Consensus Filter-Based Distributed Variational Bayesian Algorithm for Flow and Speed Density Prediction With Distributed Traffic Sensors
Consensus Filter-Based Distributed Variational Bayesian Algorithm for Flow and Speed Density Prediction With Distributed Traffic Sensors
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DOI：
10.1109/jsyst.2015.2399931
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发表时间：
2017-12
影响因子：
4.4
通讯作者：
B. Safarinejadian;Mahboobeh Estakhri Estahbanati
中科院分区：
文献类型：
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作者：
B. Safarinejadian;Mahboobeh Estakhri Estahbanati
本文制定了一种基于一致性过滤器的分布式变分贝叶斯（CFBDVB）算法，用于高速公路交通流和平均交通速度的密度近似。该算法使用一些感应环路传感器收集的流量测量值，包括流量、占用率和平均速度。这些交通传感器安装在高速公路网络的密封空间中，从而展示了分布式传感器网络。假设传感器的测量结果可以通过常见的高斯混合模型进行统计建模。使用建议的算法，解决了一个具有挑战性的问题：选择正确数量的组件。该算法从大量初始化组件开始。在该算法中，每个传感器客户端通过使用局部观测值分别计算局部充分统计数据。然后使用共识过滤器将本地足够的统计数据分散到邻居并近似每个客户端中的全局足够的统计数据。然后，使用全局充分统计量，确定并省略不相关的成分。然后，估计其余组件的参数。建议的 CFBDVB 算法具有可扩展性和鲁棒性。它估计混合物的参数并同时选择组分的数量。对传感器网络进行了各种模拟，以证实 CFBDVB 算法的良好性能。
This paper formulates a consensus filter-based distributed variational Bayesian (CFBDVB) algorithm for density approximation of traffic flow and average traffic speed in a freeway. This algorithm uses traffic measurements, including volume, occupancy rate, and average velocity, collected by some inductive loop sensors. These traffic sensors are settled in sealed spaces in the freeway network, such that they demonstrate a distributed sensor network. It is assumed that measurements of the sensors can be statistically modeled by a common Gaussian mixture model. Using the suggested algorithm, a challenging problem is solved: selection of the right number of components. The algorithm begins with a large number of initialized components. In this algorithm, each sensor client severally computes local sufficient statistics by using local observations. A consensus filter is then used to spread out local sufficient statistics to neighbors and approximate global sufficient statistics in each client. Then, using the global sufficient statistics, irrelevant components are determined and omitted. Then, the remaining components' parameters are estimated. The suggested CFBDVB algorithm is scalable and robust. It estimates the parameters of mixtures and simultaneously selects the number of components. Various simulations of sensor nets have been done to confirm the promising performance of the CFBDVB algorithm.