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摘要: 为了从海量动态交通数据中快速识别路网中存在的交通拥挤, 通过分析拥挤的特征模式和各种数据挖掘技术的特点后, 设计了一种适用于城市快速路的交通拥挤自动识别方法。该方法将占有率、速度和流量三个基础交通流参数进行组合得到新的特征变量, 运用优化的多层前馈神经网络模型对特征变量进行处理来判断是否有拥挤发生, 通过分析模型输出结果的变化趋势区分常发性拥挤和偶发性拥挤。模拟数据和实测数据对比结果表明, 该方法可以识别城市快速路上发生的交通拥挤, 具有良好的实用性。Abstract: In order to quickly identify traffic congestion from mass dynamic traffic information, traffic congestion pattern and the characteristics of various data mining technologies were analyzed, an auto-identifying method of urban expressway traffic congestion was designed.The flow, speed and occupancy of expressway were combined into several new eigenvectors, optimized multi-layer feedforward perceptron model was adopted to classify the eigenvectors during congestion and non-congestion, recurrent congestion and non-recurrent congestion could be distinguished by analyzing the variances of the model outputs, the method was tested with simulated data and actual data from an urban expressway. The result shows that the method has great practicability and can identify congestion states on urban expressway correctly.
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表 1 ANN模型优化结果
Table 1. Optimal result of ANN model
序号 系统误差 输入层节点数 隐层节点数 1 0.01646 2 1 2 0.01642 4 7 3 0.01544 4 10 4 0.01456 3 7 5* 0.01172 4 18 
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表 2 识别结果
Table 2. Indentification result
测试数据 阈值 拥挤识别率/% 拥挤误识率/% 模拟数据 T1=[0.3, 0.6] [86.55, 100.00] [0.83, 1.32] T2=[0.3, 0.5] [89.99, 100.00] [0.24, 0.97] 实际数据 T1=[0.3, 0.6] [88.99, 100.00] [1.54, 1.90] T2=[0.3, 0.5] [90.70, 100.00] [1.32, 1.53] T3=[0.3, 0.5] [94.12, 100.00] [0.91, 1.08]
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