EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 469 (2023) E3S Web of Conf., 469 (2023) 00102 References
Open Access
Issue
E3S Web of Conf.
Volume 469, 2023
The International Conference on Energy and Green Computing (ICEGC’2023)
Article Number 00102
Number of page(s) 9
DOI https://doi.org/10.1051/e3sconf/202346900102
Published online 20 December 2023
  1. El Hatri, C., & Boumhidi, J. (2017). Traffic management model for vehicle re-routing and traffic light control based on multi-objective particle swarm optimization. Journal of Intelligent Decision Technologies, 11(2), 199–208. https://doi.org/10.3233/IDT- 170288. [CrossRef] [Google Scholar]
  2. S. Chen, W. Wang, H. Van Zuylen, Construct support vector machine ensemble to detect traffic incident, Expert Syst. Appl. 38 (8) (2009) 10976–10986. [CrossRef] [Google Scholar]
  3. M. Chong, A. Abraham, M. Paprzycki, Traffic accident analysis using machine learning paradigms, Informatica 29 (1) (2005). [Google Scholar]
  4. F. Yuan, R.L. Cheu, Incident detection using support vector machines, Transp. Res. C 11 (2003) 309–328. [CrossRef] [Google Scholar]
  5. Z. Zhou, L. Zhou, An automatic incident of freeway detection algorithm based on support vector machine, in: Proceedings of the IEEE IPTC,2010, pp. 543–546. [Google Scholar]
  6. R.L. Cheu, D. Srinivasan, E.T. Teh, Support vector machine models for freeway incident detection, in: Proceedings of the IEEE ITS, 2003, pp.238–243. [Google Scholar]
  7. Mohamed Dardor, Mohammed Chlyah and Jaouad Boumhidi, “Incident detection in signalized urban roads based on genetic algorithm and support vector machine”, in 2018 International Conference on Intelligent Systems and Computer Vision (ISCV). DOI: 10.1109/ISACV.2018.8354029. [Google Scholar]
  8. Parkany, E., & Xie, C. (2005). A complete review of incident detection algorithms & their deployment: what works and what doesn’t (No. NETCR 37, NETC 00-7). [Google Scholar]
  9. Ruiz CW, Perapoch J, Castillo F, Salcedo S, Gratac´os E. Bessons monocori`onics afectes de transfusi´o fetofetal: conseqü`encies a curt i llarg termini. Pediatr Catalana 2006;66:53–61 [Google Scholar]
  10. Hochreiter, Sepp, and Jürgen Schmidhuber. “Long short-term memory.” Neural computation 9.8 (1997): 1735-1780. [CrossRef] [Google Scholar]
  11. Behrisch, M., Bieker, L., Erdmann, J., & Krajzewicz, D. (2011). SUMO–simulation of urban mobility: an overview. In Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation. ThinkMind. [Google Scholar]
  12. Wegener, A., Piórkowski, M., Raya, M., Hellbrück, H., Fischer, S., & Hubaux, J. P. (2008, April). TraCI: an interface for coupling road traffic and network simulators. In Proceedings of the 11th communications and networking simulation symposium (pp. 155-163). ACM. [Google Scholar]
  13. Bidisha Ghosh & Damien P. Smith (2014) Customization of Automatic Incident Detection Algorithms for Signalized Urban Arterials, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 18:4, 426-441 [CrossRef] [Google Scholar]
  14. R. Fu, Z. Zhang and L. Li, Using LSTM and GRU Network methods for Traffic flow prediction,k 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), Wuhan, pp. 324-328, (2016). [Google Scholar]
  15. J. Li, L. Gao, L. wei, Y. Shi, “Short term traffic flow prediction based on LSTM”, Ninth international conference on Intelligent control and information processing”, Wanzhou, pp.251-255, (2018) [Google Scholar]
  16. Y. Tian, K. Zhang, J. Li, X. Lin, B. Yang, LSTM-based traffic flow prediction with missing data, Neuro Comput. 318 (2018) 297–305. [Google Scholar]
  17. Mahmassain, H.S.; Haas, C.; Zhou, S.; Peterman, J. Others Evaluation of Incident Detection Methodologies. 1999. Available online: https://ctr.utexas.edu/wp-content/uploads/pubs/1795_1.pdf (accessed on 21 June 2022). [Google Scholar]
  18. R. Genuer, “Forêts aléatoires : aspect théoriques, sélection de variables et applications,”Thèse de Doctorat Mathématiques, Université de Paris-Sud XI, (2010). [Google Scholar]
