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The Continuous Risk Profile Approach for the Identification of High Collision Concentration Locations on Congested Highways

In: Transportation and Traffic Theory 2009: Golden Jubilee

Author

Listed:
  • Koohong Chung

    (California Department of Transportation)

  • David R. Ragland

    (University of California)

Abstract

This paper documents a new method for monitoring traffic collision data from continuous roadway facilities to detect high collision concentration locations. Many existing methods for detecting collision concentration locations require segmentation of roadways and assume traffic collision data are spatially uncorrelated, resulting in both false positives (i.e., identifying sites for safety improvements that should not have been selected) and false negatives (i.e., not identifying sites that should have been selected). The proposed method does not require segmentation of roadways; spatial correlation in the collision data does not affect the results of analysis. This new method has a lower false positive rate than the conventional sliding moving window approach. This paper shows how the proposed method can proactively identify high collision concentration locations and capture the benefit of safety improvements observed in the project location and in neighboring sites.

Suggested Citation

  • Koohong Chung & David R. Ragland, 2009. "The Continuous Risk Profile Approach for the Identification of High Collision Concentration Locations on Congested Highways," Springer Books, in: William H. K. Lam & S. C. Wong & Hong K. Lo (ed.), Transportation and Traffic Theory 2009: Golden Jubilee, chapter 0, pages 463-480, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4419-0820-9_23
    DOI: 10.1007/978-1-4419-0820-9_23
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    Citations

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    Cited by:

    1. Grembek, Offer & Kim, Kwangho & Kwon, Oh Hoon & Lee, Jinwoo & Liu, Haotian & Park, Min Ju & Washington, Simon & Ragland, David & Madanat, Samer M., 2012. "Experimental Evaluation of the Continuous Risk Profile (CRP) Approach to the Current Caltrans Methodology for High Collision Concentration Location Identification," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6sg5c0ng, Institute of Transportation Studies, UC Berkeley.
    2. Elyasi, Mohammad Reza & Saffarzade, Mahmoud & Boroujerdian, Amin Mirza, 2016. "A novel dynamic segmentation model for identification and prioritization of black spots based on the pattern of potential for safety improvement," Transportation Research Part A: Policy and Practice, Elsevier, vol. 91(C), pages 346-357.
    3. Medury, Aditya & Grembek, Offer, 2014. "Dynamic Programming-based Pedestrian Hotspot Identification Approach," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt10d0x1z7, Institute of Transportation Studies, UC Berkeley.
    4. Eui-Jin Kim & Oh Hoon Kwon & Shin Hyoung Park & Dong-Kyu Kim & Koohong Chung, 2021. "Application of naïve Bayesian approach in detecting reproducible fatal collision locations on freeway," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-21, May.
    5. Chung, Koohong & Jang, Kitae & Madanat, Samer & Washington, Simon, 2011. "Proactive detection of high collision concentration locations on highways," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(9), pages 927-934, November.

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