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keywords:(learning)

    Worst-case scenarios identification approach for the evaluation of advanced driver assistance systems in intelligent/autonomous vehicles under multiple conditions

    Chelbi, Nacer Eddine / Gingras, Denis / Sauvageau, Claude | Taylor & Francis Verlag | 2022
    Schlagwörter: ensemble and machine learning

    Welding distortion prediction of ship plate frame structures based on a self-learning database

    Cao, Yu / Song, Yuze / Zhang, Tao et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: self-learning database

    Wake distribution prediction on the propeller plane in ship design using artificial intelligence

    Kim, S.-Y. / Moon, B. Y. | Taylor & Francis Verlag | 2006
    Schlagwörter: learning algorithm

    Vehicle yaw stability control with a two-layered learning MPC

    Zhang, Zhiming / Xie, Lei / Lu, Shan et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: model learning

    Using reinforcement learning to minimize taxi idle times

    O’Keeffe, Kevin / Anklesaria, Sam / Santi, Paolo et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: machine learning , reinforcement learning

    Traffic volume prediction on low-volume roadways: a Cubist approach

    Das, Subasish | Taylor & Francis Verlag | 2021
    Schlagwörter: machine learning

    Traffic sign extraction using deep hierarchical feature learning and mobile light detection and ranging (LiDAR) data on rural highways

    Gouda, Maged / Epp, Alexander / Tilroe, Rowan et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: Deep learning

    The level of delay caused by crashes (LDC) in metropolitan and non-metropolitan areas: a comparative analysis of improved Random Forests and LightGBM

    Wang, Zehao / Jiao, Pengpeng / Wang, Jianyu et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: feature learning , machine learning

    Testing and enhancing spatial transferability of artificial neural networks based travel behavior models

    Koushik, Anil NP / Manoj, M / Nezamuddin, N et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: Machine learning , Transfer learning

    Station-level short-term demand forecast of carsharing system via station-embedding-based hybrid neural network

    Zhao, Feifei / Wang, Weiping / Sun, Huijun et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: Machine learning

    Social group detection based on multi-level consistent behaviour characteristics

    Li, Meng / Chen, Tao / Du, Hao et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: unsupervised learning

    Short-term prediction of traffic dynamics with real-time recurrent learning algorithms

    Sheu, Jiuh-Biing / Lan, Lawrence W. / Huang, Yi-San | Taylor & Francis Verlag | 2009
    Schlagwörter: real-time recurrent learning

    Ship selection in port state control: status and perspectives

    Yan, Ran / Wang, Shuaian / Peng, Chuansheng | Taylor & Francis Verlag | 2022
    Schlagwörter: machine learning in maritime transportation

    Shipping market forecasting by forecast combination mechanism

    Gao, Ruobin / Liu, Jiahui / Du, Liang et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: machine learning

    Ship classification based on convolutional neural networks

    Yang, Yang / Ding, Kaifa / Chen, Zhuang | Taylor & Francis Verlag | 2022
    Schlagwörter: transfer learning

    SATP-GAN: self-attention based generative adversarial network for traffic flow prediction

    Zhang, Liang / Wu, Jianqing / Shen, Jun et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: reinforcement learning

    Revisiting kernel logistic regression under the random utility models perspective. An interpretable machine-learning approach

    Martín-Baos, José Ángel / García-Ródenas, Ricardo / Rodriguez-Benitez, Luis | Taylor & Francis Verlag | 2021
    Schlagwörter: machine Learning

    Reinforcement learning-enabled genetic algorithm for school bus scheduling

    Köksal Ahmed, Eda / Li, Zengxiang / Veeravalli, Bharadwaj et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: reinforcement learning

    Real-time traffic incident detection based on a hybrid deep learning model

    Li, Linchao / Lin, Yi / Du, Bowen et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: deep learning

    Real-time optimal trajectory planning for autonomous vehicles and lap time simulation using machine learning

