Real-Time Traffic State Estimation on a Two-dimensional Network by State Space Model
Real-Time Traffic State Estimation on a Two-dimensional Network by State Space Model
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基于状态空间模型的二维网络实时交通状态估计
DOI：
10.1016/j.trc.2019.03.016
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
M.
中科院分区：
文献类型：
--
作者：
Kawasaki;Y.;Hara;Y.;Kuwahara;M.
本研究提出了一种状态空间模型，该模型可通过将探测车辆数据与交通流模型融合的数据同化技术来估计二维网络上的交通状态，并提供替代路线。尽管许多研究提出了利用探测车辆和交通探测器数据等传感数据的基于物理流动力学的交通监测方法，但它们基本上仅限于沿简单路段的交通监测。本研究将分析扩展到二维网络，其中每个OD存在多个替代路线，并考虑用户的路线选择行为。我们提出的方法采用顺序贝叶斯过滤和单元传输模型（CTM）来处理流量模型和探测车辆数据。根据探测车辆数据，不仅假设测量细胞密度，而且还假设测量发散率，并且这些测量结果被同化到流动模型中。假设网络中的模型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.