  19. Witten, Ian H., and Eibe Frank. “Data mining: practical machine learning tools and techniques,” Morgan Kaufmann, pp. 1-560, Jun.( 2005) by Elsevier. [Google Scholar]
  20. Parkany, E., & Xie, C. (2005). A complete review of incident detection algorithms & their deployment: what works and what doesn't (No. NETCR 37, NETC 00-7). [Google Scholar]
  21. Hsun-Jung Cho, Ming-TeTseng, “A support vector machine approach to CMOS-based radar signal processing for vehicle classification and speed estimation,” Mathematical and Computer Modelling, vol. 58, issues 1–2, Jul. 2013, pp. 438-448. [Google Scholar]
  22. S.N. Jeyanthi, “Efficient Classification Algorithms using SVMs for Large Datasets,” A Project Report Submitted in partial fulfillment of the requirements for the Degree of Master of Technology in Computational Science, Supercomputer Education and Research Center, IISC, BANGALORE, INDIA, Jun. (2007). [Google Scholar]
  23. Ahmed, F., & Hawas, Y. E. (2012). A threshold-based real-time incident detection systemfor urban traffic networks. Procedia-Social and Behavioral Sciences, 48, 1713-1722. [CrossRef] [Google Scholar]
  24. H.U.I. Jiang, H. Deng, Traffic incident detection method based on factor analysis and weighted random forest, 8 (2020). [Google Scholar]
  25. Thomas, N. E. (1998). Multi-state and multi-sensor incident detection systems for arterial streets. Transportation Research Part C: Emerging Technologies,6(5), 337-357. [CrossRef] [Google Scholar]
  26. H. Dia and K. Thomas, "Development and evaluation of arterial incident detection models using fusion of simulated probe vehicle and loop detector data", In formation Fusion, vol. 12, pp. 20-27, (2011) [CrossRef] [Google Scholar]
  27. Breiman L. Random forests. Machine Learning. (2001); 45 (1. 2001), p. 5–32. [CrossRef] [Google Scholar]
  28. Pal M. Random forest classifier for remote sensing classification. International Journal of Remote Sensing. (2015); 26(1):217–222. [Google Scholar]
  29. J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, (1892), pp.68–73. [Google Scholar]
  30. I. S. Jacobs and C. P. Bean, “Fine particles, thin films and exchange anisotropy,” in Magnetism, vol. III, G. T. Rado and H. Suhl, Eds. New York: Academic, (1963), pp. 271–350. [Google Scholar]
  31. K. Elissa, “Title of paper if known,” unpublished. [Google Scholar]
  32. R. Nicole, “Title of paper with only first word capitalized,” J. Name Stand. Abbrev., in press. [Google Scholar]
  33. Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron spectroscopy studies on magneto-optical media and plastic substrate interface,” IEEE Transl. J. Magn. Japan, vol. 2, pp. 740–741, August 1987 [Digests 9th Annual Conf. Magnetics Japan, p. 301,(1982). [CrossRef] [Google Scholar]
  34. M. Young, The Technical Writer’s Handbook. Mill Valley, CA: University Science, (1989). [Google Scholar]
  35. Dogru, N.; Subasi, A. Traffic Accident Detection Using Random Forest Classifier. In Proceedings of the 2018 15th Learning and Technology Conference (L&T), Jeddah, Saudi Arabia, 25–26 February 2018; pp. 40–45. [Google Scholar]
  36. Bharath Kumar, M.; Basit, A.; Kiruba, M.B.; Giridharan, R.; Keerthana, S.M. Road Accident Detection Using Machine Learning. In Proceedings of the 2021 International Conference on System, Computation, Automation and Networking (ICSCAN), Puducherry, India, 30–31 July 2021; pp. 2–6. [Google Scholar]
  37. Xie, T.; Shang, Q.; Yu, Y. Automated Traffic Incident Detection: Coping With Imbalanced and Small Datasets. IEEE Access 2022, 10, 35521–35540 [CrossRef] [Google Scholar]
  38. Rossi, R., Gastaldi, M., Gecchele, G., & Barbaro, V. (2015). Fuzzy logicbased incident detection system using loop detectors data. Transportation Research Procedia, 10, 266–275. [CrossRef] [Google Scholar]
  39. Lu, J., Chen, S., Wang, W., & van Zuylen, H. (2012). A hybrid model of partial least squares and neural network for traffic incident detection. Expert Systems with Applications , 39, 4775–4784. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.