Transportation mode recognition algorithm based on multi-scale feature extraction

doi:10.11772/j.issn.1001-9081.2020121915

Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (6): 1573-1580.DOI: 10.11772/j.issn.1001-9081.2020121915

Special Issue: 2020年全国开放式分布与并行计算学术年会（DPCS 2020）

• National Open Distributed and Parallel Computing Conference 2020 (DPCS 2020） • Previous Articles Next Articles

Transportation mode recognition algorithm based on multi-scale feature extraction

LIU Shize¹, QIN Yanjun², WANG Chenxing², GAO Cunyuan², LUO Haiyong³, ZHAO Fang², WANG Baohui¹

1. College of Software, Beihang University, Beijing 100191, China;
2. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China;
3. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China

Received:2020-11-04 Revised:2021-04-02 Online:2021-06-10 Published:2021-06-21
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61872046), the Scientific and Technological Innovation Ability Improvement Action Program of Beijing University of Posts and Telecommunications (2019XD-A06).

基于多尺度特征提取的交通模式识别算法

刘世泽¹, 秦艳君², 王晨星², 高存远², 罗海勇³, 赵方², 王宝会¹

1. 北京航空航天大学软件学院, 北京 100191;
2. 北京邮电大学计算机学院, 北京 100876;
3. 中国科学院计算技术研究所, 北京 100190

通讯作者: 罗海勇
作者简介:刘世泽(1988-),男,辽宁抚顺人,硕士,主要研究方向:大数据挖掘、智能感知;秦艳君(1991-),女,山西长治人,博士研究生,CCF会员,主要研究方向:城市计算、交通模式识别;王晨星(1997-),男,河北衡水人,硕士研究生,主要研究方向:城市计算、交通模式识别、交通流预测;高存远(2000-),男,云南昆明人,主要研究方向:城市计算、交通模式识别;罗海勇(1967-),男,湖北麻城人,副研究员,博士,CCF会员,主要研究方向:移动智能、普适计算;赵方(1968-),女,河南开封人,教授,博士,CCF会员,主要研究方向:移动互联网、大数据挖掘;王宝会(1973-),男,江苏滨海人,教授级高级工程师,硕士,主要研究方向:软件工程。
基金资助:
国家自然科学基金资助项目（61872046）；北京邮电大学提升科技创新能力行动计划项目（2019XD-A06）。

Abstract

Abstract: Aiming at the problems of high power consumption and complex scene for scene perception in universal transportation modes, a new transportation mode detection algorithm combining Residual Network (ResNet) and dilated convolution was proposed. Firstly, the 1D sensor data was converted into the 2D spectral image by using Fast Fourier Transform (FFT). Then, the Principal Component Analysis (PCA) algorithm was used to realize the downsampling of the spectral image. Finally, the ResNet was used to mine the local features of transportation modes, and the global features of transportation modes were mined with dilated convolution, so as to detect eight transportation modes. Experimental evaluation results show that, compared with 8 algorithms including decision tree, random forest and AlexNet, the transportation mode recognition algorithm combining ResNet and dilated convolution has the highest accuracy in eight traffic patterns including static, walking and running, and the proposed algorithm has good identification accuracy and robustness.

Key words: activity recognition, transportation mode recognition, Residual Network (ResNet), dilated convolution, low power consumption

摘要： 针对普适交通模式的场景感知功耗高、场景复杂的问题，提出一种融合残差网络（ResNet）和带孔卷积的交通模式识别算法。首先，使用快速傅里叶变换（FFT）将一维传感器数据转换为二维频谱图像；然后，使用主成分分析（PCA）算法对频谱图像降采样；最后，使用ResNet挖掘交通模式的局部特征，使用带孔卷积挖掘交通模式的全局特征，从而实现对八种交通模式进行识别。与决策树、随机森林、AlexNet等八种算法在实验中的对比评估结果显示，融合ResNet和带孔卷积的交通模式识别算法在静止、走路、跑步等八类交通模式上均有最高准确率。该算法具有良好识别精度和鲁棒性。

关键词: 行为识别, 交通模式识别, 残差网络, 带孔卷积, 低功耗

CLC Number:

TP182

LIU Shize, QIN Yanjun, WANG Chenxing, GAO Cunyuan, LUO Haiyong, ZHAO Fang, WANG Baohui. Transportation mode recognition algorithm based on multi-scale feature extraction[J]. Journal of Computer Applications, 2021, 41(6): 1573-1580.

刘世泽, 秦艳君, 王晨星, 高存远, 罗海勇, 赵方, 王宝会. 基于多尺度特征提取的交通模式识别算法[J]. 计算机应用, 2021, 41(6): 1573-1580.

