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Representative Routes Discovery from Massive Trajectories

Published: 14 August 2022 Publication History

Abstract

In this work, we study how to find the k most representative routes over large scale trajectory data, which is a fundamental operation that benefits various real-world applications, such as traffic monitoring and public transportation planning. The operator is time-sensitive as it must be able to adapt the results as traffic conditions change. We first prove the NP-hardness of the problem, and then propose a range of effective approximate solutions that have rapid response times. Specifically, we first build a lookup table that stores the trajectories covered by each edge in a given road network. Rather than performing a depth-first search for all possible routes, we find a 1/η approximate solution by developing a maximum-weight algorithm. Since each edge in a route may be close to several trajectories, we further propose a coverage-first algorithm to locate the edges with the greatest coverage gain in the solution route set. By observing that in the real world each edge is connected to only a few other edges in a road network, we have developed a connect-first algorithm that finds consecutive edges for k representative routes by greedily selecting edges with the maximum marginal gain for each route. Finally, comprehensive experiments over two real-world datasets are conducted to verify the effectiveness and efficiency of our proposed algorithms, and provide evidence of the usefulness of our solution and rapid response times in traffic monitoring tasks.

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[1]
Pankaj K. Agarwal, Rinat Ben Avraham, Haim Kaplan, and Micha Sharir. 2013. Computing the Discrete Fré chet Distance in Subquadratic Time. In SODA. 156--167.
[2]
Pankaj K. Agarwal, Kyle Fox, Kamesh Munagala, Abhinandan Nath, Jiangwei Pan, and Erin Taylor. 2018. Subtrajectory Clustering: Models and Algorithms. In PODS. 75--87.
[3]
Milutin Brankovic, Kevin Buchin, Koen Klaren, André Nusser, Aleksandr Popov, and Sampson Wong. 2020. (k, l)-Medians Clustering of Trajectories Using Continuous Dynamic Time Warping. In SIGSPATIAL. 99--110.
[4]
Kevin Buchin, Anne Driemel, Natasja van de L'Isle, and André Nusser. 2019. klcluster: Center-based Clustering of Trajectories. In SIGSPATIAL. 496--499.
[5]
Taxi Trajectory Prediction Challenge. 2015. https://www.kaggle.com/c/pkdd-15-predict-taxi-service-trajectory-i/.
[6]
Jingying Chen, Maylor K. H. Leung, and Yongsheng Gao. 2003. Noisy logo recognition using line segment Hausdorff distance. Pattern Recognit., Vol. 36, 4 (2003), 943--955.
[7]
Lei Chen and Raymond T. Ng. 2004. On The Marriage of Lp-norms and Edit Distance. In VLDB. 792--803.
[8]
Lei Chen, M. Tamer Ö zsu, and Vincent Oria. 2005. Robust and Fast Similarity Search for Moving Object Trajectories. In SIGMOD. 491--502.
[9]
Zaiben Chen, Heng Tao Shen, and Xiaofang Zhou. 2011. Discovering popular routes from trajectories. In ICDE. 900--911.
[10]
Source Code. 2022. https://github.com/rmitbggroup/RepresentativeRoutes .
[11]
Ticiana L. Coelho da Silva, Francesco Lettich, José Antô nio Fernandes de Macê do, Karine Zeitouni, and Marco A. Casanova. 2020. Online Clustering of Trajectories in Road Networks. In MDM. 99--108.
[12]
Ticiana L. Coelho da Silva, Karine Zeitouni, and José Antô nio Fernandes de Macê do. 2016. Online Clustering of Trajectory Data Stream. In MDM. 112--121.
[13]
Anna Fariha, Larkin Flodin, and Raj Kumar Maity. 2021. Maximum Coverage with Path Constraint. https://people.cs.umass.edu/ afariha/projects/690AA_project.pdf (2021).
[14]
Asim Farooq, Mowen Xie, Svetla Stoilova, Firoz Ahmad, Meng Guo, Edward J Williams, Vimal Kr Gahlot, Du Yan, and Ahmat Mahamat Issa. 2018. Transportation planning through GIS and multicriteria analysis: case study of Beijing and XiongAn. Journal of Advanced Transportation, Vol. 2018 (2018).
[15]
Fosca Giannotti, Mirco Nanni, Fabio Pinelli, and Dino Pedreschi. 2007. Trajectory pattern mining. In KDD. 330--339.
[16]
George A. M. Gomes, Emanuele Marques dos Santos, Creto Augusto Vidal, Ticiana L. Coelho da Silva, and José Antô nio Fernandes de Macê do. 2018. Real-time discovery of hot routes on trajectory data streams using interactive visualization based on GPU. Comput. Graph., Vol. 76 (2018), 129--141.
[17]
Hector Gonzalez, Jiawei Han, Xiaolei Li, Margaret Myslinska, and John Paul Sondag. 2007. Adaptive Fastest Path Computation on a Road Network: A Traffic Mining Approach. In VLDB. 794--805.
[18]
Chih-Chieh Hung, Wen-Chih Peng, and Wang-Chien Lee. 2015. Clustering and aggregating clues of trajectories for mining trajectory patterns and routes. VLDB Journal, Vol. 24, 2 (2015), 169--192.
[19]
