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。
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 autoidentifying 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. 2 tabs, ,5 figs, 11 refs.
出处
《交通运输工程学报》
EI
CSCD
北大核心
2006年第3期87-91,共5页
Journal of Traffic and Transportation Engineering
基金
国家自然科学基金重点项目(50338030)
国家自然科学基金项目(50178072)
关键词
交通信息工程
交通拥挤识别
数据挖掘
人工神经网络
交通状态
traffic information engineering； traffic congestion identification
data mining
artificial neural network
traffic state
