CityCoupling: bridging intercity human mobility
CityCoupling: bridging intercity human mobility
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DOI：
10.1145/2971648.2971737
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发表时间：
2016-09
期刊：
影响因子：
--
通讯作者：
Z. Fan;Xuan Song;R. Shibasaki;Tao Li;H. Kaneda
中科院分区：
文献类型：
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作者：
Z. Fan;Xuan Song;R. Shibasaki;Tao Li;H. Kaneda
城市范围内的人员流动有两大类，日常的，由日常或定期旅行组成，以及罕见的，发生在奥运会或自然灾害等事件期间。最先进的研究表明，常规的移动模式可以随机建模，而罕见的人类移动建模，必不可少的各种城市计算场景，如应急管理和交通管制，是一个更具挑战性和研究不足的问题。在本文中，我们提供了一个新的见解罕见的事件，并提出了一种新的算法，CityCoupling，它建立了一个城际空间映射，使用人类在一个城市的流动性作为输入，并在另一个城市再现人类的流动性，而不是训练一个罕见的事件特定的人类流动性模型，这遭受了罕见的事件的特殊性。更直观地说，我们试图回答这样一个问题：“如果这种罕见的事件发生在另一个城市会怎样？".为了找到最佳的城际空间映射，我们利用期望最大化算法估计的概率地理对应矩阵作为潜变量的城际轨迹匹配。此后，基于吉布斯采样的多隐马尔可夫模型生成模拟轨迹。我们将我们的方法应用于日本的一个大型移动的手机GPS数据集，并确定东京和大坂之间的空间映射，以转移在东京的日本东部大地震，这是严重影响的人的流动性，模拟可能会发生什么，如果大坂被地震袭击。我们假设新年倒计时是在东京和大坂同时发生的罕见事件，因此我们将我们的模拟与大坂的真实情况进行定量比较。
There are two broad categories of citywide human mobility, routine, composed of daily or periodic travel, and rare, which occurs during events such as the Olympic Games or natural disasters. State-of-the-art studies have shown that routine mobility patterns can be modeled stochastically, while rare human mobility modeling, essential to a variety of urban computing scenarios, such as emergency management and traffic regulation, is a much more challenging and understudied problem. Instead of training a rare-event-specific human mobility model, which suffers from the particularity of the rare events, in this paper we provide a new insight into rare events and propose a novel algorithm, CityCoupling, which establishes an intercity spatial mapping that uses human mobility in one city as input and reproduces human mobility in another city. More intuitively, we attempt to answer the question "What if this rare event happened in another city?". To find the optimal intercity spatial mapping, we utilize an expectation-maximization algorithm to estimate a probabilistic geographical correspondence matrix by regarding intercity trajectory matching as latent variables. Thereafter, a Gibbs sampling-based multiple hidden Markov model generates simulated trajectories. We apply our approach to a large mobile phone GPS dataset in Japan and determine the spatial mapping between Tokyo and Osaka to transfer the human mobility at the Great Eastern Japan Earthquake in Tokyo, which was heavily affected, to simulate what might have occurred if Osaka had been struck by the earthquake. We conduct the evaluation by assuming that New Year's Countdown is a rare event that occurs simultaneously in both Tokyo and Osaka, and thus we quantitatively compare our simulation with the ground truth in Osaka.