Traffic data reconstruction based on Markov random field modeling
Traffic data reconstruction based on Markov random field modeling
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DOI：
10.1088/0266-5611/30/2/025003
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发表时间：
2013-06
期刊：
影响因子：
2.1
通讯作者：
Shun'ichi Kataoka;Muneki Yasuda;C. Furtlehner;Kazuyuki Tanaka
中科院分区：
文献类型：
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作者：
Shun'ichi Kataoka;Muneki Yasuda;C. Furtlehner;Kazuyuki Tanaka
我们考虑了交通数据重建问题。假设我们有整个城市的交通数据是不完整的，因为一些道路数据没有被观测到。问题是重建数据中未被观察到的部分。在本文中，我们提出了一种新的方法来重建从各种传感器收集的不完整的交通数据。我们的方法是基于道路交通的马尔可夫随机场模型。重建采用了平均场方法和机器学习方法。我们使用日本仙台真实路网的真实模拟交通数据对该方法的性能进行了数值验证。
We consider the traffic data reconstruction problem. Suppose we have the traffic data of an entire city that are incomplete because some road data are unobserved. The problem is to reconstruct the unobserved parts of the data. In this paper, we propose a new method to reconstruct incomplete traffic data collected from various sensors. Our approach is based on Markov random field modeling of road traffic. The reconstruction is achieved by using a mean-field method and a machine learning method. We numerically verify the performance of our method using realistic simulated traffic data for the real road network of Sendai, Japan.