access icon free Floating car data-based method for detecting flooding incident under grade separation bridges in Beijing

The congestion caused by a special type of incident that is different from a normal incident, namely the flooding incident under grade separation bridges, has shown to be a serious issue in Beijing because of several horrible recent experiences. To investigate the characteristics of the congestion, this study strives to develop a floating car data (FCD)-based method for detecting the flooding incident under grade separation bridges. The study first examines the applicability of using an improved cumulative sum (CUSUM) method. However, it is found that the improved CUSUM method does not function properly when all lanes are blocked by the flooding under bridges. Then, the study proposes an analytical method by analysing characteristics of FCD. Three decision parameters, sample losing rate, speed and accumulated discrepancy, are proposed, which play a synergistic effect in the detection. It is shown from case studies that the proposed method performs satisfactorily for detecting flooding incidents under grade separation bridges. The proposed method can be used to further investigate the congestion spreading regularities to develop quick and real-time response process to mitigating the congestion triggered by the flooding.

Inspec keywords: traffic information systems; floods; bridges (structures)

Other keywords: sample losing rate; grade separation bridges; improved CUSUM method; flooding incident detection; FCD based method; floating car data-based method; Beijing; accumulated discrepancy; speed; cumulative sum method

Subjects: Traffic engineering computing

References

    1. 1)
    2. 2)
      • 1. Farradyne, P.B.: ‘Traffic incident management handbook’ (Federal Highway Administration, Office of Travel Management, 2000).
    3. 3)
      • 14. Yu, L., Yu, L., Qi, Y., et al: ‘Traffic incident detection algorithm for urban expressways based on probe vehicle data’, Transp. Syst. Eng. Inf. Technol., 2008, 8, (4), pp. 3641.
    4. 4)
      • 10. Hi-ri-o-tappa, K., Likitkhajorn, C., Poolsawat, A., Thajchayapong, S.: ‘Traffic incident detection system using series of point detectors’. 15th Int. IEEE Conf. Intelligent Transportation Systems (ITSC), 2012, pp. 182187.
    5. 5)
      • 13. Mahmassani, H.S., Haas, C., Zhou, S., et al: ‘Evaluation of incident detection methodologies’ (Center for Transportation Research, Bureau of Engineering Research, the University of Texas at Austin, 1999).
    6. 6)
      • 2. Wen, H., Sun, J., Zhang, X.: ‘Study on traffic congestion patterns of large city in China taking Beijing as an example’. Procedia – Social and Behavioral Sciences, 14 July 2014, vol. 138, pp. 482491.
    7. 7)
      • 27. Zhang, C., Yang, X., Yan, X.: ‘An automatic incident detection methodology for freeway using floating cars’, J. Wuhan Univ. Technol. Inf. (Transp. Sci. Eng.), 2006, 30, (6), pp. 973975, 983.
    8. 8)
    9. 9)
    10. 10)
      • 18. Walters, C.H., Wiles, P.B., Cooner, S.A.: ‘Incident detection primarily by cellular phones: an evaluation of a system for dallas’. The 78th TRB Annual Meeting, Washington, DC, America, 1999.
    11. 11)
      • 23. Petty, K.F., Skabardonis, A., Varaiya, P.P.: ‘Incident detection with probe vehicles: performance’. Transportation Systems, Chania, Greece, 1997, pp. 125130.
    12. 12)
    13. 13)
      • 7. Sattayhatewa, P., Ran, B.: ‘Arterial incident detection: applying CUSUM chart method’, Traffic Eng. Control, 1999, 40, (12), pp. 582585.
    14. 14)
    15. 15)
      • 25. Dia, H., Thomas, K.: ‘Development and evaluation of arterial incident detection models using fusion of simulated probe vehicle and loop detector data’, Inf. Fusion, Special Issue on Intell. Transp. Syst., 2011, 12, (1), pp. 2027..
    16. 16)
      • 16. Mussa, N.R., Upchurch, J.E.: ‘Modeling incident detection using vehicle-to-roadside communications systems’, J. Transp. Res. Forum, 2000, 39, (4), pp. 117127.
    17. 17)
    18. 18)
    19. 19)
      • 17. Skabardonis, A., Chira-Chavala, T., Rydzewski, D.: ‘The I-880 field experiment: effectiveness of incident detection using cellular phones’ (Institute of Transportation Studies, 1998).
    20. 20)
      • 26. Basnayake, C.: ‘Automated traffic incident detection using GPS-based transit probe vehicles’. PhD Thesis, University of Calgary, 2004.
    21. 21)
    22. 22)
      • 9. Motamed, M., Machemehl, R.: ‘Real time freeway incident detection’. Report #: SWUTC 600451–00083, Southwest Region University Transportation Center (SWUTC), April 2014.
    23. 23)
      • 21. Parkany, E., Bernstein, D.: ‘Design of incident detection algorithms using vehicle-to-roadside communication sensors’, Transp. Res. Rec., 1995, 1494, pp. 6774.
    24. 24)
    25. 25)
      • 28. Beijing Transportation Research Center. Real-time Traffic Performance Index. http://www.bjtrc.org.cn/PageLayout/IndexReleased/Realtime.aspx Accessed in September 2014..
    26. 26)
      • 3. Parkany, E., Xie, C.: ‘A complete review of incident detection algorithms & their deployment: what works and what doesn't’ (New England Transport Consortium, NETCR37 Project No. 00-7, 2005).
    27. 27)
      • 4. Asare, S.K., Adu-Gyamfi, Y., Attoh-Okine, N., et al: ‘Adaptive freeway incident detection algorithm using the Hilbert-Huang transform’. 92nd Annual Meeting of the Transportation Research Board, Washington, DC, 2013.
    28. 28)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2014.0228
Loading

Related content

content/journals/10.1049/iet-its.2014.0228
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading