Traffic Speed Data Investigation with Hierarchical Modeling
Traffic Speed Data Investigation with Hierarchical Modeling
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DOI：
10.1007/978-3-319-26135-5_10
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发表时间：
2015-11
期刊：
影响因子：
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通讯作者：
Tomonari Masada;A. Takasu
中科院分区：
文献类型：
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作者：
Tomonari Masada;A. Takasu
本文提出了一种新的主题模型，在城市环境中的交通速度分析。我们的主题模型是特殊的，因为引入了用于编码以下两个特定于域的流量速度方面的参数。首先，交通速度由每个具有固定位置的传感器测量。因此，很可能由彼此靠近的传感器给出类似的测量。其次，交通速度显示出24小时的周期性。因此，可能会在不同日期的同一时间点进行类似的测量。我们用高斯过程先验模型对这两个方面进行建模，并使主题概率依赖于位置和时间。以这种方式，我们的模型利用交通速度数据的元数据。我们提供了一个切片采样，以实现更少的近似比变分贝叶斯推理。我们提出了一个实验结果，我们使用的交通速度数据提供的纽约市。
This paper presents a novel topic model for traffic speed analysis in the urban environment. Our topic model is special in that the parameters for encoding the following two domain-specific aspects of traffic speeds are introduced. First, traffic speeds are measured by the sensors each having a fixed location. Therefore, it is likely that similar measurements will be given by the sensors located close to each other. Second, traffic speeds show a 24-hour periodicity. Therefore, it is likely that similar measurements will be given at the same time point on different days. We model these two aspects with Gaussian process priors and make topic probabilities location- and time-dependent. In this manner, our model utilizes the metadata of the traffic speed data. We offer a slice sampling to achieve less approximation than variational Bayesian inferences. We present an experimental result where we use the traffic speed data provided by New York City.