基于移动终端的交通情境识别技术研究
发布时间：2018-08-23 15:40
【摘要】：交通情境识别又称为交通模式识别,是利用用户的上下文信息对用户所处的交通状态的一种识别和感知,是人类行为识别的子问题。交通模式的自动识别可以替代传统的居民出行调查方式,更加便捷的获取大量居民的出行方式信息数据。用于城市的交通规划,缓解城市交通压力以及提高人们的出行效率。本文探究使用深度学习的方法对手机传感器进行建模完成交通情境识别。本文首先研究了传统的基于手机传感器的交通模式识别方法,包括使用的手机传感器类型、数据流处理过程、以及传统的分类方法的性能。本文研究的交通模式分类包括:公交、地铁、出租、高铁。根据研究需求采集并且构建了相关交通模式识别的基准数据集。共进行255次采集包含15名采集人员,采集6种不同部位,总共7861分钟的数据。在基准测试数据集上,本文提出两种交通模式识别方案:一、基于多层的RNN交通模式识别方案。方案对传感器进行预处理后提取简单的统计特征作为RNN网络的输入,使用多层或单层的lstm网络提取时序特征用于交通模式识别,最终识别准确率可以达到89%;二、结合CNN和RNN的交通模式识别方案,本方案通过将传感器数据特征图像化,生成activity image利用CNN自动的提取特征并利用RNN网络学习特征图像的时序特征。最终识别准确率可以到达78%。
[Abstract]:Traffic situation recognition, also known as traffic pattern recognition, is a kind of recognition and perception of the user's traffic state using the user's context information. It is a sub-problem of human behavior recognition. The automatic identification of traffic patterns can replace the traditional residents' travel survey and obtain a large number of residents' travel mode information data more conveniently. It is used in urban traffic planning, relieving traffic pressure and improving people's travel efficiency. This paper explores the use of depth learning to model mobile phone sensors to complete traffic situation recognition. Firstly, this paper studies the traditional traffic pattern recognition methods based on mobile phone sensors, including the types of mobile phone sensors used, the process of data flow processing, and the performance of the traditional classification methods. This paper studies the classification of traffic patterns including: public transport, subway, taxi, high-speed rail. According to the needs of the research, the benchmark data set of traffic pattern recognition is constructed. A total of 7861 minutes of data were collected from 6 different parts. Based on the benchmark data set, this paper proposes two traffic pattern recognition schemes: first, RNN traffic pattern recognition scheme based on multi-layer. After preprocessing the sensor, the scheme extracts simple statistical features as input of RNN network, and uses multi-layer or single-layer lstm network to extract time series features for traffic pattern recognition. Finally, the recognition accuracy can reach 89 parts. Combined with the traffic pattern recognition scheme of CNN and RNN, this scheme uses the sensor data feature image to generate activity image automatically extract features using CNN and use RNN network to learn the temporal features of feature images. The final recognition accuracy can reach 78.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U495;TP391.4
本文编号：2199513
[Abstract]:Traffic situation recognition, also known as traffic pattern recognition, is a kind of recognition and perception of the user's traffic state using the user's context information. It is a sub-problem of human behavior recognition. The automatic identification of traffic patterns can replace the traditional residents' travel survey and obtain a large number of residents' travel mode information data more conveniently. It is used in urban traffic planning, relieving traffic pressure and improving people's travel efficiency. This paper explores the use of depth learning to model mobile phone sensors to complete traffic situation recognition. Firstly, this paper studies the traditional traffic pattern recognition methods based on mobile phone sensors, including the types of mobile phone sensors used, the process of data flow processing, and the performance of the traditional classification methods. This paper studies the classification of traffic patterns including: public transport, subway, taxi, high-speed rail. According to the needs of the research, the benchmark data set of traffic pattern recognition is constructed. A total of 7861 minutes of data were collected from 6 different parts. Based on the benchmark data set, this paper proposes two traffic pattern recognition schemes: first, RNN traffic pattern recognition scheme based on multi-layer. After preprocessing the sensor, the scheme extracts simple statistical features as input of RNN network, and uses multi-layer or single-layer lstm network to extract time series features for traffic pattern recognition. Finally, the recognition accuracy can reach 89 parts. Combined with the traffic pattern recognition scheme of CNN and RNN, this scheme uses the sensor data feature image to generate activity image automatically extract features using CNN and use RNN network to learn the temporal features of feature images. The final recognition accuracy can reach 78.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U495;TP391.4
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2 蒋慧强;李资;;模式识别技术及其在消防通信中的应用[J];科技信息(学术研究);2007年35期
，本文编号：2199513
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