Bayesian Particle Tracking of Traffic Flows
Bayesian Particle Tracking of Traffic Flows
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DOI：
10.1109/tits.2017.2650947
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发表时间：
2014-11
影响因子：
8.5
通讯作者：
Nicholas G. Polson;Vadim O. Sokolov
中科院分区：
文献类型：
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作者：
Nicholas G. Polson;Vadim O. Sokolov
我们开发了一个贝叶斯粒子过滤器，用于跟踪交通流，该流量能够捕获流动动力学中存在的非线性和不连续性。我们的模型包括一个隐藏的状态变量，该变量捕获了交通自由流，崩溃和恢复之间的突然状态变化。我们开发了一种有效的粒子学习算法，用于实时在线推断状态和参数。这需要两步的方法，首先使用条件后验分布将混合物预测分布和第二个状态传播的电流颗粒重新采样。参数的粒子学习遵循更新有条件足够统计的递归。为了说明我们的方法论，我们分析了伊利诺伊州州际I-55高速公路系统的每日交通流量的测量。我们证明了如何使用高速公路单位检测器的测量值来推断高速公路路段上的交通流量变化的变化。最后，我们以未来研究的指示得出结论。
We develop a Bayesian particle filter for tracking traffic flows that is capable of capturing non-linearities and discontinuities present in flow dynamics. Our model includes a hidden state variable that captures sudden regime shifts between traffic free flow, breakdown, and recovery. We develop an efficient particle learning algorithm for real time online inference of states and parameters. This requires a two-step approach, first resampling the current particles with a mixture predictive distribution and second propagation of states using the conditional posterior distribution. Particle learning of parameters follows from updating recursions for conditional sufficient statistics. To illustrate our methodology, we analyze the measurements of daily traffic flow from the Illinois Interstate I-55 highway system. We demonstrate how our filter can be used to infer the change of traffic flow regime on a highway road segment based on a measurement from freeway single-loop detectors. Finally, we conclude with directions for future research.