基于深度学习的交通拥堵预测模型研究  

Research on prediction model for traffic congestion based on deep learning

作　　者：谭娟[1] 王胜春[2] 

机构地区：[1]北京工商大学商学院,北京100048 [2]北京交通大学交通数据分析与挖掘北京市重点实验室,北京100044

出　　处：《计算机应用研究》2015年第10期2951-2954,共4页Application Research of Computers

基　　金：北京市自然科学青年基金项目(9144022);国家社科基金重点项目(13AGJ008);国家教育部人文社科基金青年项目(12YJC630183)

摘　　要：针对城市道路交通拥堵预警问题,提出了一种基于深度学习的预测模型。通过归纳合并交通流参数、环境状态、时段等基础数据来构建交通流特征向量并确定四种预测状态。采用深度学习的自编码网络方法从无标签数据集中学习获取可表征数据深层特征的隐层参数并生成新特征集。应用Softmax回归对有标签的新特征集进行学习生成预测分类器,模型可对交通拥堵状况进行多态预测。通过仿真对比分析,预测模型具有较省略特征学习的预测算法更好的预测性能,平均预测精度可达85%。In order to predict the traffic congestion in city,this paper proposed the prediction model based on the deep learning. The traffic data like flow parameters,environmental situation,times of day and so on were summarized and composed into the feature vector,and defined four kinds of traffic flow pattern. It learned the unlabelled traffic dataset by the auto-encoder network to build the hidden layer function and generate the new feature dataset,and used the softmax regression to classify the traffic pattern based on the labeled feature dataset learning. Demonstrated by the simulation,the prediction model has better performance than the model without the feature learning,the average prediction accuracy can reach 85%.

关 键 词：交通拥堵 预测模型 深度学习 自编码网络 Softmax回归 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]