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1–50 von 82 Ergebnissen
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    Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis

    Parsa, Amir Bahador / Movahedi, Ali / Taghipour, Homa et al. | Elsevier | 2019
    Schlagwörter: Machine learning

    Modeling and predicting vehicle accident occurrence in Chattanooga, Tennessee

    Roland, Jeremiah / Way, Peter D. / Firat, Connor et al. | Elsevier | 2020
    Schlagwörter: Machine learning

    Short-term prediction of safety and operation impacts of lane changes in oscillations with empirical vehicle trajectories

    Li, Meng / Li, Zhibin / Xu, Chengcheng et al. | Elsevier | 2019
    Schlagwörter: Machine learning

    Traffic campaigns and overconfidence: An experimental approach

    Silva, Thiago Christiano / Laiz, Marcela T. / Tabak, Benjamin Miranda | Elsevier | 2020
    Schlagwörter: Machine learning

    Quantifying and comparing the effects of key risk factors on various types of roadway segment crashes with LightGBM and SHAP

    Wen, Xiao / Xie, Yuanchang / Wu, Lingtao et al. | Elsevier | 2021
    Schlagwörter: Machine learning

    Speed violation analysis of heavy vehicles on highways using spatial analysis and machine learning algorithms

    Kuşkapan, Emre / Çodur, M. Yasin / Atalay, Ahmet | Elsevier | 2021
    Schlagwörter: Machine learning

    Factors impacting bike crash severity in urban areas

    Dash, Ishita / Abkowitz, Mark / Philip, Craig | Elsevier | 2022
    Schlagwörter: Machine learning

    A literature review of machine learning algorithms for crash injury severity prediction

    Santos, Kenny / Dias, João P. / Amado, Conceição | Elsevier | 2021
    Schlagwörter: Machine learning

    A comparative study of machine learning classifiers for injury severity prediction of crashes involving three-wheeled motorized rickshaw

    Ijaz, Muhammad / lan, Liu / Zahid, Muhammad et al. | Elsevier | 2021
    Schlagwörter: Machine learning (ML)

    Workers’ compensation claim counts and rates by injury event/exposure among state-insured private employers in Ohio, 2007–2017

    Wurzelbacher, Steven J. / Meyers, Alysha R. / Lampl, Michael P. et al. | Elsevier | 2021
    Schlagwörter: Machine learning

    The use of machine learning improves the assessment of drug-induced driving behaviour

    van der Wall, H.E.C. / Doll, R.J. / van Westen, G.J.P. et al. | Elsevier | 2020
    Schlagwörter: Machine learning

    A machine learning model for predicting noise limits of motor vehicles in UNECE R51 regulations

    Freier Zugriff
    Tan, Gangping / Chen, Qingshuang / Li, Changyin et al. | BASE | 2020
    Schlagwörter: machine learning

    Real-time traffic accidents post-impact prediction: Based on crowdsourcing data

    Lin, Yunduan / Li, Ruimin | Elsevier | 2020
    Schlagwörter: Machine learning

    Classifying injury narratives of large administrative databases for surveillance—A practical approach combining machine learning ensembles and human review

    Marucci-Wellman, Helen R. / Corns, Helen L. / Lehto, Mark R. | Elsevier | 2016
    Schlagwörter: Machine learning

    Machine learning approaches to analysing textual injury surveillance data: A systematic review

    Vallmuur, Kirsten | Elsevier | 2015
    Schlagwörter: Machine learning

    Sensitivity and specificity of the driver sleepiness detection methods using physiological signals: A systematic review

    Watling, Christopher N. / Mahmudul Hasan, Md / Larue, Grégoire S. | Elsevier | 2020
    Schlagwörter: Machine learning

    Characterizing accident narratives with word embeddings: Improving accuracy, richness, and generalizability

    Goldberg, David M. | Elsevier | 2021
    Schlagwörter: Machine learning , Transfer learning

    The novel approaches to classify cyclist accident injury-severity: Hybrid fuzzy decision mechanisms

    Katanalp, Burak Yiğit / Eren, Ezgi | Elsevier | 2020
    Schlagwörter: Machine learning

    The usefulness of artificial intelligence for safety assessment of different transport modes

    Tselentis, Dimitrios I. / Papadimitriou, Eleonora / van Gelder, Pieter | Elsevier | 2023
    Schlagwörter: Machine Learning

    A hybrid machine learning model for predicting Real-Time secondary crash likelihood

    Li, Pei / Abdel-Aty, Mohamed | Elsevier | 2021
    Schlagwörter: Machine learning

    Enhancing autonomous vehicle hyperawareness in busy traffic environments: A machine learning approach

    Alozi, Abdul Razak / Hussein, Mohamed | Elsevier | 2024
    Schlagwörter: Machine learning

    A crash severity analysis at highway-rail grade crossings: The random survival forest method

    Keramati, Amin / Lu, Pan / Iranitalab, Amirfarrokh et al. | Elsevier | 2020
    Schlagwörter: Machine learning

    Real-time accident detection: Coping with imbalanced data

    Parsa, Amir Bahador / Taghipour, Homa / Derrible, Sybil et al. | Elsevier | 2019
    Schlagwörter: Machine learning

    Geographically weighted machine learning for modeling spatial heterogeneity in traffic crash frequency and determinants in US

