The biggest challenge of analysing network traffic dynamics of large-scale networks is its complexity and pattern interpretability. In this work, we present a new computationally efficient method, inspired by human vision, to reduce the dimensions of a large-scale network and describe the traffic conditions with a compact, scalable and interpretable custom feature vector. This is done by extracting pockets of congestion that encompass connected 3D subnetworks as 3D shapes. We then parameterize these 3D shapes as 2D projections and construct parsimonious feature vectors from these projections. There are various applications of these feature vectors such as revealing the day-to-day regularity of the congestion patterns and building a classification model that allows us to predict travel time from any origin to any destination in the network. We demonstrate that our method achieves a 44% accuracy improvement when compared against the consensus method for travel prediction of an urban network of Amsterdam. Our method also outperforms historical average methods, especially for days with severe congestion. Furthermore, we demonstrate the scalability of the approach by applying the method on the entire Dutch highway network and show that the feature vector was able to encapsulate the network dynamics with a 93% prediction accuracy. There are many paths to further refine and improve the method. The compact form of the feature vector allows us to efficiently enrich it with more information such as context, weather and event without increasing the computational complexity.

分析大型网络的网络流量动态的最大挑战是其复杂性和模式可解释性。在这项工作中，我们提出了一种新的计算有效方法，该方法受到人类视觉的启发，可以减少大型网络的规模，并使用紧凑，可伸缩且可解释的自定义特征向量来描述流量条件。这是通过提取拥塞的口袋来完成的，这些口袋将连接的3D子网包含为3D形状。然后，将这些3D形状参数化为2D投影，并从这些投影构造简约特征向量。这些特征向量有多种应用，例如揭示拥塞模式的日常规律并建立分类模型，使我们能够预测从网络中任何起点到目的地的旅行时间。我们证明，与针对阿姆斯特丹城市网络的出行预测的共识方法相比，我们的方法实现了44％的精度提高。我们的方法也优于历史平均方法，尤其是在严重拥堵的日子。此外，我们通过在整个荷兰高速公路网络上应用该方法展示了该方法的可扩展性，并表明特征向量能够以93％的预测精度封装网络动态。有许多途径可以进一步完善和改进该方法。特征向量的紧凑形式使我们能够在不增加计算复杂性的情况下，通过更多信息（例如上下文，天气和事件）有效地丰富它。