Understanding predictability and exploration in human mobility
Understanding predictability and exploration in human mobility
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DOI：
10.1140/epjds/s13688-017-0129-1
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发表时间：
2018-01-11
期刊：
影响因子：
3.6
通讯作者：
Gonzalez, Marta C.
中科院分区：
文献类型：
--
作者：
Cuttone, Andrea;Lehmann, Sune;Gonzalez, Marta C.
人类移动预测模型在交通控制、普适计算和上下文广告等领域有着重要的应用。文献中模型的预测性能差异很大，从90%以上到40%以下不等。在这项工作中，我们研究了哪些潜在因素-就建模方法和数据源的时空特征而言-导致了文献中报道的这种显著的广泛表现。具体来说，我们研究了影响下一地点预测准确性的因素，使用了400多个用户的高精度位置数据集，观察时间在3个月到1年之间。我们表明，预测时间桶位置比预测下一个位置更容易实现高精度。此外，我们还证明了数据的时空分辨率对预测的准确性有很大的影响。最后，我们发现对新地点的探索是人类流动性的一个重要因素，我们测量到平均20-25%的迁移是向新地方的迁移，而大约20%的迁移是向新地方迁移。70%的地点只被访问一次。我们讨论了这些机制如何成为限制我们预测人类流动性能力的重要因素。
Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature varies quite broadly, from over 90% to under 40%. In this work we study which underlying factors - in terms of modeling approaches and spatio-temporal characteristics of the data sources - have resulted in this remarkably broad span of performance reported in the literature. Specifically we investigate which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users observed for periods between 3 months and one year. We show that it is much easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover, we demonstrate how the temporal and spatial resolution of the data have strong influence on the accuracy of prediction. Finally we reveal that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility.