Lopez Clélia, Leclercq Ludovic, Krishnakumari Panchamy, Chiabaut Nicolas, van Lint Hans
Univ. Lyon, IFSTTAR, ENTPE, LICIT, Lyon, F-69675, France.
Delft University of Technology, CITG, Delft, N-2600GA, The Netherlands.
Sci Rep. 2017 Oct 25;7(1):14029. doi: 10.1038/s41598-017-14237-8.
In this paper, we investigate the day-to-day regularity of urban congestion patterns. We first partition link speed data every 10 min into 3D clusters that propose a parsimonious sketch of the congestion pulse. We then gather days with similar patterns and use consensus clustering methods to produce a unique global pattern that fits multiple days, uncovering the day-to-day regularity. We show that the network of Amsterdam over 35 days can be synthesized into only 4 consensual 3D speed maps with 9 clusters. This paves the way for a cutting-edge systematic method for travel time predictions in cities. By matching the current observation to historical consensual 3D speed maps, we design an efficient real-time method that successfully predicts 84% trips travel times with an error margin below 25%. The new concept of consensual 3D speed maps allows us to extract the essence out of large amounts of link speed observations and as a result reveals a global and previously mostly hidden picture of traffic dynamics at the whole city scale, which may be more regular and predictable than expected.
在本文中，我们研究城市拥堵模式的每日规律。我们首先将每10分钟的链路速度数据划分为3D簇，这些簇勾勒出拥堵脉冲的简洁概况。然后，我们收集具有相似模式的日子，并使用共识聚类方法生成一个适用于多个日子的独特全局模式，从而揭示每日规律。我们表明，阿姆斯特丹35天的网络可以被合成仅4个具有9个簇的共识3D速度图。这为城市出行时间预测的前沿系统方法铺平了道路。通过将当前观测结果与历史共识3D速度图进行匹配，我们设计了一种高效的实时方法，该方法成功预测了84%行程的出行时间，误差幅度低于25%。共识3D速度图的新概念使我们能够从大量链路速度观测中提取本质，从而揭示整个城市尺度上交通动态的全局且此前大多隐藏的图景，这可能比预期的更具规律性和可预测性。
