Skip to main content
SpringerLink
Log in

Forecasting spatiotemporal impact of traffic incidents for next-generation navigation systems

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

The advances in sensor technologies enable real-time collection of high-fidelity spatiotemporal data on transportation networks of major cities. In this paper, using two real-world transportation datasets: (1) incident and (2) traffic data, we address the problem of predicting and quantifying the impact of traffic incidents. Traffic incidents include any nonrecurring events on road networks, such as accidents, weather hazard or road construction. By analyzing archived incident data, we classify incidents based on their features (e.g., time, location, type of incident). Subsequently, we model the impact of each incident class on its surrounding traffic by analyzing the archived traffic data at the time and location of the incidents. Consequently, in real-time, if we observe a similar incident (from real-time incident feeds), we predict and quantify its impact on the surrounding traffic using our models. This information, in turn, can help drivers to effectively avoid impacted areas in real-time. To be useful for such real-time navigation application, and unlike current approaches, we study the dynamic behavior of incidents and model the impact as a quantitative time varying spatial span. In addition, we study a novel approach that improves our classification method by analyzing traffic density around the incident area and the initial behavior of the incident. We evaluated our approach with very large traffic and incident datasets collected from Los Angeles County and the results show by utilizing our impact prediction approach in the navigation system, precision of the travel time calculation can be improved by up to 67 %.

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Notes

  1. In this study, we focus on the impact on the upstream direction of incident location for incidents occurred on freeways.

  2. Specifically, we choose the maximum number of clusters while constrain \(s\) to stay in the range (0.5, 0.7], which indicating the reasonable evidence for clustering result.

  3. In most cases, 40 % is large enough to distinguish whether the speed changes is due to noisy sensor data or due to traffic incidents. Thereby, it should be a reasonable choice in the calculation of travel time.

References

  1. Al-Deek H, Garib A, Radwan AE (1995) New method for estimating freeway incident congestion. Transp Res Board 1494:30–39

    Google Scholar 

  2. Boyles S, Fajardo D,Waller ST (2007) Naive bayesian classifier for incident duration prediction. In: Proceedings of the transportation research board 86th annual meeting. http://trid.trb.org/view.aspx?id=801877

  3. Cheng H, Tan PN, Gao J, Scripps J (2006) Multistep-ahead time series prediction. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

  4. Chung Y, Recker WW (2012) A methodological approach for estimating temporal and spatial extent of delays caused by freeway accidents. IEEE Trans Intell Transp Syst 13(3):1454–1461

  5. Daganzo CF (1994) The cell transmission model: a dynamic representation of highway traffic consistent with the hydrodynamic theory. Transp Res Part B Methodol 28:269–287

    Article  Google Scholar 

  6. Giuliano G (1989) Incident characteristics, frequency, and duration on a high volume urban freeway. Transp Res Part A Gen 23(5):387–396

    Article  Google Scholar 

  7. Institute MG (2011) Big data: The next frontier for innovation, competition, and productivity. http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation

  8. Kim W, Natarajan S, Chang GL (2008) Empirical analysis and modeling of freeway incident duration. In: 11th international IEEE conference on intelligent transportation systems, 2008. ITSC 2008, pp 453–457

  9. Kwon J, Mauch M, Varaiya P (2006) Components of congestion: Delay from incidents, special events, lane closures, weather, potential ramp metering gain, and excess demand. Transportation Research Record no. 1959, Transportation Research Board, pp 84–91

  10. Lawson TW, Lovell DJ, Daganzo CF (1997) Using the input-output diagram to determine the spatial and temporal extents of a queue upstream of a bottleneck. Trans Res Rec 1572:140–147

    Article  Google Scholar 

  11. Lighthill MJ, Whitham GB (1955) On kinematic waves: Ii. a theory of traffic flow on long crowded roads. Proc R Soc A 229:317–345

  12. Mahalanobis PC (1936) On the generalised distance in statistics. Proc Natl Inst Sci India 2(1):49–55

    MATH  MathSciNet  Google Scholar 

  13. Miller M, Gupta C (2012) Mining traffic incidents to forecast impact. In: Proceeding UrbComp ’12 Proceedings of the ACM SIGKDD international workshop on Urban computing, ACM, New York, NY, pp 33–40. http://dl.acm.org/citation.cfm?id=2346502

  14. Ozbay K, Kachroo P (1999) Incident management in intelligent transportation systems. Artech House, Norwood

    Google Scholar 

  15. Pal R, Sinha KC (2002) Simulation model for evaluating and improving effectiveness of freeway service patrol programs. J Transp Eng 128:355–365

    Article  Google Scholar 

  16. Pan B, Demiryurek U, Shahabi C (2012) Utilizing real-world transportation data for accurate traffic prediction. In: IEEE international conference on data mining series (ICDM)

  17. Pan B, Demiryurek U, Gupta C, Shahabi C (2013) Forecasting spatiotemporal impact of traffic incidents on road networks. In: IEEE international conference on data mining series (ICDM)

  18. Report FM (2013) http://www.metro.net/board/Items/2012/03March/20120322RBMItem57.pdf. Last visited Feb 14, 2013

  19. RIITS (2011) http://www.riits.net. Last visited December, 2011

  20. Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65

    Article  MATH  Google Scholar 

  21. SIGALERT (2013) http://www.sigalert.com. Last visited May, 2013

  22. Smith KW, Smith BL (2012) Forecasting the clearance time of freeway accidents. In: Center Transp. Studies, Univ. Virginia, Charlottesville, VA, Rep. STL-2001-01

  23. TTI (2012) 2012 Urban mobility report and appendices. Taxes Transportation Institute. http://mobility.tamu.edu/ums/report/

  24. Wang Z, Murray-Tuite PM (2010) A cellular automata approach to estimate incident-related travel time on interstate 66 in near real time. Virginia transportation research council

  25. Watson GS (1971) Review: M. G. Kendall, A. Stuart, The Advanced Theory of Statistics. Ann Math Statist 42(3):1138–1140

  26. Waze (2013) http://www.waze.com. Last visited May, 2013

  27. Wirasinghe SC (1978) Determination of traffic delays from shock-wave analysis. Transp Res 12:343–348

Download references

Acknowledgments

This research has been funded in part by NSF Grant IIS-1115153, a contract with Los Angeles Metropolitan Transportation Authority (LA Metro), the USC Integrated Media Systems Center (IMSC), HP Labs and unrestricted cash gifts from Google, Northrop Grumman, Microsoft and Oracle. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily react the views of any of the sponsors such as the National Science Foundation or LA Metro.

Author information

Authors and Affiliations

  1. Integrated Media System Center, University of Southern California, Los Angeles, CA, USA

    Bei Pan, Ugur Demiryurek & Cyrus Shahabi

  2. Hitachi Americas R&D, Tarrytown, NY, USA

    Chetan Gupta

  3. Department of Computer Science, University of Southern California, Los Angeles, CA, USA

    Bei Pan

Authors
  1. Bei Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ugur Demiryurek
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chetan Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cyrus Shahabi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bei Pan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pan, B., Demiryurek, U., Gupta, C. et al. Forecasting spatiotemporal impact of traffic incidents for next-generation navigation systems. Knowl Inf Syst 45, 75–104 (2015). https://doi.org/10.1007/s10115-014-0783-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-014-0783-6

Keywords

Search

Navigation