Jae-Gil Lee, Jiawei Han, and Kyu-Young Whang. 2007. Trajectory clustering: a partition-and-group framework. In SIGMOD. 593--604.
[20]
Mingqian Li, Panrong Tong, Mo Li, Zhongming Jin, Jianqiang Huang, and Xian-Sheng Hua. 2021. Traffic Flow Prediction with Vehicle Trajectories. In AAAI. 294--302.
[21]
Fandel Lin, Hsun-Ping Hsieh, and Jie-Yu Fang. 2020. A Route-Affecting Region Based Approach for Feature Extraction in Transportation Route Planning. In ECML PKDD. 275--290.
[22]
Cheng Long, Raymond Chi-Wing Wong, and H. V. Jagadish. 2013. Direction-Preserving Trajectory Simplification. Proc. VLDB Endow., Vol. 6, 10 (2013), 949--960.
[23]
Wuman Luo, Haoyu Tan, Lei Chen, and Lionel M. Ni. 2013. Finding time period-based most frequent path in big trajectory data. In SIGMOD. 713--724.
[24]
Paul Mees, John Stone, Muhammad Imran, and Gustav Nielson. 2010. Public transport network planning: a guide to best practice in NZ cities. Technical Report.
[25]
Sarana Nutanong, Edwin H. Jacox, and Hanan Samet. 2011. An Incremental Hausdorff Distance Calculation Algorithm. Proc. VLDB Endow., Vol. 4, 8 (2011), 506--517.
[26]
OpenStreetMap. 2021. https://www.openstreetmap.org/.
[27]
Costas Panagiotakis, Nikos Pelekis, Ioannis Kopanakis, Emmanuel Ramasso, and Yannis Theodoridis. 2012. Segmentation and Sampling of Moving Object Trajectories Based on Representativeness. IEEE Trans. Knowl. Data Eng., Vol. 24, 7 (2012), 1328--1343.
[28]
Nikos Pelekis, Panagiotis Tampakis, Marios Vodas, Costas Panagiotakis, and Yannis Theodoridis. 2017. In-DBMS Sampling-based Sub-trajectory Clustering. In EDBT. 632--643.
[29]
Dimitris Sacharidis, Kostas Patroumpas, Manolis Terrovitis, Verena Kantere, Michalis Potamias, Kyriakos Mouratidis, and Timos K. Sellis. 2008. On-line discovery of hot motion paths. In EDBT, Vol. 261. 392--403.
[30]
Michail Vlachos, Dimitrios Gunopulos, and George Kollios. 2002. Discovering Similar Multidimensional Trajectories. In ICDE. 673--684.
[31]
Sheng Wang, Zhifeng Bao, J. Shane Culpepper, and Gao Cong. 2021 a. A Survey on Trajectory Data Management, Analytics, and Learning. ACM Comput. Surv., Vol. 54, 2 (2021), 39:1--39:36.
[32]
Sheng Wang, Zhifeng Bao, J. Shane Culpepper, Timos Sellis, and Xiaolin Qin. 2019. Fast Large-Scale Trajectory Clustering. Proc. VLDB Endow., Vol. 13, 1 (2019), 29--42.
[33]
Sheng Wang, Yuan Sun, Christopher Musco, and Zhifeng Bao. 2021 b. Public Transport Planning: When Transit Network Connectivity Meets Commuting Demand. In SIGMOD. 1906--1919.
[34]
Can Yang and Gyozo Gidofalvi. 2018. Fast map matching, an algorithm integrating hidden Markov model with precomputation. International Journal of Geographical Information Science, Vol. 32, 3 (2018), 547--570.
[35]
Byoung-Kee Yi, H. V. Jagadish, and Christos Faloutsos. 1998. Efficient Retrieval of Similar Time Sequences Under Time Warping. In ICDE. 201--208.
[36]
Jing Yuan, Yu Zheng, Chengyang Zhang, Wenlei Xie, Xing Xie, Guangzhong Sun, and Yan Huang. 2010. T-drive: driving directions based on taxi trajectories. In SIGSPATIAL/GIS. 99--108.
[37]
Dongxiang Zhang, Mengting Ding, Dingyu Yang, Yi Liu, Ju Fan, and Heng Tao Shen. 2018. Trajectory Simplification: An Experimental Study and Quality Analysis. Proc. VLDB Endow., Vol. 11, 9 (2018), 934--946.
[38]
Linjiang Zheng, Qisen Feng, Weining Liu, and Xin Zhao. 2016. Discovering Trip Hot Routes Using Large Scale Taxi Trajectory Data. In ADMA. 534--546.
[39]
Yu Zheng, Lizhu Zhang, Xing Xie, and Wei-Ying Ma. 2009. Mining interesting locations and travel sequences from GPS trajectories. In WWW. 791--800.
[40]
Fenghua Zhu, Yisheng Lv, Yuanyuan Chen, Xiao Wang, Gang Xiong, and Feiyue Wang. 2020. Parallel Transportation Systems: Toward IoT-Enabled Smart Urban Traffic Control and Management. IEEE Trans. Intell. Transp. Syst., Vol. 21, 10 (2020), 4063--4071.

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  • (2024)Trajectory Similarity Measurement: An Efficiency PerspectiveProceedings of the VLDB Endowment10.14778/3665844.366585817:9(2293-2306)Online publication date: 6-Aug-2024

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    cover image ACM Conferences
    KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
    August 2022
    5033 pages
    ISBN:9781450393850
    DOI:10.1145/3534678
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    Published: 14 August 2022

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    Author Tags

    1. massive trajectories
    2. representative routes
    3. route discovery

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    • (2024)Trajectory Similarity Measurement: An Efficiency PerspectiveProceedings of the VLDB Endowment10.14778/3665844.366585817:9(2293-2306)Online publication date: 6-Aug-2024

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