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Representing traffic congestions on moving objects trajectories
Keywords:
road network, traffic flow, Moving objects, trajectories, traffic congestionAbstract
The discovery of moving objects trajectory patterns representing a high traffic density have been covered on different works using diverse approaches. These models are useful for the areas of transportation planning, traffic monitoring and advertising on public roads. Besides of the important utility, these type of patterns usually do not specify a difference between a high traffic and a traffic congestion. In this work, we propose a model for the discovery of high traffic flow patterns and traffic congestions, represented in the same pattern. Also, as a complement, we present a model that discovers alternative paths to the severe traffic on these patterns. These proposed patterns could help to improve traffic allowing the identification of problems and possible alternatives.
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References
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[14] Pu Wang, Timothy Hunter, Alexandre M. Bayen, Katja Schechtner, and Marta C. Gonzalez. “Understanding road usage patterns in urban areas”. Scientific Reports, 2, 2012.
[15] Jie Yang, Xiaoping Li, Dandan Wang, and Jia Wang. “A group mining method for big data on distributed vehicle trajectories in wan”. International Journal of Distributed Sensor Networks, 2014.
[16] Yu Zheng. “Trajectory data mining: An overview”. ACM Transaction on Intelligent Systems and Technology, 2015.
[17] Yu Zheng, Yanchi Liu, Jing Yuan, and Xing Xie. “Urban computing with taxicabs”. In: Ubicomp 2011. ACM, 2011
[2] Farnoush Banaei-Kashani, Cyrus Shahabi, and Bei Pan. “Discovering patterns in traffic sensor data”. In: Proceedings of the 2Nd ACM SIGSPATIALInternational Workshop on GeoStreaming, IWGS ’11, pp. 10–16, New York, USA, 2011. ACM.
[3] Kyoung Soo Bok, He Li, Jong Tae Lim, and Jae Soo Yoo. “Discovering congested routes using vehicle trajectories in road networks”. Advances in Multimedia, 2014.
[4] Zaiben Chen, Heng Tao Shen, and Xiaofang Zhou. “Discovering popular routes from trajectories”. In: Data Engineering (ICDE), 2011 IEEE 27th International Conference on, pp 900–911, 2011.
[5] Panos Kalnis, Nikos Mamoulis, and Spiridon Bakiras. “On discovering moving clusters in spatiotemporal data”. In: Claudia Bauzer Medeiros, Max J. Egenhofer, and Elisa Bertino, editors, Advances in Spatial and Temporal Databases, volume 3633 of Lecture Notes in Computer Science, pp. 364–381. Springer Berlin Heidelberg, 2005.
[6] Ahmed Kharrat, Karine Zeitouni, Iulian Sandu-Popa, and Sami Faiz. “Characterizing traffic density and its evolution through moving object trajectories”. Fifth International IEEE Conference on Signal-Image Technologies and Internet-Based Systems, pp. 257–263, 2009.
[7] Xiaolei Li, Jiawei Han, Jae-Gil Lee, and Hector Gonzalez. “Traffic density-based discovery of hot routes in road networks”. In: SSTD’07 - Proceedings of the 10th international conference on Advances in spatial and temporal databases, pp. 441–459, Berlin, Heidelberg, 2007. Springer-Verlag.
[8] Wenting Liu, Zhijian Wang, and Jun Feng. “Continuous clustering of moving objects in spatial networks”. In: Ignac Lovrek, Robert J. Howlett, and Lakhmi C. Jain, editors, Knowledge-Based Intelligent Information and Engineering Systems, volume 5178 of Lecture Notes in Computer Science, pp. 543–550. Springer Berlin Heidelberg, 2008.
[9] Irene Ntoutsi, Nikos Mitsou, and Gerasimos Marketos. “Traffic mining in a road-network: How does the traffic flow?” International Journal of Business Intelligence and Data Mining, 3:82–98(17), 2008.
[10] Rebecca Ong, Fabio Pinelli, Roberto Trasarti, Mirco Nanni, Chiara Renso, Salvatore Rinzivillo, and Fosca Giannotti. “Traffic jams detection using flock mining”. In: Dimitrios Gunopulos, Thomas Hofmann, Donato Malerba, and Michalis Vazirgiannis, editors, Machine Learning and Knowledge Discovery in Databases, volume 6913 of Lecture Notes in Computer Science, pp. 650–653. Springer Berlin Heidelberg, 2011.
[11] Dimitris Sacharidis, Kostas Patroumpas, Manolis Terrovitis, Verena Kantere, Michalis Potamias, Kyriakos Mouratidis, and Timos Sellis. “On-line discovery of hot motion paths”. In: Proceedings of the 11th International Conference on Extending Database Technology: Advances in Database Technology, EDBT ’08, pp. 392–403, New York, NY, USA, 2008. ACM.
[12] Anna Izabel J. Tostes, Fátima de L. P. Duarte-Figueiredo, Renato Assunção, Juliana Salles, and Antonio A. F. Loureiro. From data to knowledge: “City-wide traffic flows analysis and prediction using Bing maps”. In: Proceedings of the 2Nd ACM SIGKDD International Workshop on Urban Computing, UrbComp ’13, pp. 12:1–12:8, New York, NY, USA, 2013. ACM.
[13] Junjie Wang, Dong Wei, Kun M. He, Hang Gong, and Pu Wang. “Encapsulating urban traffic rhythms into road networks”. Scientific Reports, 4, 2014.
[14] Pu Wang, Timothy Hunter, Alexandre M. Bayen, Katja Schechtner, and Marta C. Gonzalez. “Understanding road usage patterns in urban areas”. Scientific Reports, 2, 2012.
[15] Jie Yang, Xiaoping Li, Dandan Wang, and Jia Wang. “A group mining method for big data on distributed vehicle trajectories in wan”. International Journal of Distributed Sensor Networks, 2014.
[16] Yu Zheng. “Trajectory data mining: An overview”. ACM Transaction on Intelligent Systems and Technology, 2015.
[17] Yu Zheng, Yanchi Liu, Jing Yuan, and Xing Xie. “Urban computing with taxicabs”. In: Ubicomp 2011. ACM, 2011
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Published
2015-11-01
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[1]
“Representing traffic congestions on moving objects trajectories”, JCS&T, vol. 15, no. 02, pp. p. 81–86, Nov. 2015, Accessed: Jan. 14, 2026. [Online]. Available: https://journal.info.unlp.edu.ar/JCST/article/view/550
