Skip to main content
SpringerLink
Log in

Incident Detection in Freeway Based on Autocorrelation Factor of GPS Probe Data

  • Published:
International Journal of Intelligent Transportation Systems Research Aims and scope Submit manuscript

Abstract

This study proposes a statistical approach to incident detection in a section of the intercity freeway by applying GPS probe data to a GIS geofenced platform. We evaluated the proposed method using data sources from real traffic sensors of the intercity Tehran-Qom freeway in Iran. Through the SEPEHTAN project in Iran, intercity bus fleet equipped with an onboard unit that provides GPS data transferring to the central database. The main novelties in this paper are gathering density and speed time series from GPS probe data in a GIS platform and using autocorrelation factor to detect the location of the incident. The method compared with three different AID algorithms and real terms as well. Although the penetration rate was 3%, the results were considerably meet with the actual traffic condition. We reached 92.8% detection rate and 7.1% for the false alarm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Abdulhai, B., Ritchie, S.G.: Enhancing the universality and transferability of freeway incident detection using a Bayesian-based neural network. Transp. Res. C. 7(5), 261–280 (1999)

    Article  Google Scholar 

  2. Adeli, H., Karim, A.: Fuzzy-wavelet RBFNN model for freeway incident detection. J. Transp. Eng. 11–12, 464–471 (2000)

    Article  Google Scholar 

  3. Ahmed, S.A.: Stochastic processes in freeway traffic. Traffic Engineering Control (June/July). 306–310, (1983)

  4. Cathey, F.W., and Dailey, D.J., Transit Vehicles as Traffic Probe Sensors. Transportation Research Board the 81st Annual Meeting (CD-ROM), Washington, D.C., 2002

    Article  Google Scholar 

  5. Chang, E.C.-P., Wang, S.-H.: Improved freeway incident detection using fuzzy set theory. Transp. Res. Rec. 1453, 75–82 (1995)

    Google Scholar 

  6. Cook, A.R., Cleveland, D.E.: Detection of freeway capacity-reducing incidents by traffic-stream measurements. Transp. Res. Rec. 495, 1–11 (1974)

    Google Scholar 

  7. Daganzo, C.: The cell transmission model. Part II: Network traffic. Transportation Research Part B. 29(2), 79–93 (1995)

    Google Scholar 

  8. Dudek, C.L., Messer, C.J.: Incident detection on urban freeways. Transp. Res. Rec. 495, 12–24 (1974)

    Google Scholar 

  9. Golob, T.F.: Will rocker, Yannis Pavlis, probabilistic models of freeway safety performance using traffic flow data as predictors. Elsevier, Safety Science. 46(2008), 1306–1333 (2008)

    Article  Google Scholar 

  10. Guillaume, L.: Road Traffic Data: Collection Methods and Applications. JRC Technical Notes, European Commission (2008)

    Google Scholar 

  11. Hato, E., Development of MOALS (Mobile Activity Loggers Supported by GPS-Phones) for travel behavior analysis. Presented at the 85th Annual Meeting of Transportation Research Board, Washington, D.C., 2006

  12. Hiribarren, G., Herrera, J.C.: Real traffic state estimation on arterial based on trajectory data. Transp. Res. B. 69, 19–30 (2014)

    Article  Google Scholar 

  13. Hsiao, C.-H., Lin, C.-T., Cassidy, M.: Application of fuzzy logic and neural networks to automatically detect freeway traffic incidents. J. Transp. Eng. 120(5), 753–772 (1994)

    Article  Google Scholar 

  14. Ishak, S., Al-Deek, H.: Performance of automatic ANN-based incident detection on freeways. J. Transp. Eng. 281–290, (1999)

  15. Jenelius, E., Koutsopulos, H.: Travel time estimation for urban road networks using low-frequency probe vehicle data. Transp. Res. B. 53, 64–81 (2013)

    Article  Google Scholar 

  16. Levin, M., Krause, G.M.: Incident detection: a Bayesian approach. Transp. Res. Rec. 682, 52–58 (1978)

    Google Scholar 

  17. Lin, W.-H., “Incident detection with data from loop surveillance systems: the role of wave analysis. ” Dissertation, Institute of Transportation Studies, University of California at Berkeley, 1995

  18. Mak, C.L., Fan, H.S.L.: Development of dual-station automated expressway incident detection algorithms. IEEE Trans. Intell. Transp. Syst. 8(3), 480–490 (2007)

    Article  Google Scholar 

  19. Nanthawichit, C., T. Nakatsuji, and H. Suzuki. “Dynamic Estimation of Traffic States on a Freeway Using Probe Vehicle Data.” submitted to Journal of Infrastructure Planning and Management, JSCE, 2002

