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    A Bayesian deep learning method for freeway incident detection with uncertainty quantification

    Liu, Genwang / Jin, Haolin / Li, Jiaze et al. | Elsevier | 2022
    Keywords: Bayesian deep learning , 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
    Keywords: Machine learning (ML)

    A comparison of statistical learning methods for deriving determining factors of accident occurrence from an imbalanced high resolution dataset

    Schlögl, Matthias / Stütz, Rainer / Laaha, Gregor et al. | Elsevier | 2019
    Keywords: Statistical learning

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

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

    A conflict-based approach for real-time road safety analysis: Comparative evaluation with crash-based models

    Orsini, Federico / Gecchele, Gregorio / Rossi, Riccardo et al. | Elsevier | 2021
    Keywords: 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
    Keywords: Machine learning

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

    Sun, Yixuan / Mallick, Tanwi / Balaprakash, Prasanna et al. | Elsevier | 2022
    Keywords: Data-centric 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
    Keywords: Machine-learning algorithms

    A deep learning approach for real-time crash prediction using vehicle-by-vehicle data

    Basso, Franco / Pezoa, Raúl / Varas, Mauricio et al. | Elsevier | 2021
    Keywords: Deep learning

    A deep learning based traffic crash severity prediction framework

    Rahim, Md Adilur / Hassan, Hany M. | Elsevier | 2021
    Keywords: Deep learning , Transfer learning

    A deep reinforcement learning-based intelligent intervention framework for real-time proactive road safety management

    Roy, Ananya / Hossain, Moinul / Muromachi, Yasunori | Elsevier | 2021
    Keywords: Deep reinforcement 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
    Keywords: Machine learning

    Advancing urban traffic accident forecasting through sparse spatio-temporal dynamic learning

    Cui, Pengfei / Yang, Xiaobao / Abdel-Aty, Mohamed et al. | Elsevier | 2024
    Keywords: Hypergraph Learning , Self-Supervised Learning

    A dynamic test scenario generation method for autonomous vehicles based on conditional generative adversarial imitation learning

    Jia, Lulu / Yang, Dezhen / Ren, Yi et al. | Elsevier | 2023
    Keywords: Conditional generative adversarial imitation learning

    A feature learning approach based on XGBoost for driving assessment and risk prediction

    Shi, Xiupeng / Wong, Yiik Diew / Li, Michael Zhi-Feng et al. | Elsevier | 2019
    Keywords: Feature learning

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

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

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

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

    A long short-term memory-based framework for crash detection on freeways with traffic data of different temporal resolutions

    Jiang, Feifeng / Yuen, Kwok Kit Richard / Lee, Eric Wai Ming | Elsevier | 2020
    Keywords: Deep learning methods

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

    Free access
    Tan, Gangping / Chen, Qingshuang / Li, Changyin et al. | BASE | 2020
    Keywords: machine learning

    Analysis of mobile phone use engagement during naturalistic driving through explainable imbalanced machine learning

    Ziakopoulos, Apostolos / Kontaxi, Armira / Yannis, George | Elsevier | 2022
    Keywords: Explainable machine learning

    Analyzing angle crashes at unsignalized intersections using machine learning techniques

    Abdel-Aty, Mohamed / Haleem, Kirolos | Elsevier | 2010
    Keywords: 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
    Keywords: Machine learning

    An analytic framework using deep learning for prediction of traffic accident injury severity based on contributing factors

    Ma, Zhengjing / Mei, Gang / Cuomo, Salvatore | Elsevier | 2021
    Keywords: Deep learning

    An integrated approach of machine learning and Bayesian spatial Poisson model for large-scale real-time traffic conflict prediction

    Li, Dongya / Fu, Chuanyun / Sayed, Tarek et al. | Elsevier | 2023
    Keywords: Machine learning

    An integrated data- and theory-driven crash severity model

    Liu, Dongjie / Li, Dawei / Sze, N.N. et al. | Elsevier | 2023
    Keywords: Interpretable machine learning

    An integrated methodology for real-time driving risk status prediction using naturalistic driving data

    Shangguan, Qiangqiang / Fu, Ting / Wang, Junhua et al. | Elsevier | 2021
    Keywords: Machine learning algorithms

    An interpretable clustering approach to safety climate analysis: Examining driver group distinctions

    Sun, Kailai / Lan, Tianxiang / Goh, Yang Miang et al. | Elsevier | 2023
    Keywords: Interpretable 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
    Keywords: 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
    Keywords: 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
    Keywords: Machine learning

    A railway intrusion detection method based on decomposition and semi-supervised learning for accident protection

    Li, Bin / Tan, Lei / Wang, Feng et al. | Elsevier | 2023
    Keywords: Semi-supervised learning

    A systematic approach to macro-level safety assessment and contributing factors analysis considering traffic crashes and violations

    Wang, Xuesong / Zhang, Xueyu / Pei, Yingying | Elsevier | 2023
    Keywords: Interpretable machine learning framework

    A task-level emergency experience reuse method for freeway accidents onsite disposal with policy distilled reinforcement learning

    Yan, Longhao / Wang, Ping / Qi, Fan et al. | Elsevier | 2023
    Keywords: Transfer learning , Multi-agent reinforcement learning

    A variable speed limit control approach for freeway tunnels based on the model-based reinforcement learning framework with safety perception

    Jin, Jieling / Li, Ye / Huang, Helai et al. | Elsevier | 2024
    Keywords: Model-based reinforcement learning

    Can automated driving prevent crashes with distracted Pedestrians? An exploration of motion planning at unsignalized Mid-block crosswalks

    Zhu, Hong / Han, Tianyang / Alhajyaseen, Wael K.M. et al. | Elsevier | 2022
    Keywords: Reinforcement learning

    Can we trust our eyes? Interpreting the misperception of road safety from street view images and deep learning

    Yu, Xujing / Ma, Jun / Tang, Yihong et al. | Elsevier | 2023
    Keywords: Deep learning

    Changes in self-reported driving intentions and attitudes while learning to drive in Great Britain

    Helman, S. / Kinnear, N.A.D. / McKenna, F.P. et al. | Elsevier | 2013
    Keywords: Learning to drive

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

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

    Collaborative robot assisted smart needle placement

    Free access
    Neidhardt, Maximilian / Gerlach, Stefan / Laves, Max-Heinrich et al. | BASE | 2021
    Keywords: machine learning

    Computer-based training for safety: Comparing methods with older and younger workers

    Wallen, Erik S. / Mulloy, Karen B. | Elsevier | 2006
    Keywords: Computer-based learning , Adult 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
    Keywords: Machine learning