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


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...

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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