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Journal Abstract Search

379 related items for PubMed ID: 29502853

  • 1. 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]

  • 2. 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; 20(1):20-26. PubMed ID: 28162916
    [Abstract] [Full Text] [Related]

  • 3. A multinomial logit model-Bayesian network hybrid approach for driver injury severity analyses in rear-end crashes.
    Chen C, Zhang G, Tarefder R, Ma J, Wei H, Guan H.
    Accid Anal Prev; 2015 Jul; 80():76-88. PubMed ID: 25888994
    [Abstract] [Full Text] [Related]

  • 4. 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]

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

  • 6. The Association between Regional Environmental Factors and Road Trauma Rates: A Geospatial Analysis of 10 Years of Road Traffic Crashes in British Columbia, Canada.
    Brubacher JR, Chan H, Erdelyi S, Schuurman N, Amram O.
    PLoS One; 2016 Nov; 11(4):e0153742. PubMed ID: 27099930
    [Abstract] [Full Text] [Related]

  • 7. 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]

  • 8. Understanding the effects of vehicle platoons on crash type and severity.
    Hyun KK, Mitra SK, Jeong K, Tok A.
    Accid Anal Prev; 2021 Jan; 149():105858. PubMed ID: 33220605
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  • 9. 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; 113():149-158. PubMed ID: 29407662
    [Abstract] [Full Text] [Related]

  • 10. Modeling rear-end collisions including the role of driver's visibility and light truck vehicles using a nested logit structure.
    Abdel-Aty M, Abdelwahab H.
    Accid Anal Prev; 2004 May; 36(3):447-56. PubMed ID: 15003590
    [Abstract] [Full Text] [Related]

  • 11. 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]

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

  • 13. Analysis of pre-crash scenarios and contributing factors for autonomous vehicle crashes at intersections.
    Liu Q, Wang X, Liu S, Yu C, Glaser Y.
    Accid Anal Prev; 2024 Feb; 195():107383. PubMed ID: 37984113
    [Abstract] [Full Text] [Related]

  • 14. Effect of driver's age and side of impact on crash severity along urban freeways: a mixed logit approach.
    Haleem K, Gan A.
    J Safety Res; 2013 Sep; 46():67-76. PubMed ID: 23932687
    [Abstract] [Full Text] [Related]

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

  • 16. Evaluation of rear-end crash risk at work zone using work zone traffic data.
    Meng Q, Weng J.
    Accid Anal Prev; 2011 Jul; 43(4):1291-300. PubMed ID: 21545857
    [Abstract] [Full Text] [Related]

  • 17. 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]

  • 18. Effects of peripheral transverse line markings on drivers' speed and headway choice and crash risk in car-following: A naturalistic observation study.
    Ding N, Zhu S, Jiao N, Liu B.
    Accid Anal Prev; 2020 Oct; 146():105701. PubMed ID: 32823033
    [Abstract] [Full Text] [Related]

  • 19. Exploratory analysis of automated vehicle crashes in California: A text analytics & hierarchical Bayesian heterogeneity-based approach.
    Boggs AM, Wali B, Khattak AJ.
    Accid Anal Prev; 2020 Feb; 135():105354. PubMed ID: 31790970
    [Abstract] [Full Text] [Related]

  • 20. 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; 54(5):687-97. PubMed ID: 23156615
    [Abstract] [Full Text] [Related]

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