Mining traffic congestion propagation patterns based on spatio-temporal co-location patterns
Mining traffic congestion propagation patterns based on spatio-temporal co-location patterns
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基于时空共置模式挖掘交通拥堵传播模式
DOI：
10.1007/s12065-019-00332-4
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发表时间：
2019-12
影响因子：
2.6
通讯作者：
Lizhen Wang
中科院分区：
文献类型：
--
作者：
Lu Yang;Lizhen Wang
交通拥堵是一定时期内供需不平衡的直接反映。由于交通道路的复杂性和拥堵的传播性，仅针对局部路段的交通拥堵疏散效果不明显。基于交通流实测数据，结合路网拓扑结构和拥堵存在时间，判断路段间拥堵的时空相关性。提出了一种时空协同拥堵模式挖掘方法，用于发现城市交通中具有拥堵传播特性的有序道路集合，并度量其在拥堵事件中的影响。该方法不仅揭示了拥塞的传播过程，而且揭示了导致大规模拥塞的主要传播路径。最后，我们在贵阳市的交通数据集上对该算法进行了实验。实验结果揭示了贵阳市交通拥堵的规律，包括拥堵传播模式的普遍共现及其在拥堵事件中的影响。
Traffic congestion is a direct reflection of the imbalance between supply and demand for a certain period of time. Owing to the complexity of traffic roads and the propagation of congestion, the evacuation of traffic congestion for local road sections alone cannot achieve significant results. Based on the measured data of traffic flow, this paper combines the topology of the road network and the existence time of congestion to judge the spatio-temporal correlation of congestion between road sections. We proposed a spatio-temporal co-location congestion pattern mining method to discover the orderly set of roads with congestion propagation in urban traffic, and measure its influence in congestion events. The proposed method not only reveals the process of congestion propagation but also uncovers the main propagation paths leading to the large-scale congestion. Finally, we experimented with the algorithm on the traffic dataset in Guiyang city. The experimental results reveal the traffic congestion rule in Guiyang City, including the prevalent co-occurrence of congestion propagation patterns and their influence in congestion events.
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DOI：
10.1109/icicisys.2009.5358192
发表时间：
2009-12
期刊：
2009 IEEE International Conference on Intelligent Computing and Intelligent Systems
影响因子：
--
作者：
Feng Qian;Liang Yin;Qinming He;Jiangfeng He
通讯作者：
Feng Qian;Liang Yin;Qinming He;Jiangfeng He
影响因子：
2.7
作者：
Mete Celik
通讯作者：
Mete Celik
DOI：
10.1109/icdmw.2012.130
发表时间：
2012-12
期刊：
2012 IEEE 12th International Conference on Data Mining Workshops
影响因子：
--
作者：
K. Pillai;R. Angryk;J. Banda;M. Schuh;Tim Wylie
通讯作者：
K. Pillai;R. Angryk;J. Banda;M. Schuh;Tim Wylie
影响因子：
1.2
作者：
Zhenyu Shan;Zhigeng Pan;Fengwei Li;Huihui Xu
通讯作者：
Zhenyu Shan;Zhigeng Pan;Fengwei Li;Huihui Xu
DOI：
10.1063/1.2155762
发表时间：
2005
期刊：
--
影响因子：
--
作者：
H. Lieu
通讯作者：
H. Lieu