Traffic state estimation on a two-dimensional network by a state-space model
Traffic state estimation on a two-dimensional network by a state-space model
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DOI：
10.1016/j.trpro.2019.05.017
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
Yosuke Kawasaki;Yusuke Hara;M. Kuwahara
中科院分区：
文献类型：
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作者：
Yosuke Kawasaki;Yusuke Hara;M. Kuwahara
这项研究提出了一种状态空间模型，通过将探测车辆数据与交通流模型融合的数据同化技术，估计具有替代路线的二维网络上的交通状态。虽然一些研究提出了基于物理流动力学的交通监控方法，使用诸如探测车和交通检测器数据等传感数据，但它们基本上局限于简单路段的交通监控。这项研究将分析扩展到一个二维网络，其中每个OD存在几条可选路线，并考虑了用户的路径选择行为。我们提出的方法采用序贯贝叶斯滤波和细胞传输模型(CTM)用于流动模型和探测车数据。从探测飞行器的数据中，假设不仅要测量单元密度，而且还要测量发散比，并将这些测量结果同化到流动模型中。模型在假设网络中的验证揭示了模型的潜力，并揭示了未来的问题。
This study proposes a state-space model that estimates traffic states over a two-dimensional network with alternative routes available by a data assimilation technique that fuses probe vehicle data with a traffic flow model. Although a number of studies propose traffic monitoring methods based on physical flow dynamics using sensing data such as probe vehicle and traffic detector data, they are basically limited to traffic monitoring along a simple road section. This study extends the analysis to a two-dimensional network, in which several alternative routes exist for each OD, with consideration of the route choice behaviours of users. Our proposed method employs sequential Bayesian filtering with a cell transmission model (CTM) for the flow model and probe vehicle data. From the probe vehicle data, not only cell densities but also diverging ratios are assumed to be measured and these measurements are assimilated into the flow model. The model validation in a hypothetical network reveals the potential of the model, and discloses future issues.