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Journal Abstract Search

215 related items for PubMed ID: 38968864

  • 1. Exploring spatial heterogeneity in factors associated with injury severity in speeding-related crashes: An integrated machine learning and spatial modeling approach.
    Zhang Z, Xu N, Liu J, Jones S.
    Accid Anal Prev; 2024 Oct; 206():107697. PubMed ID: 38968864
    [Abstract] [Full Text] [Related]

  • 2. Unveiling the risks of speeding behavior by investigating the dynamics of driver injury severity through advanced analytics.
    Islam M, Hosseini P, Kakhani A, Jalayer M, Patel D.
    Sci Rep; 2024 Sep 28; 14(1):22431. PubMed ID: 39341813
    [Abstract] [Full Text] [Related]

  • 3. Hotspots and causes of motor vehicle crashes in Baltimore, Maryland: A geospatial analysis of five years of police crash and census data.
    Dezman Z, de Andrade L, Vissoci JR, El-Gabri D, Johnson A, Hirshon JM, Staton CA.
    Injury; 2016 Nov 28; 47(11):2450-2458. PubMed ID: 27614672
    [Abstract] [Full Text] [Related]

  • 4. Ordered logistic models of influencing factors on crash injury severity of single and multiple-vehicle downgrade crashes: A case study in Wyoming.
    Rezapour M, Moomen M, Ksaibati K.
    J Safety Res; 2019 Feb 28; 68():107-118. PubMed ID: 30876502
    [Abstract] [Full Text] [Related]

  • 5. LAVIA--an evaluation of the potential safety benefits of the French intelligent speed adaptation project.
    Driscoll R, Page Y, Lassarre S, Ehrlich J.
    Annu Proc Assoc Adv Automot Med; 2007 Feb 28; 51():485-505. PubMed ID: 18184509
    [Abstract] [Full Text] [Related]

  • 6. Bicyclist injury severity in traffic crashes: A spatial approach for geo-referenced crash data to uncover non-stationary correlates.
    Liu J, Khattak AJ, Li X, Nie Q, Ling Z.
    J Safety Res; 2020 Jun 28; 73():25-35. PubMed ID: 32563400
    [Abstract] [Full Text] [Related]

  • 7. Crash severity along rural mountainous highways in Malaysia: An application of a combined decision tree and logistic regression model.
    Rusli R, Haque MM, Saifuzzaman M, King M.
    Traffic Inj Prev; 2018 Jun 28; 19(7):741-748. PubMed ID: 29932734
    [Abstract] [Full Text] [Related]

  • 8. Characteristics of Single Vehicle Crashes with a Teen Driver in South Carolina, 2005-2008.
    Shults RA, Bergen G, Smith TJ, Cook L, Kindelberger J, West B.
    Accid Anal Prev; 2019 Jan 28; 122():325-331. PubMed ID: 28947072
    [Abstract] [Full Text] [Related]

  • 9. Analyzing injury severity of three-wheeler motorized rickshaws: A correlated random parameters approach with heterogeneity in means.
    Pervez A, Jamal A, Haider Khan S.
    Accid Anal Prev; 2024 Sep 28; 204():107651. PubMed ID: 38833987
    [Abstract] [Full Text] [Related]

  • 10. 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 Sep 28; 11(4):e0153742. PubMed ID: 27099930
    [Abstract] [Full Text] [Related]

  • 11. Speed enforcement detection devices for preventing road traffic injuries.
    Wilson C, Willis C, Hendrikz JK, Bellamy N.
    Cochrane Database Syst Rev; 2006 Apr 19; (2):CD004607. PubMed ID: 16625608
    [Abstract] [Full Text] [Related]

  • 12. Examining factors affecting driver injury severity in speeding-related crashes: a comparative study across driver age groups.
    Se C, Champahom T, Jomnonkwao S, Ratanavaraha V.
    Int J Inj Contr Saf Promot; 2024 Jun 19; 31(2):234-255. PubMed ID: 38190335
    [Abstract] [Full Text] [Related]

  • 13. Modeling single-vehicle run-off-road crash severity in rural areas: Accounting for unobserved heterogeneity and age difference.
    Gong L, Fan WD.
    Accid Anal Prev; 2017 Apr 19; 101():124-134. PubMed ID: 28226253
    [Abstract] [Full Text] [Related]

  • 14. Examining driver injury severity in motor vehicle crashes: A copula-based approach considering temporal heterogeneity in a developing country context.
    Pervaz S, Bhowmik T, Eluru N.
    Accid Anal Prev; 2024 Oct 19; 206():107721. PubMed ID: 39059315
    [Abstract] [Full Text] [Related]

  • 15. Data mining approach to explore emergency vehicle crash patterns: A comparative study of crash severity in emergency and non-emergency response modes.
    Hossain MM, Zhou H, Das S.
    Accid Anal Prev; 2023 Oct 19; 191():107217. PubMed ID: 37453252
    [Abstract] [Full Text] [Related]

  • 16. Wrong-way driving crashes: A random-parameters ordered probit analysis of injury severity.
    Jalayer M, Shabanpour R, Pour-Rouholamin M, Golshani N, Zhou H.
    Accid Anal Prev; 2018 Aug 19; 117():128-135. PubMed ID: 29698866
    [Abstract] [Full Text] [Related]

  • 17. Investigating the influence of streetscape environmental characteristics on pedestrian crashes at intersections using street view images and explainable machine learning.
    Yue H.
    Accid Anal Prev; 2024 Sep 19; 205():107693. PubMed ID: 38955107
    [Abstract] [Full Text] [Related]

  • 18. Exploring the effects of roadway characteristics on the frequency and severity of head-on crashes: case studies from Malaysian federal roads.
    Hosseinpour M, Yahaya AS, Sadullah AF.
    Accid Anal Prev; 2014 Jan 19; 62():209-22. PubMed ID: 24172088
    [Abstract] [Full Text] [Related]

  • 19. Exploring the factors contribute to the injury severities of vulnerable roadway user involved crashes.
    Dong C, Khattak AJ, Shao C, Xie K.
    Int J Inj Contr Saf Promot; 2019 Sep 19; 26(3):302-314. PubMed ID: 31169068
    [Abstract] [Full Text] [Related]

  • 20. A novel framework for crash frequency prediction: Geographic support vector regression based on agent-based activity models in Greater Melbourne.
    Duong Q, Gilbert H, Nguyen H.
    Accid Anal Prev; 2024 Nov 19; 207():107747. PubMed ID: 39163666
    [Abstract] [Full Text] [Related]

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