|Table of Contents|

[1] Ji Genlin, Zhou Xingxing, Zhao Zhujun, et al. A parallel algorithm for detecting traffic patternsusing stay point features and moving features [J]. Journal of Southeast University (English Edition), 2019, 35 (1): 22-29. [doi:10.3969/j.issn.1003-7985.2019.01.004]

A parallel algorithm for detecting traffic patternsusing stay point features and moving features()

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

2019 1
Research Field:
Computer Science and Engineering
Publishing date:


A parallel algorithm for detecting traffic patternsusing stay point features and moving features
Ji Genlin1 2 Zhou Xingxing1 2 Zhao Zhujun1 Zhao Bin1
1School of Computer Science and Technology, Nanjing Normal University, Nanjing 210023, China
2School of Geographic Science, Nanjing Normal University, Nanjing 210023, China
traffic patterns detection stay point trajectory classification parallel mining of trajectory
In order to detect the traffic pattern of moving objects in the city more accurately and quickly, a parallel algorithm for detecting traffic patterns using stay points and moving features is proposed. First, the features of the stay points in different traffic patterns are extracted, that is, the stay points of various traffic patterns are identified, respectively, and the clustering algorithm is used to mine the unique features of the stop points to different traffic patterns. Then, the moving features in different traffic patterns are extracted from a trajectory of a moving object, including the maximum speed, the average speed, and the stopping rate. A classifier is constructed to predict the traffic pattern of the trajectory using the stay points and moving features. Finally, a parallel algorithm based on Spark is proposed to detect traffic patterns. Experimental results show that the stay points and moving features can reflect the difference between different traffic modes to a greater extent, and the detection accuracy is higher than those of other methods. In addition, the parallel algorithm can increase the speed of identifying traffic patterns.


[1] Biljecki F, Ledoux H, van Oosterom P. Transportation mode-based segmentation and classification of movement trajectories[J].International Journal of Geographical Information Science, 2013, 27(2): 385-407.
[2] Mun M Y, Seo Y W. Everyday mobility context classification using radio beacons[C]//2013 IEEE 10th Consumer Communications and Networking Conference. Las Vegas, NV, USA, 2013: 112-117.
[3] Bolbol A, Cheng T, Tsapakis I, et al. Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification[J].Computers, Environment and Urban Systems, 2012, 36(6): 526-537.
[4] Patel D, Sheng C, Hsu W, et al. Incorporating duration information for trajectory classification[C]//2012 IEEE 28th International Conference on Data Engineering. Washington, DC, USA, 2012: 1132-1143.
[5] Patel D. Incorporating duration and region associationinformation in trajectory classification[J]. Journal of Location Based Services, 2013, 7(4): 246-271.
[6] Zheng Y, Chen Y K, Li Q N, et al. Understanding transportation modes based on GPS data for web applications[J].ACM Transactions on the Web, 2010, 4(1): 1-36.
[7] Zheng Y, Li Q, Chen Y, et al. Understanding mobility based on GPS data[C]//International Conference on Ubiquitous Computing. Seoul, Korea, 2008:312-321.
[8] Zhu J, Jiang N, Hu B. The application of multiple movement parameters in trajectory classification for moving objects[J].Journal of Geo-Information Science, 2016, 18(2): 143-150.(in Chinese)
[9] 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. DOI:10.1109/tits.2015.2405759.
[10] Su X, Caceres H, Tong H H, et al. Online travel mode identification using smartphones with battery saving considerations[J].IEEE Transactions on Intelligent Transportation Systems, 2016, 17(10): 2921-2934.
[11] Lee J G, Han J, Li X, et al.TraClass: Trajectory classification using hierarchical region-based and trajectory-based clustering [J]. Proceedings of the VLDB Endowment, 2008, 1(1):1081-1094.
[12] MacDonald A, Ellen J. Multi-level resolution features for classification of transportation trajectories[C]//2015 IEEE 14th International Conference on Machine Learning and Applications. Miami, FL, USA, 2015: 713-718. DOI:10.1109/ICMLA.2015.66.
[13] Endo Y, Toda H, Nishida K, et al. Deep feature extraction from trajectories for transportation mode estimation[C]// Pacific-Asia Conference on Knowledge Discovery and Data Mining. Auckland, New Zealand, 2016: 54-66.
[14] Visvalingam M, Whyatt J D. The Douglas-Peucker algorithm for line simplification: Re-evaluation through visualization[J]. Computer Graphics Forum, 1990, 9(3): 213-225.
[15] Vrotsou K, Janetzko H, Navarra C, et al. SimpliFly: A methodology for simplification and thematic enhancement of trajectories[J].IEEE Transactions on Visualization and Computer Graphics, 2015, 21(1): 107-121.
[16] Geraty M.Spark_Dbscan[EB/OL].(2014)[2018-06-20].https://github.com/alitouka/spark_dbscan/wiki.
[17] Boukhechba M, Bouzouane A, Bouchard B, et al. Online recognition of people’s activities from raw GPS data: Semantic trajectory data analysis[C]//Proceedings of the 8th ACM International Conference on Pervasive Technologies Related to Assistive Environments. Corfu, Greece, 2015:1-8.


Biography: Ji Genlin(1964—), male, doctor, professor, glji@njnu.edu.cn.
Foundation item: The National Natural Science Foundation of China(No. 41471371).
Citation: Ji Genlin, Zhou Xingxing, Zhao Zhujun, et al. A parallel algorithm for detecting traffic patterns using stay point features and moving features[J].Journal of Southeast University(English Edition), 2019, 35(1):22-29.DOI:10.3969/j.issn.1003-7985.2019.01.004.
Last Update: 2019-03-20