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Exploring an Ensemble of Textual Machine Learning Methodologies for Traffic Event Detection and Classification Cover

Exploring an Ensemble of Textual Machine Learning Methodologies for Traffic Event Detection and Classification

Open Access
|Nov 2020

References

  1. 1. Abirami, A. and Gayathri, V. (2017) A survey on sentiment analysis methods and approach, in Advanced Computing (ICoAC), 2016 Eighth International Conference on, 2017: IEEE, pp. 72-76.10.1109/ICoAC.2017.7951748
  2. 2. Aiello, L-C., Petkos, G., Martin, C., Corney, D., Papadopoulos, S., Skraba, R., Göker, A. (2013) Sensing trending topics in Twitter, IEEE Transactions in Multimedia, 15(6), pp. 1268–1282.10.1109/TMM.2013.2265080
  3. 3. Alhumoud, S. (2019) Twitter Analysis for Intelligent Transportation. The Computer Journal 62, 1547–1556. https://doi.org/10.1093/comjnl/bxy12910.1093/comjnl/bxy129
  4. 4. Ali, F., El-Sappagh, S., Kwak, D. (2019) Fuzzy ontology and LSTM-based text mining: A transportation network monitoring system for assisting travel. Sensors, 19(2), 234.10.3390/s19020234635877130634527
  5. 5. Alotaibi, S.; Mehmood, R.; Katib, I. (2019) Sentiment Analysis of Arabic Tweets in Smart Cities: A Review of Saudi Dialect. In Proceedings of the 2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC), Rome, Italy, June 10–13, pp. 330–335.10.1109/FMEC.2019.8795331
  6. 6. Aqib, M., Mehmood, R., Alzahrani, A., Katib, I., Albeshri, A., Altowaijri, S.M. (2019) Smarter Traffic Prediction Using Big Data, In-Memory Computing, Deep Learning and GPUs. Sensors 19, 2206. https://doi.org/10.3390/s1909220610.3390/s19092206653933831086055
  7. 7. Chang, H., Lee, Y., Yoon, B., Baek, S. (2012) Dynamic near-term traffic flow prediction: system-oriented approach based on past experiences. IET Intel. Transport Systems 6, 292–305. doi:10.1049/iet-its.2011.0123.10.1049/iet-its.2011.0123
  8. 8. D’Andrea, E., Ducange, P., Lazzerini, B., ---amp--- Marcelloni, F. (2015) Real-time detection of traffic from twitter stream analysis. IEEE transactions on intelligent transportation systems, 16(4), 2269-2283.10.1109/TITS.2015.2404431
  9. 9. Dabiri, S., Heaslip, K. (2019) Developing a Twitter-based traffic event detection model using deep learning architectures. Expert systems with applications, Vol. 118, pp. 425-439.10.1016/j.eswa.2018.10.017
  10. 10. Ding, J. (2019) Investigation on the Traffic Flow Based on Wireless Sensor Network Technologies Combined with FA-BPNN Models, Journal of Internet Technology, Vol. 20, No. 2, pp. 589-597.
  11. 11. Essien, A., Petrounias, I., Sampaio, P., ---amp--- Sampaio, S. (2020) A deep-learning model for urban traffic flow prediction with traffic events mined from twitter. World Wide Web, 1-24. https://doi.org/10.1007/s11280-020-00800-310.1007/s11280-020-00800-3
  12. 12. Goswami, A., ---amp--- Kumar, A. (2019) Event Detection Using Twitter Platform. In Digital Business, pp. 429-480. Springer, Cham.10.1007/978-3-319-93940-7_18
  13. 13. Gu, Y., Qian, Z. S., ---amp--- Chen, F. (2016) From Twitter to detector: Real-time traffic incident detection using social media data. Transportation research part C: emerging technologies, 67, pp. 321-342. https://doi.org/10.1016/j.trc.2016.02.01110.1016/j.trc.2016.02.011
  14. 14. Karita, S., Chen, N., Hayashi, T., Hori, T., Inaguma, H., Jiang, Z., Someki, M., Soplin, N.E.Y., Yamamoto, R., Wang, X. and Watanabe, S. (2019) A comparative study on transformer vs RNN in speech applications. In: 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp. 449-456.10.1109/ASRU46091.2019.9003750