    Wang, Shuli / Gao, Kun / Zhang, Lanfang et al. | Elsevier | 2024
    Schlagwörter: Spatial machine learning

    Towards safer streets: A framework for unveiling pedestrians’ perceived road safety using street view imagery

    Hamim, Omar Faruqe / Ukkusuri, Satish V. | Elsevier | 2023
    Schlagwörter: Machine learning

    A Comprehensive Railroad-Highway Grade Crossing Consolidation Model: A Machine Learning Approach

    Soleimani, Samira / Mousa, Saleh R. / Codjoe, Julius et al. | Elsevier | 2019
    Schlagwörter: Machine Learning

    Physiological signal-based drowsiness detection using machine learning: Singular and hybrid signal approaches

    Hasan, Md Mahmudul / Watling, Christopher N. / Larue, Grégoire S. | Elsevier | 2021
    Schlagwörter: Machine learning

    Exploring nighttime pedestrian crash patterns at intersection and segments: Findings from the machine learning algorithm

    Hossain, Ahmed / Sun, Xiaoduan / Shahrier, Mahir et al. | Elsevier | 2023
    Schlagwörter: Machine learning

    Application of explainable machine learning for real-time safety analysis toward a connected vehicle environment

    Yuan, Chen / Li, Ye / Huang, Helai et al. | Elsevier | 2022
    Schlagwörter: Machine learning

    Applying machine learning approaches to analyze the vulnerable road-users' crashes at statewide traffic analysis zones

    Rahman, Md Sharikur / Abdel-Aty, Mohamed / Hasan, Samiul et al. | Elsevier | 2019
    Schlagwörter: Machine learning

    A data-driven, kinematic feature-based, near real-time algorithm for injury severity prediction of vehicle occupants

    Wang, Qingfan / Gan, Shun / Chen, Wentao et al. | Elsevier | 2021
    Schlagwörter: Machine-learning algorithms

    Unveiling the relevance of traffic enforcement cameras on the severity of vehicle–pedestrian collisions in an urban environment with machine learning models

    Pineda-Jaramillo, Juan / Barrera-Jiménez, Humberto / Mesa-Arango, Rodrigo | Elsevier | 2022
    Schlagwörter: Machine learning

    Analyzing the injury severity in single-bicycle crashes: An application of the ordered forest with some practical guidance

    Zhang, Yingheng / Li, Haojie / Ren, Gang | Elsevier | 2023
    Schlagwörter: Machine learning

    Deriving a joint risk estimate from dynamic data collected at motorcycle rides

    Hula, Andreas / Fürnsinn, Florian / Schwieger, Klemens et al. | Elsevier | 2021
    Schlagwörter: Machine learning

    Safety assurance for automated driving systems that can adapt using machine learning: A qualitative interview study

    Ballingall, Stuart / Sarvi, Majid / Sweatman, Peter | Elsevier | 2022
    Schlagwörter: Machine learning

    Railroad accident analysis using extreme gradient boosting

    Bridgelall, Raj / Tolliver, Denver D. | Elsevier | 2021
    Schlagwörter: Machine learning

    Advances, challenges, and future research needs in machine learning-based crash prediction models: A systematic review

    Ali, Yasir / Hussain, Fizza / Haque, Md Mazharul | Elsevier | 2023
    Schlagwörter: Machine learning

    Integrating machine learning into path analysis for quantifying behavioral pathways in bicycle-motor vehicle crashes

    Lu, Weike / Liu, Jun / Fu, Xing et al. | Elsevier | 2022
    Schlagwörter: Machine learning

    On the interpretability of machine learning methods in crash frequency modeling and crash modification factor development

    Wen, Xiao / Xie, Yuanchang / Jiang, Liming et al. | Elsevier | 2022
    Schlagwörter: Machine learning

    Applying machine learning, text mining, and spatial analysis techniques to develop a highway-railroad grade crossing consolidation model

    Soleimani, Samira / Leitner, Michael / Codjoe, Julius | Elsevier | 2021
    Schlagwörter: Machine learning

    Young driver fatal motorcycle accident analysis by jointly maximizing accuracy and information

    Halbersberg, Dan / Lerner, Boaz | Elsevier | 2019
    Schlagwörter: Machine learning

    Computer vision and driver distraction: Developing a behaviour-flagging protocol for naturalistic driving data

    Kuo, Jonny / Koppel, Sjaan / Charlton, Judith L. et al. | Elsevier | 2014
    Schlagwörter: Machine learning

    Review on big data applications in safety research of intelligent transportation systems and connected/automated vehicles

    Lian, Yanqi / Zhang, Guoqing / Lee, Jaeyoung et al. | Elsevier | 2020
    Schlagwörter: Machine learning

    A data-centric weak supervised learning for highway traffic incident detection

    Sun, Yixuan / Mallick, Tanwi / Balaprakash, Prasanna et al. | Elsevier | 2022
    Schlagwörter: Data-centric machine learning

    Vulnerable road users’ crash hotspot identification on multi-lane arterial roads using estimated exposure and considering context classification

    Mahmoud, Nada / Abdel-Aty, Mohamed / Cai, Qing et al. | Elsevier | 2021
    Schlagwörter: Machine learning