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Identifying human spatio-temporal activity patterns from mobile-phone traces

机译:通过手机痕迹识别人的时空活动模式

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Understanding and modeling people's mobility is a crucial component of transportation planning and management. Research in this area was originally concentrated on modeling commuting flows as they generally account for a vast majority of trips. Nowadays however, more and more trips are done to perform other activities, such as leisure. Identifying the types of places visited during a trip can be beneficial to understand the performed activities and so characterize the daily mobility of a population. In this paper we analyze a large mobile phone location dataset to monitor human locations over the course of two week time interval. We then map human locations to geographical features of the visited places and use that to characterize the daily human mobility. A limited number of visited land use patterns is found that allows describing different types of people and their daily mobility choices. The resulting patterns are characterized with peculiar trip lengths and home locations, thus showing interesting insights into modeling human travel demand, with applications to transportation activity-based models and place recommender systems.
机译:了解和建模人员的出行是交通规划和管理的关键组成部分。该领域的研究最初集中于对通勤流程进行建模,因为通勤流程通常占出行的绝大多数。然而,如今,越来越多的旅行进行其他活动,例如休闲。确定出行期间访问的地点的类型可能有助于了解已执行的活动,从而表征人群的日常流动性。在本文中,我们分析了一个大型手机位置数据集,以在两周的时间间隔内监视人类位置。然后,我们将人类位置映射到所访问场所的地理特征,并用其来表征人类的日常活动。发现访问的土地使用模式数量有限,可以描述不同类型的人及其日常出行选择。由此产生的模式具有特殊的行程长度和家乡位置,从而显示出对人类出行需求建模的有趣见解,并将其应用于基于交通活动的模型和地点推荐系统。

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