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主管单位 中华人民共和国
工业和信息化部
主办单位 哈尔滨工业大学 主编 冷劲松 国际刊号ISSN 0367-6234 国内刊号CN 23-1235/T

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引用本文:郭茂祖,王鹏跃,赵玲玲.基于深度学习的出行模式识别方法[J].哈尔滨工业大学学报,2019,51(11):1.DOI:10.11918/j.issn.0367-6234.201902039
GUO Maozu,WANG Pengyue,ZHAO Lingling.Research on recognition method of transportation modes based on deep learning[J].Journal of Harbin Institute of Technology,2019,51(11):1.DOI:10.11918/j.issn.0367-6234.201902039
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基于深度学习的出行模式识别方法
郭茂祖1,2,王鹏跃1,2,赵玲玲3
(1.北京建筑大学 电气与信息工程学院,北京 100044; 2.建筑大数据智能处理方法研究北京市重点实验室 (北京建筑大学),北京 100044; 3.哈尔滨工业大学 计算机科学与技术学院,哈尔滨 150001)
摘要:
居民出行信息可体现居民活动规律、反映城市交通问题,是制定交通规划与管理的重要依据.利用GPS获取的轨迹数据虽具有大量时空信息但不能直接表达出行模式,需要数据处理和挖掘算法提取隐藏知识来识别出行模式.由于居民出行模式具有高度的非线性和复杂性,识别具有很大挑战.本文利用深度学习方法的特征学习表征优势,解决特征提取的繁琐计算或漏提特征等弊端,通过对轨迹进行去野和划分等预处理后,计算轨迹片段的运动学特征构成输入数据,提出基于卷积神经网络与门控循环单元相结合的识别出行模式方法,利用卷积神经网络的深层特征表征优势和门控循环单元的时序特性挖掘能力,提高对非线性分类问题的学习能力和识别出行模式的准确性.为验证所提出方法的有效性,还设计单独的卷积神经网络和门控循环单元等模型,在Geolife数据集上进行测试和对比.实验结果表明,本文方法虽仅计算4个特征量仍具有较好的识别效果,并且优于单独采用卷积神经网络等分类方法的识别性能.
关键词:  出行模式识别  卷积神经网络  门控循环单元  语义挖掘  GPS轨迹数据
DOI:10.11918/j.issn.0367-6234.201902039
分类号:TP391
文献标识码:A
基金项目:国家自然科学基金(0,3);北京市教委科技计划重点项目(KZ201810016019);北京市属高校高水平创新团队建设计划项目(IDHT20190506)
Research on recognition method of transportation modes based on deep learning
GUO Maozu1,2,WANG Pengyue1,2,ZHAO Lingling3
(1.School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China; 2.Beijing Key Laboratory of Intelligent Processing for Building Big Data(Beijing University of Civil Engineering and Architecture), Beijing 100044, China; 3.School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China)
Abstract:
Resident travel information can reflect the activity routines of residents and urban traffic problems, which is an important basis for formulating transportation planning and management. Although the trajectory information acquired by GPS has a lot of spatio-temporal information, it cannot directly express transportation modes. Data processing and mining algorithms are needed to extract hidden knowledge to infer transportation modes, while recognition has great challenges due to the high degree of non-linearity and complexity of residents’ travel patterns. In this study, the advantages of deep learning were utilized to solve difficult calculation features or missing extraction features. After pre-processing of the trajectory information, kinematic features of the trajectory segments were calculated to form the input data. A method that combines convolutional neural network with gate recurrent unit was proposed to recognize transportation modes. By utilizing the advantages of convolutional neural networks, the deep features and the ability of gate recurrent unit were characterized to mine time series characteristics, improve the learning ability of nonlinear classification problems, and increase the accuracy of transportation modes recognition. In order to verify the effectiveness of the proposed method, separate convolutional neural network and gate recurrent unit were designed, which was tested and compared on the published GeoLife dataset. Experimental results show that although the proposed method only used four features, it still received well recognition results. Besides, the proposed method had better recognition performance than using a convolutional neural network and other classification methods.
Key words:  transportation modes recognition  convolutional neural network  gate recurrent unit  semantic mining  GPS trajectory data

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