Congestion prediction represents a major priority for traffic management centres around the world to ensure timely incident response handling. The increasing amounts of generated traffic data have been used to train machine learning predictors for traffic, however this is a challenging task due to inter-dependencies of traffic flow both in time and space. Recently, deep learning techniques have shown significant prediction improvements over traditional models, however open questions remain around their applicability, accuracy and parameter tuning. This paper proposes an advanced deep learning framework for simultaneously predicting the traffic flow on a large number of monitoring stations along a highly circulated motorway in Sydney, Australia, including exit and entry loop count stations, and over varying training and prediction time horizons. The spatial and temporal features extracted from the 36.34 million data points are used in various deep learning architectures that exploit their spatial structure (convolutional neuronal networks), their temporal dynamics (recurrent neuronal networks), or both through a hybrid spatio-temporal modelling (CNN-LSTM). We show that our deep learning models consistently outperform traditional methods, and we conduct a comparative analysis of the optimal time horizon of historical data required to predict traffic flow at different time points in the future.
翻译:对世界各地的交通管理中心而言,对交通的预测是一个重要的优先事项,以确保及时处理事故反应。越来越多的交通数据被用于培训机器学习交通预测器,然而,由于时间和空间交通流动的相互依存性,这是一项具有挑战性的任务。最近,深层次的学习技术显示,对传统模型的预测有重大改进,但对于这些模型的适用性、准确性和参数调控仍有一些尚未解决的问题。本文件建议建立一个先进的深层次学习框架,以同时预测澳大利亚悉尼一条高度流通的高速公路上大量监测站的交通流量,包括出入口环数计站,以及不同培训和预测时间范围。从3 634万个数据点中提取的空间和时间特征被用于各种深层学习结构(革命性神经网络),其时间动态(经常性神经网络),或者通过混合时空模型(CNN-LSTM)。我们表明,我们的深层学习模型始终超越了传统的方法,我们对预测未来不同时间点交通流量所需的历史数据的最佳时间范围进行了比较分析。