论文标题
使用深度学习的交通拥堵异常检测和预测
Traffic congestion anomaly detection and prediction using deep learning
论文作者
论文摘要
拥塞预测是世界各地交通管理中心的主要优先事项，以确保及时的事件响应处理。越来越多的生成的流量数据已用于训练流量的机器学习预测指标，但是，由于时空和空间的交通流相互依存，这是一项艰巨的任务。最近，深度学习技术显示了对传统模型的显着预测改进，但是，围绕其适用性，准确性和参数调整仍然存在开放性问题。 This paper brings two contributions in terms of: 1) applying an outlier detection an anomaly adjustment method based on incoming and historical data streams, and 2) proposing an advanced deep learning framework for simultaneously predicting the traffic flow, speed and occupancy 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.从3634万个数据点中提取的空间和时间特征用于利用其空间结构（卷积神经元网络），其时间动态（反复的神经元网络）的各种深度学习体系结构，或通过混合时空建模（CNN-LSTM）。我们表明，我们的深度学习模型始终胜过传统方法，并对未来不同时间点预测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。最后，我们证明，异常调整方法在时空和空间中都使用深度学习带来了重大改进。
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 brings two contributions in terms of: 1) applying an outlier detection an anomaly adjustment method based on incoming and historical data streams, and 2) proposing an advanced deep learning framework for simultaneously predicting the traffic flow, speed and occupancy 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. Lastly, we prove that the anomaly adjustment method brings significant improvements to using deep learning in both time and space.
