模式。使用从美国纽约西部1464名参与者那里获得的GPS调查数据，我们证明了所提出的系统能够高精度地预测个体在城市环境中访问公共场所的频率。
