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Traffic status prediction and analysis based on mining frequent subgraph patterns

机译：基于采矿频繁子图模式的交通状态预测与分析

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摘要

With the development of the city, the traffic congestion and traffic accidents on the urban road increase frequently. Using traffic modeling and analysis to improve the traffic conditions become more important. Now, using the traffic flow model to study the traffic problems has made many achievements. However, traffic flow model cannot be a good choice for describing the relations of the traffic element at a specific moment, but these relations are indeed significant for forecasting traffic status from that moment on. In this paper, a graph model for the static traffic was studied, and then analyzed the feature of a graph substructure for traffic congestion at one moment. We propose an effective frequent subgraph mining algorithm to find the frequent substructure that represent traffic congestion status in a graph. Our mining algorithm can enhance the efficiency of finding the congestion subgraph. Analyzing the proportion of the congestion subgraph in a graph for traffic to forecast the traffic status at that moment later, thus to find ways to improve traffic conditions.
机译：随着城市的发展，交通拥堵和城市道路的交通事故频繁增加。使用流量建模和分析来提高交通状况变得更加重要。现在，使用流量模型来研究交通问题已经取得了许多成就。然而，交通流量模型不能是描述在特定时刻交通元素关系的不错选择，但这些关系对于预测从该时刻的交通状况进行了重要意义。在本文中，研究了静态流量的图形模型，然后分析了一个时刻的交通拥堵的图形子结构的特征。我们提出了一种有效频繁的子图挖掘算法，找到代表图中交通拥塞状态的频繁子结构。我们的挖掘算法可以提高找到拥塞子图的效率。分析图表中的拥塞子图的比例，以便在稍后预测该时刻的交通状态，从而找到改善交通状况的方法。

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