  15. 15. Khairnar, J., and Kinikar, M. (2013) Machine learning algorithms for opinion mining and sentiment classification, International Journal of Scientific and Research Publications, 3(6), pp. 1-6.
  16. 16. Kim, Y., (2014) Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882.10.3115/v1/D14-1181
  17. 17. Kokkinos, K., Nathanail, E., ---amp--- Papageorgiou, E. (2018) Applying Unsupervised and Supervised Machine Learning Methodologies in Social Media Textual Traffic Data. In: The 4th Conference on Sustainable Urban Mobility (pp. 665-672). Springer, Cham.10.1007/978-3-030-02305-8_80
  18. 18. Lu, Z., Xia, J., Wang, M., Nie, Q., ---amp--- Ou, J. (2020) Short-term traffic flow forecasting via multi-regime modeling and ensemble learning. Applied Sciences, 10(1), 356.10.3390/app10010356
  19. 19. Mazoyer, B., Cagé, J., Hervé, N., ---amp--- Hudelot, C. (2020) A french corpus for event detection on twitter. In Proceedings of the 12th Language Resources and Evaluation Conference pp. 6220-6227.
  20. 20. Mostafaeipour, A., Rafsanjani, A. J., Ahmadi, M., ---amp--- Dhanraj, J. A. (2020) Investigating the performance of Hadoop and Spark platforms on machine learning algorithms. Journal of SuperComputing.10.1007/s11227-020-03328-5
  21. 21. Osman, A. M. S. (2019) A novel big data analytics framework for smart cities. Future Generation Computer Systems, 91, 620-633.10.1016/j.future.2018.06.046
  22. 22. Porter, M. F. (1980) An algorithm for suffix stripping, Program: Electron. Library Inf. Syst., 14(3), pp 130–137.10.1108/eb046814
  23. 23. Shim, J. P., French, A. M., Guo, C., ---amp--- Jablonski, J. (2015) Big data and analytics: Issues, solutions, and ROI. Communications of the Association for Information Systems, 37(1), 39.10.17705/1CAIS.03739
  24. 24. Social Feed Manager, (2020) A social network data acquisition software, https://gwu-libraries.github.io/sfmui/ (last accessed, July 20th, 2020).
  25. 25. Suma, S., Mehmood, R., Albugami, N., Katib, I., ---amp--- Albeshri, A. (2017) Enabling next generation logistics and planning for smarter societies. Procedia Computer Science, 109, 1122-1127.10.1016/j.procs.2017.05.440
  26. 26. Tanuja, U., Gururaj, H. L., ---amp--- Janhavi, V. (2020) A Machine Learning Algorithm for Classification, Analyzation and Prediction of Multimedia Messages in Social Networks. In Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019) (pp. 485-499). Springer, Singapore.10.1007/978-981-15-3369-3_37
  27. 27. Tsai, C. W., Lai, C. F., Chao, H. C., ---amp--- Vasilakos, A. V. (2015) Big data analytics: a survey. Journal of Big data, 2(1), pp. 1-32.10.1186/s40537-015-0030-3
  28. 28. Wongcharoen, S., ---amp--- Senivongse, T. (2016) Twitter analysis of road traffic congestion severity estimation. In 13th International Joint Conference on Computer Science and Software Engineering (JCSSE) (pp. 1-6). IEEE.10.1109/JCSSE.2016.7748850
  29. 29. Xin, Y., ---amp--- MacEachren, A. M. (2020) Characterizing traveling fans: a workflow for event-oriented travel pattern analysis using Twitter data. International Journal of Geographical Information Science, 1-20.10.1080/13658816.2020.1770259
  30. 30. Zhou, Y., and Cao, Z.-W. (2011) Research on the construction and filter method of stop-word list in text preprocessing. In: Proc. 4th ICICTA, Shenzhen, China, vol. 1, pp. 217–221.
DOI: https://doi.org/10.2478/ttj-2020-0023 | Journal eISSN: 1407-6179 | Journal ISSN: 1407-6160
Language: English
Page range: 285 - 294
Published on: Nov 26, 2020
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2020 Konstantinos Kokkinos, Eftihia Nathanail, published by Transport and Telecommunication Institute
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.