In this work, we propose an algorithm performing short-term predictions of the flux of vehicles on a stretch of road, using past measurements of the flux. This algorithm is based on a physics-aware recurrent neural network. A discretization of a macroscopic traffic flow model (using the so-called Traffic Reaction Model) is embedded in the architecture of the network and yields flux predictions based on estimated and predicted space-time dependent traffic parameters. These parameters are themselves obtained using a succession of LSTM ans simple recurrent neural networks. Besides, on top of the predictions, the algorithm yields a smoothing of its inputs which is also physically-constrained by the macroscopic traffic flow model. The algorithm is tested on raw flux measurements obtained from loop detectors.
翻译:在这项工作中,我们提出一种算法,利用过去对通量的测量,对路段上的车辆通量进行短期预测,这种算法以物理觉悟常态神经网络为基础,将大型交通流量模型(使用所谓的交通反应模型)分解纳入网络结构,根据估计和预测的时空依赖交通参数得出通量预测。这些参数本身是使用一系列LSTM系统简单经常性神经网络获得的。此外,除了预测之外,算法还产生其输入的顺畅,这种流流流模型也受到宏观交通流量模型的物理制约。算法是根据环探测器获得的原始通量测量进行测试的。