Predicting human mobility through the assimilation of social media traces into mobility models
Predicting human mobility through the assimilation of social media traces into mobility models
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DOI：
10.1140/epjds/s13688-016-0092-2
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发表时间：
2016-10-21
期刊：
影响因子：
3.6
通讯作者：
Cattuto, Ciro
中科院分区：
文献类型：
--
作者：
Beiro, Mariano G.;Panisson, Andre;Cattuto, Ciro
预测不同空间尺度的人员流动受到个体轨迹的异质性和交通网络的多尺度性质的挑战。随着人类行为的大量数字痕迹变得可用，通过将各种数字平台和位置感知服务收集的移动代理数据集成到移动模型中，出现了改进移动模型的机会。在这里，我们提出了一种人类流动性的混合模型，该模型在堆叠回归过程下将来自流行照片共享系统的大规模公开数据集与经典重力模型相集成。我们使用美国航空旅行和日常通勤的两个地面实况数据集来验证我们方法的性能和普遍性：使用两种不同的交叉验证方案，我们表明混合模型可以在两个空间尺度上提供增强的流动性预测。
Predicting human mobility flows at different spatial scales is challenged by the heterogeneity of individual trajectories and the multi-scale nature of transportation networks. As vast amounts of digital traces of human behaviour become available, an opportunity arises to improve mobility models by integrating into them proxy data on mobility collected by a variety of digital platforms and location-aware services. Here we propose a hybrid model of human mobility that integrates a large-scale publicly available dataset from a popular photo-sharing system with the classical gravity model, under a stacked regression procedure. We validate the performance and generalizability of our approach using two ground-truth datasets on air travel and daily commuting in the United States: using two different cross-validation schemes we show that the hybrid model affords enhanced mobility prediction at both spatial scales.