[1]王昊,刘高军,段建勇,等.基于特征自学习的交通模式识别研究[J].哈尔滨工程大学学报,2019,40(02):354-358.[doi:10.11990/jheu.201708043]
 WANG Hao,LIU GaoJun,DUAN Jianyong,et al.Transportation mode detection based on self-learning of features[J].hebgcdxxb,2019,40(02):354-358.[doi:10.11990/jheu.201708043]
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基于特征自学习的交通模式识别研究(/HTML)
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《哈尔滨工程大学学报》[ISSN:1006-6977/CN:61-1281/TN]

卷:
40
期数:
2019年02期
页码:
354-358
栏目:
出版日期:
2019-02-05

文章信息/Info

Title:
Transportation mode detection based on self-learning of features
作者:
王昊1 刘高军1 段建勇1 薛媛媛2 冯卓楠2
1. 北方工业大学 计算机学院, 北京 100144;
2. 清华大学 计算机科学与技术系, 北京 100084
Author(s):
WANG Hao1 LIU GaoJun1 DUAN Jianyong1 XUE Yuanyuan2 FENG Zhuonan2
1. Computer College, North China University of Technology, Beijing 100144, China;
2. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
关键词:
交通模式识别深度特征轨迹挖掘特征学习卷积网络轨迹
分类号:
TP391
DOI:
10.11990/jheu.201708043
文献标志码:
A
摘要:
针对目前交通模式识别以人工设计特征为主,特征设计主观性强、区分度不高的问题,本文依据深度学习理论,建立了基于卷积神经网络的特征自动学习模型。该模型利用卷积神经网络自动学习深度特征,然后与人工特征共同用于交通模式识别。模型基于微软GeoLife数据,针对不同特征组合与分类方法设计实验,实验结果表明模型能学习到高区分度深度特征、有效提高交通模式识别准确率。

参考文献/References:

[1] 陈娜. 基于位置信息的北京市出行模式研究[D]. 北京:北京交通大学, 2016.CHEN Na. Position-based resident travel pattern of BeiJing[D]. Beijing:Beijing Jiaotong University, 2016.
[2] 隋雪芹. 基于社会媒体的用户移动轨迹挖掘及其在朋友推荐中的应用研究[D]. 济南:山东大学, 2016.SUI Xueqin. Research on movement trajectory detection and application in friend recommendation based on social media[D]. Ji’nan:Shandong University, 2016.
[3] PATTERSON D J, LIAO lin, FOX D, et al. Inferring high-level behavior from low-level sensors[C]//Proceedings of the 5th International Conference on Ubiquitous Computing. Seattle, WA, USA, 2003:73-89.
[4] ZHENG Yu, LI Quannan, CHEN Yukun, et al. Understanding mobility based on GPS data[C]//Proceedings of the 10th International Conference on Ubiquitous Computing. Seoul, Korea, 2008:312-321.
[5] REDDY S, MUN M, BURKE J, et al. Using mobile phones to determine transportation modes[J]. ACM transactions on sensor networks (TOSN), 2010, 6(2):13.
[6] ZHENG Yu, LIU Like, WANG Longhao, et al. Learning transportation mode from raw gps data for geographic applications on the web[C]//Proceedings of the 17th International Conference on World Wide Web. Beijing, 2008:247-256.
[7] ZHENG Yu. Trajectory data mining:an overview[J]. ACM transactions on intelligent systems and technology, 2015, 6(3):29.
[8] LIAO Lin, PATTERSON D J, FOX D, et al. Learning and inferring transportation routines[J]. Artificial intelligence, 2007, 171(5):311-331.
[9] SHAH R C, WAN C Y, LU Hong, et al. Classifying the mode of transportation on mobile phones using GIS information[C]//Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Seattle, Washington, 2014:225-229.
[10] ZHENG Yu, CHEN Yukun, LI Quannan, et al. Understanding transportation modes based on GPS data for web applications[J]. ACM transactions on the web (TWEB), 2010, 4(1):1.
[11] VINCENT P, LAROCHELLE H, BENGIO Y, et al. Extracting and composing robust features with denoising autoencoders[C]//Proceedings of the 25th International Conference on Machine Learning. Helsinki, Finland, 2008:1096-1103.
[12] VINCENT P, LAROCHELLE H, LAJOIE I, et al. Stacked denoising autoencoders:Learning useful representations in a deep network with a local denoising criterion[J]. The journal of machine learning research, 2010, 11:3371-3408.
[13] HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786):504-507.
[14] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada, 2012:1097-1105.
[15] 毕晓君, 冯雪赟. 基于改进深度学习模型C-GRBM的人体行为识别[J]. 哈尔滨工程大学学报, 2018, 39(1):156-162.BI Xiaojun, FENG Xueyun. Human action recognition based on improved depth learning model C-GRBM[J]. Journal of Harbin Engineering University, 2018, 39(1):156-162.
[16] ZHENG Yu, ZHANG Lizhu, XIE Xing, et al. Mining interesting locations and travel sequences from GPS trajectories[C]//Proceedings of the 18th International Conference on World Wide Web. Madrid, Spain, 2009:791-800.
[17] ZHENG Yu, XIE Xing, MA Weiying. GeoLife:A collaborative social networking service among user, location and trajectory[J]. IEEE data(base) engineering bulletin, 2010, 32(2):32-39.

备注/Memo

备注/Memo:
收稿日期:2017-08-13。
基金项目:国家自然科学基金项目(61672040);北方工业大学科研启动基金项目(110051360002).
作者简介:王昊,男,讲师,博士.
通讯作者:王昊,E-mail:wh08@tsinghua.org.cn
更新日期/Last Update: 2019-01-30