| 移动感知环境下基于CSA-SSVR的交通状态预测方法 |
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| 引用本文： | 夏卓群,罗君鹏,胡珍珍.移动感知环境下基于CSA-SSVR的交通状态预测方法[J].计算机工程与科学,2018,40(8):1482-1487. |
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| 作者姓名： | 夏卓群 罗君鹏 胡珍珍 |
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| 作者单位： | (1.长沙理工大学综合交通运输大数据智能处理湖南省重点实验室，湖南 长沙 410114;
2.长沙理工大学计算机与通信工程学院，湖南 长沙 410114;
3.国防科技大学计算机学院，湖南 长沙 410073) |
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| 基金项目： | 国家自然科学基金(61572514)；湖南省自然科学基金(14JJ7043)；湖南省交通厅科技进步与创新项目( 201405) |
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| 摘 要： | 相较于传统感知网络，移动群智感知网络在部署和维护成本上有着较大优势，在智能交通系统中得到了越来越多的应用。交通状态的预测对交通管理系统具有重要意义，从移动群智感知环境下获取的车速数据出发，以支持向量回归算法(SVR)为基础，引入周期性算子，并采用布谷鸟算法(CSA)确定周期性SVR(SSVR)中的主要参数，提出了CSA-SSVR，对道路未来车速进行预测，据此判断道路的未来交通状态。实验表明，CSA-SSVR在移动群智感知环境下对于交通状态预测问题的准确性较高。
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| 关 键 词： | 移动群智感知网络 交通状态预测 SVR算法 布谷鸟算法 |
| 收稿时间： | 2017-02-23 |
| 修稿时间： | 2018-08-25 |
Traffic state prediction for mobile crowdsensing
networks based on CSA-SSVR |
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| XIA Zhuo qun,LUO Jun peng,HU Zhen zhen.Traffic state prediction for mobile crowdsensing
networks based on CSA-SSVR[J].Computer Engineering & Science,2018,40(8):1482-1487. |
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| Authors: | XIA Zhuo qun LUO Jun peng HU Zhen zhen |
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| Affiliation: | (1.Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation，
Changsha University of Science and Technology，Changsha 410114;
2.School of Computer and Communication Engineering，Changsha University of Science and Technology，Changsha 410114;
3.College of Computer，National University of Defense Technology，Changsha 410073，China)
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| Abstract: | Compared with traditional sensor networks, the mobile crowdsensing network has a great advantage in deployment and maintenance costs, thus becoming more and more popular in intelligent transportation systems. Prediction of traffic state is significant for traffic management systems. We explore the travel speed data in the mobile crowdsensing network to predict the future speed of vehicles. Combining with the seasonal algorithm and the cuckoo search algorithm (CSA), we use the support vector regression model (SVR) to determine the main parameters of the SSVR, and propose a prediction model called CSA SSVR to estimate the road traffic status in the future. Experimental results show that this model has good accuracy in traffic state prediction in the mobile crowdsensing network. |
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| Keywords: | mobile crowdsensing network traffic state forecasting SVR algorithm cuckoo search algorithm |
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