  20. Nanthawichit, C., Nakatsuji, T., Suzuki, H.: Application of Probe-Vehicle Data for Real-Time Traffic-State Estimation and Short-Term Travel-Time Prediction on a Freeway. In: Application of Probe Vehicle Data for Real-Time Traffic State Estimation and Short-Term Travel Time Prediction on a Freeway. TRB annual meeting (2003)

  21. Patire A. D., Wright M., Prodhomme B., Bayen A. M. “How much GPS data do we need?” Transportation Research Part C: Emerging Technologies Vol. 58, Part B, PP. 325–342, 2015

    Article  Google Scholar 

  22. Pascal, A., Defloria, F., Nivoli, M., Chiara, D., Pedroli, M.: Motorway speed pattern identification from floating vehicle data for freight applications. Transportation Research Part C: Emerging Technologies. 51, 104–119 (2015)

    Article  Google Scholar 

  23. Payne, H.J., Tignor, S.C.: Freeway incident detection algorithms based on decision trees with states. Transp. Res. Rec. 682, 30–37 (1978)

    Google Scholar 

  24. Payne, H. J., Hwelfenbein, E. D., and Knobel, H. C. (1976). “Development and testing of incident detection algorithms, 2: Research methodology and results.” Rep. No. FHWA-RD-76-20, Federal Highway Administration, Washington, DC

  25. Qiu T.z., Lu X., Chow A.H.F., Shladover S.E., “Estimation of freeway traffic density with loop detector and probe vehicle data. ” Transportation research record, Jornal of Transportation Research Board, No. 2178, Washington, D.C, 2010

  26. Ramezani M, Nikolas G., “Queue Profile Estimation in Congested Urban Networks with Probe Data” Computer-Aided Civil and Infrastructure Engineering, 1–19, 2014

    Google Scholar 

  27. Road Maintenance and Transportation organization of Iran web site, http://www.rmto.ir/SalnameAmari

  28. Sanwal, K. K., and Walrand, J., “Vehicles as Probes.” California PATH Working Paper UCB-ITS-PWP-95-11, Institue of Transportation Studies, University of California, Berkley, 1995

  29. Seo, T., Kusakabe, T.: Probe vehicle-based traffic state estimation method with spacing information and conservation law. Transportation Research Part C: Emerging Technologies. 59, 391–403 (2015)

    Article  Google Scholar 

  30. Shi, A., Haiqiang, Y., Jian, W., Na, C., Jianxun, C.: Mining urban recurrent congestion evolution pattern from GPS-equipped vehicle mobility data. Inf. Sci. 373, 515–526 (2016)

    Article  Google Scholar 

  31. Stephanedes, Y.J., Chassiakos, A.P.: Application of filtering techniques for incident detection. J. Transp. Eng. 119(1), 13–26 (1993)

    Article  Google Scholar 

  32. Tanabe, J., Asakura, Y., Itsubo, S., Maekawa, T., and Okutani, T., “Uncertainty of travel time and route choice behaviour empirical analysis using probe person data”. The 3rd International Symposium on Transport Network Reliability (INSTR2007), 2007

  33. Torfehnejad, H., Jalali, A.: Traffic condition detection in freeway by using autocorrelation of density and flow. International Journal of Transportation Engineering. 6(1), 85–98 (2018)

    Google Scholar 

  34. Van Aerde, M., Hellinga, B., Yu, L., Rakha, H.: Vehicle Probes as Real-Time ATMS Sources of Dynamic O-D and Travel Time Data, pp. 207–230. Large Urban Systems – Proceedings of the ATMS Conference, St. Petersberg, Florida (1993)

    Google Scholar 

  35. Whitson, R.H., Burr, J.H., Drew, D.R., McCasland, W.R.: Real-time evaluation of freeway quality of traffic service. Highw. Res. Rec. 289, 38–50 (1969)

    Google Scholar 

  36. Willsky A.S., Chow E.Y., Gershwin S.B, Greene C.S., Houpt P.K., Kurkjian A.L., “Dynamic model-based techniques for the detection of incidents on freeways.” IEEE Transactions on automatic control, vol. AC-25, No.3, 1980

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the ICT department of the road maintenance and transportation organization (RMTO) in Iran. We thank the colleagues in this department for providing us with the real data of fixed LPR cameras and GPS data extracted from the database servers.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran

    Ali Jalali & Hamid Torfeh Nejad

Authors
  1. Ali Jalali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamid Torfeh Nejad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Ali Jalali or Hamid Torfeh Nejad.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jalali, A., Torfeh Nejad, H. Incident Detection in Freeway Based on Autocorrelation Factor of GPS Probe Data. Int. J. ITS Res. 18, 174–182 (2020). https://doi.org/10.1007/s13177-019-00189-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13177-019-00189-y

Keywords

Search

Navigation