A model to automatic select traffic event extraction algorithms for ITS applications
Abstract
Traffic events can be useful to a variety of Intelligent Transportation System (ITS) applications. This work presents a model that can correlate features of multiple data sources with demands from ITS applications interested in consuming traffic events in order to establish the best strategy for extracting them. Once used, the model yields to a list of events, each of them reporting what happened, besides where and when. An instance of the proposed model using two social networks as data sources and four machine learning algorithms was implemented as a case study. The results have shown that it was possible to extract a great part of the expected events, all of them with complete information.
Keywords:
Events, Intelligent Transportation Systems, Machine Learning
References
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Sakaki, T., Matsuo, Y., Yanagihara, T., Chandrasiri, N. P., and Nawa, K. (2012). Real-time event extraction for driving information from social sensors. InCyber Technologyin Automation, Control, and Intelligent Systems (CYBER), 2012 IEEE International Conference on, pages 221–226. IEEE
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Allan, J., Papka, R., and Lavrenko, V. (1998). On-line new event detection and tracking.InProceedings of the 21st annual international ACM SIGIR conference on Researchand development in information retrieval, pages 37–45. ACM.
Anantharam, P., Barnaghi, P., Thirunarayan, K., and Sheth, A. (2015). Extracting citytraffic events from social streams.ACM Transactions on Intelligent Systems and Tech-nology (TIST), 6(4):43
Brants, T., Chen, F., and Farahat, A. (2003). A system for new event detection. In Proceedings of the 26th annual international ACM SIGIR conference on Research anddevelopment in informaion retrieval, pages 330–337. ACM
Finkel, J. R. and Manning, C. D. (2009). Nested named entity recognition. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing:Volume 1-Volume 1, pages 141–150. Association for Computational Linguistics.
Galton, A. and Augusto, J. C. (2002). Two approaches to event definition. In International Conference on Database and Expert Systems Applications, pages 547–556. Springer
Kokkinogenis, Z., Filguieras, J., Carvalho, S., Sarmento, L., and Rossetti, R. (2015). Mobility network evaluation in the user perspective: Real-time sensing of traffic informa-tion in twitter messages.Advances in Artificial Transportation Systems and Simulation,pages 219–234
Kumaran, G., Allan, J., and McCallum, A. (2004). Classification models for new eventdetection. In International conference on information and knowledge management(CIKM2004).
Petalas, Y. G., Ammari, A., Georgakis, P., and Nwagboso, C. (2016). A big data architecture for traffic forecasting using multi-source information. InInternational Workshopof Algorithmic Aspects of Cloud Computing, pages 65–83. Springer.
Ratinov, L. and Roth, D. (2009). Design challenges and misconceptions in named entityrecognition. In Proceedings of the thirteenth conference on computational naturallanguage learning, pages 147–155. Association for Computational Linguistics.
Ribeiro Jr, S. S., Davis Jr, C. A., Oliveira, D. R. R., Meira Jr, W., Gonçalves, T. S., andPappa, G. L. (2012). Traffic observatory: a system to detect and locate traffic eventsand conditions using twitter. InProceedings of the 5th ACM SIGSPATIAL InternationalWorkshop on Location-Based Social Networks, pages 5–11. ACM
Sakaki, T., Matsuo, Y., Yanagihara, T., Chandrasiri, N. P., and Nawa, K. (2012). Real-time event extraction for driving information from social sensors. InCyber Technologyin Automation, Control, and Intelligent Systems (CYBER), 2012 IEEE International Conference on, pages 221–226. IEEE
Tejaswin, P., Kumar, R., and Gupta, S. (2015). Tweeting traffic: Analyzing twitter forgenerating real-time city traffic insights and predictions. InProceedings of the 2ndIKDD Conference on Data Sciences, page 9. ACM
Wang, D., Al-Rubaie, A., Davies, J., and Clarke, S. S. (2014). Real time road traffic mo-nitoring alert based on incremental learning from tweets. InEvolving and AutonomousLearning Systems (EALS), 2014 IEEE Symposium on, pages 50–57. IEEE
Zhang, Y., Jin, R., and Zhou, Z.-H. (2010). Understanding bag-of-words model: a statistical framework.International Journal of Machine Learning and Cybernetics, 1(1-4):43–52
Published
2021-07-18
How to Cite
PEREIRA, Alexandra S.; SILVA, Thais R. M. B.; SILVA, Fabrício A.; CORREIA, Luiz H. A.; LOUREIRO, Antonio A. F..
A model to automatic select traffic event extraction algorithms for ITS applications. In: PROCEEDINGS OF BRAZILIAN SYMPOSIUM ON UBIQUITOUS AND PERVASIVE COMPUTING (SBCUP), 13. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 51-60.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2021.16003.
