| 城市道路状况概率神经网络判别方法 |
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| 引用本文： | 郭庚麒,曹成涛,徐建闽.城市道路状况概率神经网络判别方法[J].计算机工程与应用,2009,45(13):214-216. |
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| 作者姓名： | 郭庚麒 曹成涛 徐建闽 |
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| 作者单位： | 1. 华南理工大学,广州,510640;广东交通职业技术学院,广州,510650
2. 华南理工大学,广州,510640 |
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| 基金项目： | 国家高技术研究发展计划(863计划) |
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| 摘 要： | 针对移动交通流检测信息的特点,在分析概率神经网络与Global K-means聚类算法的基础上,提出了一种基于移动交通流检测信息的城市路况概率神经网络判别方法。通过分析路况的相关因素,同时考虑信号控制交叉口红灯对车辆行程时间延误的影响,利用Global K-means算法改进的概率神经网络对探测车采集的实时交通信息进行处理,进而得出城市的道路状况。应用结果表明该方法能够有效地判别和跟踪道路状况的变化,比不考虑交叉口红灯的影响时能够更准确地反映城市道路的路况信息。
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| 关 键 词： | 城市路况 探测车 概率神经网络 智能交通系统信息平台 |
| 收稿时间： | 2008-12-13 |
| 修稿时间： | 2009-2-13
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Traffic condition recognition based on probability neural network |
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| GUO Geng-qi,CAO Cheng-tao,XU Jian-min.Traffic condition recognition based on probability neural network[J].Computer Engineering and Applications,2009,45(13):214-216. |
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| Authors: | GUO Geng-qi CAO Cheng-tao XU Jian-min |
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| Affiliation: | GUO Geng-qi1,2,CAO Cheng-tao1,XU Jian-min11.South China University of Technology,Guangzhou 510640,China 2.Guangdong Communication Polytechnic,Guangzhou 510650,China |
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| Abstract: | A traffic condition recognition method based on floating car data is proposed by analyzing Probability Neural Network (PNN) and Global K-means algorithm.The related factors of traffic condition and the collection method of floating car data are presented.Considering the influence of traffic control intersection delay to travel time,a probability neural network classifier is designed using Global K-means algorithm and applied to the recognition of traffic condition with floating car data.The experiment resul... |
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| Keywords: | traffic condition floating car probability neural network Intelligent Transport System（ITS） information platform |
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