Table of Contents Author Guidelines Submit a Manuscript
Journal of Advanced Transportation
Volume 2017, Article ID 4018409, 13 pages
https://doi.org/10.1155/2017/4018409
Research Article

A Streaming Algorithm for Online Estimation of Temporal and Spatial Extent of Delays

National Electronics and Computer Technology Center (NECTEC), 112 Thailand Science Park, Phahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani 12120, Thailand

Correspondence should be addressed to Suttipong Thajchayapong; ht.ro.cetcen@gnopayahcjaht.gnopittus

Received 2 July 2016; Accepted 20 September 2016; Published 10 January 2017

Academic Editor: David F. Llorca

Copyright © 2017 Kittipong Hiriotappa et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. N. Parrado and Y. Donoso, “Congestion based mechanism for route discovery in a V2I-V2V system applying smart devices and IoT,” Sensors, vol. 15, no. 4, pp. 7768–7806, 2015. View at Publisher · View at Google Scholar · View at Scopus
  2. K. Nellore and G. P. Hancke, “A survey on urban traffic management system using wireless sensor networks,” Sensors, vol. 16, no. 2, article 157, 2016. View at Publisher · View at Google Scholar · View at Scopus
  3. E. I. Vlahogianni, M. G. Karlaftis, and J. C. Golias, “Short-term traffic forecasting: where we are and where we're going,” Transportation Research—Part C: Emerging Technologies, vol. 43, pp. 3–19, 2014. View at Publisher · View at Google Scholar · View at Scopus
  4. R. Li and G. Rose, “Incorporating uncertainty into short-term travel time predictions,” Transportation Research Part C: Emerging Technologies, vol. 19, no. 6, pp. 1006–1018, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. Y. Chung, “A methodological approach for estimating temporal and spatial extent of delays caused by freeway accidents,” IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 3, pp. 1454–1461, 2012. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Weng and Q. Meng, “Estimating capacity and traffic delay in work zones: an overview,” Transportation Research—Part C: Emerging Technologies, vol. 35, pp. 34–45, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. X. Zhang, E. Onieva, A. Perallos, E. Osaba, and V. C. S. Lee, “Hierarchical fuzzy rule-based system optimized with genetic algorithms for short term traffic congestion prediction,” Transportation Research—Part C: Emerging Technologies, vol. 43, pp. 127–142, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Thajchayapong, E. S. Garcia-Trevino, and J. A. Barria, “Distributed classification of traffic anomalies using microscopic traffic variables,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 1, pp. 448–458, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Lippi, M. Bertini, and P. Frasconi, “Short-term traffic flow forecasting: an experimental comparison of time-series analysis and supervised learning,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 2, pp. 871–882, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. F. Zheng and H. Van Zuylen, “Urban link travel time estimation based on sparse probe vehicle data,” Transportation Research—Part C: Emerging Technologies, vol. 31, pp. 145–157, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. F. Dion and H. Rakha, “Estimating dynamic roadway travel times using automatic vehicle identification data for low sampling rates,” Transportation Research Part B: Methodological, vol. 40, no. 9, pp. 745–766, 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. D. Paterson and G. Rose, “Dynamic travel time estimation on instrumented freeways,” in Proceedings of the 6th World Congress on Intelligent Transportation Systems, Toronto, Canada, 1999.
  13. J. Kwon, B. Coifman, and P. Bickel, “Day-to-day travel-time trends and travel-time prediction from loop-detector data,” Transportation Research Record, no. 1717, pp. 120–129, 2000. View at Google Scholar · View at Scopus
  14. J. Rice and E. Van Zwet, “A simple and effective method for predicting travel times on freeways,” IEEE Transactions on Intelligent Transportation Systems, vol. 5, no. 3, pp. 200–207, 2004. View at Publisher · View at Google Scholar · View at Scopus
  15. C. D. R. Lindveld, R. Thijs, P. H. L. Bovy, and N. J. van der Zijpp, “Evaluation of online travel time estimators and predictors,” Transportation Research Record, vol. 1719, pp. 45–53, 2000. View at Publisher · View at Google Scholar · View at Scopus
  16. H. Yang, S. Chen, Y. Wang, B. Wu, and Z. Wang, “Comparison of delay estimation models for signalised intersections using field observations in Shanghai,” IET Intelligent Transport Systems, vol. 10, no. 3, pp. 165–174, 2016. View at Publisher · View at Google Scholar