References

[1] WANG C,LUO H,ZHAO F,et al. Combining residual and LSTM recurrent networks for transportation mode detection using multimodal sensors integrated in smartphones[J]. IEEE Transactions on Intelligent Transportation Systems,2020(Early Access):1-13.
[2] QIN Y,LUO H,ZHAO F,et al. Toward transportation mode recognition using deep convolutional and long short-term memory recurrent neural networks[J]. IEEE Access,2019,7:142353-142367.
[3] WIDHALM P, NITSCHE P, BRÄNDIE N. Transport mode detection with realistic smartphone sensor data[C]//Proceedings of the 21st International Conference on Pattern Recognition. Piscataway:IEEE,2012:573-576.
[4] ZHENG Y,LIU L,WANG L,et al. Learning transportation mode from raw GPS data for geographic application on the web[C]//Proceedings of the 2008 17th International Conference on World Wide Web. New York:ACM,2008:247-256.
[5] ENDO Y,TODA H,NISHIDA K,et al. Deep feature extraction from trajectories for transportation mode estimation[C]//Proceedings of the 2016 Pacific-Asia Conference on Knowledge Discovery and Data Mining,LNCS 9652. Cham:Springer,2016:54-66.
[6] JIANG X, DE SOUZA E N, PESARANGHADER A, et al. TrajectoryNet:an embedded GPS trajectory representation for pointbased classification using recurrent neural networks[C]//Proceedings of the 2017 27th Annual International Conference on Computer Science and Software Engineering. Armonk:IBM,2017:192-200.
[7] GRAVES A,JAITLY N. Towards end-to-end speech recognition with recurrent neural networks[C]//Proceedings of the 31st International Conference on Machine Learning Research. New York:JMLR. org,2014:1764-1772.
[8] STENNETH L,WOLFSON O,YU P S,et al. Transportation mode detection using mobile phones and GIS information[C]//Proceedings of the 2011 19th ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems. New York:ACM,2011:54-63.
[9] BEDOGNI L,DI FELICE M,BONONI L. By train or by car? Detecting the user's motion type through smartphone sensors data[C]//Proceedings of the 2012 IFIP Wireless Days. Piscataway:IEEE,2012:1-6.
[10] JAHANGIRI A, RAKHA H A. Applying machine learning techniques to transportation mode recognition using mobile phone sensor data[J]. IEEE Transactions on Intelligent Transportation Systems,2015,16(5):2406-2417.
[11] FANG S H,FEI Y X,XU Z,et al. Learning transportation modes from smartphone sensors based on deep neural network[J]. IEEE Sensors Journal,2017,17(18):6111-6118.
[12] 王璞, 刘洋, 黄智仁. 一种轻量级梯度提升机的交通模式识别[J]. 哈尔滨工业大学学报, 2019, 51(9):96-102.(WANG P, LIU Y,HUANG Z R. Transportation modes recognition using a light gradient boosting machine[J]. Journal of Harbin Institute of Technology,2019,51(9):96-102.)
[13] 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(2):354-358.(WANG H,LIU G J,DUAN J Y,et al. Transportation mode detection based on self-learning of features[J]. Journal of Harbin Engineering University,2019,40(2):354-358.)
[14] 熊苏生. 基于改进LightGBM的交通模式识别算法[J]. 计算机与现代化, 2018(10):68-73,126.(XIONG S S. Identifying transportation mode based on improved LightGBM algorithm[J]. Computer and Modernization,2018(10):68-73,126.)
[15] YANG J B,NGUYEN M N,SAN P P,et al. Deep convolutional neural networks on multichannel time series for human activity recognition[C]//Proceedings of the 2015 24th International Joint Conference on Artificial Intelligence. Palo Alto:AAAI Press, 2015:3995-4001.
[16] ZHOU P,SHI W,TIAN J,et al. Attention-based bidirectional long short-term memory networks for relation classification[C]//Proceedings of the 2016 54th Annual Meeting of the Association for Computational Linguistics. Stroudsburg:ACL, 2016:207-212.
[17] XIAO Z, WANG Y, FU K, et al. Identifying different transportation modes from trajectory data using tree-based ensemble classifiers[J]. ISPRS International Journal of GeoInformation,2017,6(2):Article No. 57.
[18] FENG T, TIMMERMANS H J P. Comparison of advanced imputation algorithms for detection of transportation mode and activity episode using GPS data[J]. Transportation Planning and Technology,2016,39(2):180-194.
[19] IGNATOV A. Real-time human activity recognition from accelerometer data using convolutional neural networks[J]. Applied Soft Computing,2018,62:915-922.
[20] LIU H, LEE I. End-to-end trajectory transportation mode classification using Bi-LSTM recurrent neural network[C]//Proceedings of the 2017 12th International Conference on Intelligent Systems and Knowledge Engineering. Piscataway:IEEE,2017:1-5.
[21] HANNUN A,CASE C,CASPER J,et al. DeepSpeech:scaling up end-to-end speech recognition[EB/OL].[2020-04-26]. https://arxiv.org/pdf/1412.5567v1.pdf.
[22] BOHTE W,MAAT K. Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys:a large-scale application in the Netherlands[J]. Transportation Research Part C:Emerging Technologies,2009,17(3):285-297.
[23] FRIEDRICH B,LÜBBE C,HEIN A. Combining LSTM and CNN for mode of transportation classification from smartphone sensors[C]//Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing adn Proceedings of the 2020 ACM International Symposium on Wearable Computers. New York:ACM,2020:305-310.

Transportation mode recognition algorithm based on multi-scale feature extraction

基于多尺度特征提取的交通模式识别算法

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