Encyclopedia of GIS

2017 Edition
| Editors: Shashi Shekhar, Hui Xiong, Xun Zhou

Accident Impact Prediction

  • Cyrus Shahabi
  • Bei(Penny) Pan
Reference work entry
DOI: https://doi.org/10.1007/978-3-319-17885-1_1568

Definition

For the first time, real-time high-fidelity spatiotemporal data on the transportation networks of major cities have become available. This gold mine of data can be utilized to learn about the behavior of traffic congestion at different times and locations, potentially resulting in major savings in time and fuel, the two important commodities of the twenty-first century. According to FASANA Motion report (Report 2012), approximately 50% of the freeway congestions are caused by nonrecurring issues, such as traffic accidents, weather hazard, special events, and construction zone closures. Hence, it is fairly important to quantify and predict the impact of traffic incidents on the surrounding traffic. This quantification can alleviate the significant financial and time losses attributed to traffic incidents, for example, it can be used by city transportation agencies for providing evacuation plan to eliminate potential congested grid locks, for effective dispatching of...

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

References

  1. Arnold A, Liu Y, Abe N (2007) Temporal causal modeling with graphical granger methods. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, KDD’07. ACM, New York, pp 66–75Google Scholar
  2. Daganzo CF (1994) The cell transmission model: a dynamic representation of highway traffic consistent with the hydrodynamic theory. Transp Res Part B: Methodol 28:269–287CrossRefGoogle Scholar
  3. Demiryurek U, Banaei-Kashani F, Shahabi C, Ranganathan A (2011) Online computation of fastest path in time-dependent spatial networks. In: SSTD, MinneapolisCrossRefGoogle Scholar
  4. Glymour C, Scheines R, Spirtes P, Kelly K (1987) Discovering causal structure. Academic, OrlandozbMATHGoogle Scholar
  5. Granger CWJ (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37:424–438CrossRefGoogle Scholar
  6. Hall MA, Smith LA (1998) Practical feature subset selection for machine learning. In: ACSC98, Perth. Springer, Berlin, pp 181–191Google Scholar
  7. Kwon J, Mauch M, Varaiya PP (2006) Components of congestion: delay from incidents, special events, lane closures, weather, potential ramp metering gain, and excess demand. Transp Res Rec 1959:84–91CrossRefGoogle Scholar
  8. 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 1572:140–147CrossRefGoogle Scholar
  9. Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C, Byers AH (2011) Big data: the next frontier for innovation, competition, and productivity. McKinsey Global Institute, New YorkGoogle Scholar
  10. Ozbay K, Kachroo P (1999) Incident management in intelligent transportation systems. Artech House, Norwood, MAGoogle Scholar
  11. Pal R, Sinha KC (2002) Simulation model for evaluating and improving effectiveness of freeway service patrol programs. J Transp Eng 128:355–365CrossRefGoogle Scholar
  12. Pan B, Demiryurek U, Shahabi C (2012) Utilizing real-world transportation data for accurate traffic prediction. In: ICDM, BrusselsCrossRefGoogle Scholar
  13. Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San MateozbMATHGoogle Scholar
  14. SIGALERT (2013) http://www.sigalert.com. Last visited May 2013
  15. Spirtes P, Glymour C, Scheines R (2001) Causation, prediction, and search. MIT, CambridgezbMATHGoogle Scholar
  16. 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, CharlottesvilleGoogle Scholar
  17. WAZE (2014) http://www.waze.com. Last visited May 2014
  18. Wirasinghe SC (1978) Determination of traffic delays from shock-wave analysis. Trans Res 12:343–348CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Cyrus Shahabi
    • 1
    • 2
    • 3
    • 4
  • Bei(Penny) Pan
    • 5
  1. 1.Computer Science DepartmentUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Information Laboratory (InfoLab), Computer Science DepartmentUniversity of Southern CaliforniaLos AngelesUSA
  3. 3.University of Southern CaliforniaLos AngelesUSA
  4. 4.Integrated Media Systems CenterUniversity of Southern CaliforniaLos AngelesUSA
  5. 5.Microsoft Corp.RedmondUSA