论文标题
具有复发性神经网络的道路网络的交通流量预测
Traffic Flow Forecast of Road Networks with Recurrent Neural Networks
论文作者
论文摘要
近年来，人们对发展智能城市的兴趣急剧增加。在这种情况下，智能运输系统描述了一个主要主题。对于有效的智能运输系统，交通流量的预测是必不可少的。由于其随机性和非线性性质，交通流量预测是一项艰巨的任务。除经典的统计方法外，神经网络是预测未来交通流的有希望的可能性。在我们的工作中，该预测是通过各种复发性神经网络进行的。这些经过培训，该培训是对诱导循环的测量，这些循环被放置在城市的交叉点中。从1月初到2018年7月底，我们使用了数据。每个模型都包含来自所有传感器的测量流量的序列，并同时预测每个传感器的未来流量。研究了多种模型架构，预测范围和输入数据。大多数情况下，具有封闭式复发单元的矢量输出模型在所有考虑的预测方案中都达到了测试集上的最小误差。由于数据量少，因此训练的模型的概括是有限的。
The interest in developing smart cities has increased dramatically in recent years. In this context an intelligent transportation system depicts a major topic. The forecast of traffic flow is indispensable for an efficient intelligent transportation system. The traffic flow forecast is a difficult task, due to its stochastic and non linear nature. Besides classical statistical methods, neural networks are a promising possibility to predict future traffic flow. In our work, this prediction is performed with various recurrent neural networks. These are trained on measurements of induction loops, which are placed in intersections of the city. We utilized data from beginning of January to the end of July in 2018. Each model incorporates sequences of the measured traffic flow from all sensors and predicts the future traffic flow for each sensor simultaneously. A variety of model architectures, forecast horizons and input data were investigated. Most often the vector output model with gated recurrent units achieved the smallest error on the test set over all considered prediction scenarios. Due to the small amount of data, generalization of the trained models is limited.