  17. C. L. Mak and H. S. L. Fan, “Development of dual-station automated expressway incident detection algorithms,” IEEE Transactions on Intelligent Transportation Systems, vol. 8, no. 3, pp. 480–490, 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. K. Hi-Ri-O-Tappa, C. Likitkhajorn, A. Poolsawat, and S. Thajchayapong, “Traffic incident detection system using series of point detectors,” in Proceedings of the 15th International IEEE Conference on Intelligent Transportation Systems (ITSC '12), pp. 182–187, Anchorage, Alaska, USA, September 2012. View at Publisher · View at Google Scholar · View at Scopus
  19. V. Alexiadis, J. Colyar, J. Halkias, R. Hranac, and G. McHale, “The next generation simulation program,” ITE Journal, vol. 74, no. 8, pp. 22–26, 2004. View at Google Scholar · View at Scopus
  20. F. G. Habtemichael, M. Cetin, and K. A. Anuar, “Incident-induced delays on freeways: quantification method by grouping similar traffic patterns,” Transportation Research Record, vol. 2484, pp. 60–69, 2015. View at Publisher · View at Google Scholar
  21. J. Li, C.-J. Lan, and X. Gu, “Estimation of incident delay and its uncertainty on freeway networks,” Transportation Research Record, vol. 1959, pp. 37–45, 2006. View at Publisher · View at Google Scholar
  22. Y. Chung, “Assessment of non-recurrent congestion caused by precipitation using archived weather and traffic flow data,” Transport Policy, vol. 19, no. 1, pp. 167–173, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. J. A. Barria and S. Thajchayapong, “Detection and classification of traffic anomalies using microscopic traffic variables,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 3, pp. 695–704, 2011. View at Publisher · View at Google Scholar · View at Scopus
  24. K. HiriOtappa and S. Thajchayapong, “Generalizability and transferability of incident detection algorithm using dynamic time warping,” in Proceedings of the in 19th World Congress on Intelligent Transportation Systems, Vienna , Austria, 2012.
  25. S. Thajchayapong and J. A. Barria, “Spatial inference of traffic transition using micro–macro traffic variables,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 2, pp. 854–864, 2015. View at Publisher · View at Google Scholar · View at Scopus
  26. C. A. Ratanamahatana and E. Keogh, “Everything you know about dynamic time warping is wrong,” in Proceedings of the 3rd Workshop on Mining Temporal and Sequential Data in Conjunction with the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '04), Citeseer, Seattle, Wash, USA, August 2004.
  27. Transportation Research Thesauras, Transportation Research Board National Academy, http://trt.trb.org/trt.asp?NN=Bthd.
  28. N. Chiabaut, C. Buisson, and L. Leclercq, “Fundamental diagram estimation through passing rate measurements in congestion,” IEEE Transactions on Intelligent Transportation Systems, vol. 10, no. 2, pp. 355–359, 2009. View at Publisher · View at Google Scholar · View at Scopus
  29. J. J. Fernández-Lozano, M. Martín-Guzmán, J. Martín-Ávila, and A. García-Cerezo, “A wireless sensor network for urban traffic characterization and trend monitoring,” Sensors, vol. 15, no. 10, pp. 26143–26169, 2015. View at Publisher · View at Google Scholar · View at Scopus
  30. J.-S. Oh, C. Oh, S. G. Ritchie, and M. Chang, “Real-time estimation of accident likelihood for safety enhancement,” Journal of Transportation Engineering, vol. 131, no. 5, pp. 358–363, 2005. View at Publisher · View at Google Scholar · View at Scopus
  31. A. P. Chassiakos and Y. J. Stephanedes, “Smoothing algorithms for incident detection,” Transportation Research Record, vol. 1394, pp. 8–16, 1993. View at Google Scholar · View at Scopus
  32. V. Alarcon-Aquino and J. A. Barria, “Anomaly detection in communication networks using wavelets,” IEE Proceedings—Communications, vol. 148, no. 6, pp. 355–362, 2001. View at Publisher · View at Google Scholar · View at Scopus
  33. D. Berndt and J. Clifford, “Using dynamic time warping to find patterns in time series,” in Proceedings of the Workshop on Knowledge Discovery in Databases (KDD '94), vol. 10, pp. 359–370, Seattle, Wash, USA, August 1994.
  34. K. Kiratiratanapruk and S. Siddhichai, “Vehicle detection and tracking for traffic monitoring system,” in Proceedings of the IEEE Region 10 Conference, (TENCON '06), pp. 1–4, IEEE, Hong Kong, November 2006. View at Publisher · View at Google Scholar · View at Scopus
  35. A. Hegyi, B. De Schutter, and J. Hellendoorn, “Optimal coordination of variable speed limits to suppress shock waves,” IEEE Transactions on Intelligent Transportation Systems, vol. 6, no. 1, pp. 102–112, 2005. View at Publisher · View at Google Scholar · View at Scopus
  36. Y. Lv, Y. Duan, W. Kang, Z. Li, and F.-Y. Wang, “Traffic flow prediction with big data: a deep learning approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 2, pp. 865–873, 2015. View at Publisher · View at Google Scholar · View at Scopus
  37. P. Lopez-Garcia, E. Onieva, E. Osaba, A. D. Masegosa, and A. Perallos, “A hybrid method for short-term traffic congestion forecasting using genetic algorithms and cross entropy,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 2, pp. 557–569, 2016. View at Publisher · View at Google Scholar · View at Scopus