Modeling Freeway Traffic with Coupled HMMs
Modeling Freeway Traffic with Coupled HMMs
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发表时间：
2000
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通讯作者：
Jaimyoung Kwon
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作者：
Jaimyoung Kwon
我们考虑了从高速公路收集的环路检测器数据的建模问题。这些数据是一个向量时间序列，包含高速公路沿线多个站点在30秒采样窗口内的平均车辆速度。我们假设每个位置的测量速度是由一个隐藏的离散变量生成的，该变量表示交通在空间和时间上的t个时间点的基本状态(例如，拥堵或畅通)。我们进一步假设隐藏变量y依赖于它们的时空邻居。这种模型被称为耦合隐马尔可夫模型(CHMM)。我们可以用EM来拟合这个模型的参数。然而，由于精确推理是困难的，我们考虑了两种不同的近似方案：一种基于序贯蒙特卡罗技术(粒子滤波)，另一种基于Bo Yen-Koller(BK)算法。我们表明，与精确推理相比，这两种算法都表现得很好，并且所得到的学习模型涵盖了数据的许多重要特征。这样的宏观模型可以被证明对故障诊断和预测未来的交通模式是有用的，特别是在响应因果干预时。
We consider the problem of modeling loop detector data colle ted from freeways. The data, which is a vector time-series, contains the peed of vehicles, averaged over a 30 second sampling window, at a num ber of sites along the freeway. We assume the measured speed at each loc tion is generated from a hidden discrete variable, which represe nts the underlying state (e.g., congested or free-flowing) of the traffic a t th t point in space and time. We further assume that the hidden variables o n y depend on their spatial-temporal neighbors. Such a model is called a coupled hidden Markov model (CHMM). We can fit the parameters of this m odel using EM. However, since exact inference is intractable, we consider two different approximation schemes: one based on a sequential Monte Carlo technique (particle filtering), and the other based on the Bo yen-Koller (BK) algorithm. We show that both algorithms perform well, c ompared to exact inference, and that the resulting learned model cap tures many important features of the data. Such a macroscopic model cou ld prove useful for fault diagnosis, and in predicting future traffic patterns, particularly in response to causal interventions.