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Journal Abstract Search

456 related items for PubMed ID: 30802778

  • 1. Impact of heterogeneity of car-following behavior on rear-end crash risk.
    Zhang J, Wang Y, Lu G.
    Accid Anal Prev; 2019 Apr; 125():275-289. PubMed ID: 30802778
    [Abstract] [Full Text] [Related]

  • 2. A rear-end collision risk assessment model based on drivers' collision avoidance process under influences of cell phone use and gender-A driving simulator based study.
    Li X, Yan X, Wu J, Radwan E, Zhang Y.
    Accid Anal Prev; 2016 Dec; 97():1-18. PubMed ID: 27565040
    [Abstract] [Full Text] [Related]

  • 3. Assessing rear-end crash potential in urban locations based on vehicle-by-vehicle interactions, geometric characteristics and operational conditions.
    Dimitriou L, Stylianou K, Abdel-Aty MA.
    Accid Anal Prev; 2018 Sep; 118():221-235. PubMed ID: 29502853
    [Abstract] [Full Text] [Related]

  • 4. Effect of daily car-following behaviors on urban roadway rear-end crashes and near-crashes: A naturalistic driving study.
    Wang X, Zhang X, Guo F, Gu Y, Zhu X.
    Accid Anal Prev; 2022 Jan; 164():106502. PubMed ID: 34837850
    [Abstract] [Full Text] [Related]

  • 5. In-depth analysis of drivers' merging behavior and rear-end crash risks in work zone merging areas.
    Weng J, Xue S, Yang Y, Yan X, Qu X.
    Accid Anal Prev; 2015 Apr; 77():51-61. PubMed ID: 25687332
    [Abstract] [Full Text] [Related]

  • 6. How instantaneous driving behavior contributes to crashes at intersections: Extracting useful information from connected vehicle message data.
    Arvin R, Kamrani M, Khattak AJ.
    Accid Anal Prev; 2019 Jun; 127():118-133. PubMed ID: 30851563
    [Abstract] [Full Text] [Related]

  • 7. Analysis of rear-end crash potential and driver contributing factors based on car-following driving simulation.
    Bumrungsup L, Kanitpong K.
    Traffic Inj Prev; 2022 Jun; 23(5):296-301. PubMed ID: 35522546
    [Abstract] [Full Text] [Related]

  • 8. Analysis of naturalistic driving videos of fleet services drivers to estimate driver error and potentially distracting behaviors as risk factors for rear-end versus angle crashes.
    Harland KK, Carney C, McGehee D.
    Traffic Inj Prev; 2016 Jul 03; 17(5):465-71. PubMed ID: 26760293
    [Abstract] [Full Text] [Related]

  • 9. Heavy-truck drivers' following behavior with intervention of an integrated, in-vehicle crash warning system: a field evaluation.
    Bao S, LeBlanc DJ, Sayer JR, Flannagan C.
    Hum Factors; 2012 Oct 03; 54(5):687-97. PubMed ID: 23156615
    [Abstract] [Full Text] [Related]

  • 10. Analysis of work zone rear-end crash risk for different vehicle-following patterns.
    Weng J, Meng Q, Yan X.
    Accid Anal Prev; 2014 Nov 03; 72():449-57. PubMed ID: 25150525
    [Abstract] [Full Text] [Related]

  • 11. Assessing rear-end collision risk of cars and heavy vehicles on freeways using a surrogate safety measure.
    Zhao P, Lee C.
    Accid Anal Prev; 2018 Apr 03; 113():149-158. PubMed ID: 29407662
    [Abstract] [Full Text] [Related]

  • 12. Characteristics of rear-end crashes involving passenger vehicles with automatic emergency braking.
    Cicchino JB, Zuby DS.
    Traffic Inj Prev; 2019 Apr 03; 20(sup1):S112-S118. PubMed ID: 31381436
    [Abstract] [Full Text] [Related]

  • 13. Using naturalistic driving study data to investigate the impact of driver distraction on driver's brake reaction time in freeway rear-end events in car-following situation.
    Gao J, Davis GA.
    J Safety Res; 2017 Dec 03; 63():195-204. PubMed ID: 29203019
    [Abstract] [Full Text] [Related]

  • 14. Reducing the risk of rear-end collisions with infrastructure-to-vehicle (I2V) integration of variable speed limit control and adaptive cruise control system.
    Li Y, Wang H, Wang W, Liu S, Xiang Y.
    Traffic Inj Prev; 2016 Aug 17; 17(6):597-603. PubMed ID: 26761633
    [Abstract] [Full Text] [Related]

  • 15. Does assisted driving behavior lead to safety-critical encounters with unequipped vehicles' drivers?
    Preuk K, Stemmler E, Schießl C, Jipp M.
    Accid Anal Prev; 2016 Oct 17; 95(Pt A):149-56. PubMed ID: 27442594
    [Abstract] [Full Text] [Related]

  • 16. Crash probability estimation via quantifying driver hazard perception.
    Li Y, Zheng Y, Wang J, Kodaka K, Li K.
    Accid Anal Prev; 2018 Jul 17; 116():116-125. PubMed ID: 28595973
    [Abstract] [Full Text] [Related]

  • 17. Validating a driving simulator using surrogate safety measures.
    Yan X, Abdel-Aty M, Radwan E, Wang X, Chilakapati P.
    Accid Anal Prev; 2008 Jan 17; 40(1):274-88. PubMed ID: 18215559
    [Abstract] [Full Text] [Related]

  • 18. Is vehicle automation enough to prevent crashes? Role of traffic operations in automated driving environments for traffic safety.
    Jeong E, Oh C, Lee S.
    Accid Anal Prev; 2017 Jul 17; 104():115-124. PubMed ID: 28499140
    [Abstract] [Full Text] [Related]

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  • 20. Investigation on occupant injury severity in rear-end crashes involving trucks as the front vehicle in Beijing area, China.
    Yuan Q, Lu M, Theofilatos A, Li YB.
    Chin J Traumatol; 2017 Feb 17; 20(1):20-26. PubMed ID: 28162916
    [Abstract] [Full Text] [Related]

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