Belief Propagation and Bethe approximation for Traffic Prediction
Belief Propagation and Bethe approximation for Traffic Prediction
复制标题
交通预测的置信传播和 Bethe 近似
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
A. D. L. Fortelle
中科院分区：
文献类型：
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作者：
C. Furtlehner;Jean;A. D. L. Fortelle
定义并研究了一种基于“信任传播”(BP)和Bethe近似的推理算法。其思想是将变量的相关性或边际概率组成的先验信息编码到图中，并使用消息传递过程从一些额外的实时信息估计实际状态。这种方法最初是为交通预测而设计的，特别适用于唯一可用的信息是浮动车数据的设置。本文基于统计物理的伊辛模型，提出了一种离散化的流量描述方法，实现了流量的实时重构和预测。BP的一般属性在此上下文中进行了说明。特别是对先验数据和图拓扑结构的稳定性进行了详细的研究。通过对一个简单的交通玩具模型的数值研究，说明了该算法的性能。讨论了如何将该方法推广到多种流量模式的叠加编码。
We define and study an inference algorithm based on "belief propagation" (BP) and the Bethe approximation. The idea is to encode into a graph an a priori information composed of correlations or marginal probabilities of variables, and to use a message passing procedure to estimate the actual state from some extra real-time information. This method is originally designed for traffic prediction and is particularly suitable in settings where the only information available is floating car data. We propose a discretized traffic description, based on the Ising model of statistical physics, in order to both reconstruct and predict the traffic in real time. General properties of BP are addressed in this context. In particular, a detailed study of stability is proposed with respect to the a priori data and the graph topology. The behavior of the algorithm is illustrated by numerical studies on a simple traffic toy model. How this approach can be generalized to encode superposition of many traffic patterns is discussed.