These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

214 related articles for article (PubMed ID: 33302233)

  • 1. Mid-term prediction of at-fault crash driver frequency using fusion deep learning with city-level traffic violation data.
    Wu YW; Hsu TP
    Accid Anal Prev; 2021 Feb; 150():105910. PubMed ID: 33302233
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Class-imbalanced crash prediction based on real-time traffic and weather data: A driving simulator study.
    Elamrani Abou Elassad Z; Mousannif H; Al Moatassime H
    Traffic Inj Prev; 2020; 21(3):201-208. PubMed ID: 32125890
    [No Abstract]   [Full Text] [Related]  

  • 3. Real-time driving risk assessment using deep learning with XGBoost.
    Shi L; Qian C; Guo F
    Accid Anal Prev; 2022 Dec; 178():106836. PubMed ID: 36191455
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A systematic approach to macro-level safety assessment and contributing factors analysis considering traffic crashes and violations.
    Wang X; Zhang X; Pei Y
    Accid Anal Prev; 2024 Jan; 194():107323. PubMed ID: 37864889
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Efficient mapping of crash risk at intersections with connected vehicle data and deep learning models.
    Hu J; Huang MC; Yu X
    Accid Anal Prev; 2020 Sep; 144():105665. PubMed ID: 32683130
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data.
    Bao J; Liu P; Ukkusuri SV
    Accid Anal Prev; 2019 Jan; 122():239-254. PubMed ID: 30390519
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework.
    Wang C; Liu L; Xu C; Lv W
    Int J Environ Res Public Health; 2019 Jan; 16(3):. PubMed ID: 30691063
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Safety critical event prediction through unified analysis of driver and vehicle volatilities: Application of deep learning methods.
    Arvin R; Khattak AJ; Qi H
    Accid Anal Prev; 2021 Mar; 151():105949. PubMed ID: 33385957
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A deep learning based traffic crash severity prediction framework.
    Rahim MA; Hassan HM
    Accid Anal Prev; 2021 May; 154():106090. PubMed ID: 33740462
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study.
    Zahid M; Chen Y; Jamal A; Al-Ofi KA; Al-Ahmadi HM
    Int J Environ Res Public Health; 2020 Jul; 17(14):. PubMed ID: 32708404
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Crash involvement of motor vehicles in relationship to the number and severity of traffic offenses. An exploratory analysis of Dutch traffic offenses and crash data.
    Goldenbeld C; Reurings M; Van Norden Y; Stipdonk H
    Traffic Inj Prev; 2013; 14(6):584-91. PubMed ID: 23859422
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Expressway crash risk prediction using back propagation neural network: A brief investigation on safety resilience.
    Wang J; Kong Y; Fu T
    Accid Anal Prev; 2019 Mar; 124():180-192. PubMed ID: 30660834
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Understand the impact of traffic states on crash risk in the vicinities of Type A weaving segments: A deep learning approach.
    Zhao J; Liu P; Xu C; Bao J
    Accid Anal Prev; 2021 Sep; 159():106293. PubMed ID: 34252581
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Applying machine learning approaches to analyze the vulnerable road-users' crashes at statewide traffic analysis zones.
    Rahman MS; Abdel-Aty M; Hasan S; Cai Q
    J Safety Res; 2019 Sep; 70():275-288. PubMed ID: 31848006
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Predicting crash-relevant violations at stop sign-controlled intersections for the development of an intersection driver assistance system.
    Scanlon JM; Sherony R; Gabler HC
    Traffic Inj Prev; 2016 Sep; 17 Suppl 1():59-65. PubMed ID: 27586104
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Driver Behavior Profiling and Recognition Using Deep-Learning Methods: In Accordance with Traffic Regulations and Experts Guidelines.
    Al-Hussein WA; Por LY; Kiah MLM; Zaidan BB
    Int J Environ Res Public Health; 2022 Jan; 19(3):. PubMed ID: 35162493
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Crash involvement of drivers with multiple crashes.
    Chandraratna S; Stamatiadis N; Stromberg A
    Accid Anal Prev; 2006 May; 38(3):532-41. PubMed ID: 16405858
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A data-driven Bayesian network for probabilistic crash risk assessment of individual driver with traffic violation and crash records.
    Joo YJ; Kho SY; Kim DK; Park HC
    Accid Anal Prev; 2022 Oct; 176():106790. PubMed ID: 35933893
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Driver behavior analysis on rural 2-lane, 2-way highways using SHRP 2 NDS data.
    Wu J; Xu H
    Traffic Inj Prev; 2018; 19(8):838-843. PubMed ID: 30689397
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Transfer learning for spatio-temporal transferability of real-time crash prediction models.
    Man CK; Quddus M; Theofilatos A
    Accid Anal Prev; 2022 Feb; 165():106511. PubMed ID: 34894483
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.