    Freier Zugriff
    Garlick, S. / Bradley, A. | Taylor & Francis Verlag | 2022
    Schlagwörter: machine learning

    Propeller optimization by interactive genetic algorithms and machine learning

    Freier Zugriff
    Gypa, Ioli / Jansson, Marcus / Wolff, Krister et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: machine learning

    Probabilistic traffic breakdown forecasting through Bayesian approximation using variational LSTMs

    Zechin, Douglas / Cybis, Helena Beatriz Bettella | Taylor & Francis Verlag | 2023
    Schlagwörter: machine learning

    Prediction of pedestrians’ wait-or-go decision using trajectory data based on gradient boosting decision tree

    Xin, Xiuying / Jia, Ning / Ling, Shuai et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: ensemble learning

    Prediction of extent of damage in vehicle during crash using improved XGBoost model

    Vadhwani, Diya / Thakor, Devendra | Taylor & Francis Verlag | 2023
    Schlagwörter: machine learning

    Prediction model of crash severity in imbalanced dataset using data leveling methods and metaheuristic optimization algorithms

    Danesh, Akbar / Ehsani, Mehrdad / Moghadas Nejad, Fereidoon et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: machine learning algorithm

    Predicting the traction power of metropolitan railway lines using different machine learning models

    Pineda-Jaramillo, J. / Martínez-Fernández, P. / Villalba-Sanchis, I. et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: Machine Learning

    Predicting incident duration using random forests

    Hamad, Khaled / Al-Ruzouq, Rami / Zeiada, Waleed et al. | Taylor & Francis Verlag | 2020
    Schlagwörter: machine learning

    Predicting and explaining severity of road accident using artificial intelligence techniques, SHAP and feature analysis

    Panda, Chakradhara / Mishra, Alok Kumar / Dash, Aruna Kumar et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: machine learning

    Physical, data-driven and hybrid approaches to model engine exhaust gas temperatures in operational conditions

    Freier Zugriff
    Coraddu, Andrea / Oneto, Luca / Cipollini, Francesca et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: multitask learning

    Performance evaluation of mode choice models under balanced and imbalanced data assumptions

    Rezaei, Shahrbanoo / Khojandi, Anahita / Haque, Antora Mohsena et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: imbalanced learning , machine learning

    Parameter tuning of EV drivers' charging behavioural model using machine learning techniques

    Fotouhi, Zohreh / Narimani, Hamed / Hashemi, Massoud Reza | Taylor & Francis Verlag | 2023
    Schlagwörter: Deep reinforcement learning (DRL)

    Optimizing sensitivity parameters of automated driving vehicles in an open heterogeneous traffic flow system

    Bouadi, Marouane / Jia, Bin / Jiang, Rui et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: reinforcement learning

    On the potential for recognising of social interaction and social learning in modelling travellers’ change of behaviour under uncertainty

    Sunitiyoso, Yos / Avineri, Erel / Chatterjee, Kiron | Taylor & Francis Verlag | 2011
    Schlagwörter: social learning

    Online longitudinal trajectory planning for connected and autonomous vehicles in mixed traffic flow with deep reinforcement learning approach

    Cheng, Yanqiu / Hu, Xianbiao / Chen, Kuanmin et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: deep Q-learning , reinforcement learning

    Multilevel weather detection based on images: a machine learning approach with histogram of oriented gradient and local binary pattern-based features

    Khan, Md Nasim / Das, Anik / Ahmed, Mohamed M. et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: machine learning

    MODELLING LEARNING AND ADAPTATION IN TRANSPORTATION CONTEXTS

    Arentze, Theo / Timmermans, Harry | Taylor & Francis Verlag | 2005
    Schlagwörter: learning and adaptation

    Modeling lateral movement decisions of powered two wheelers in disordered heterogeneous traffic conditions

    Amrutsamanvar, Rushikesh | Taylor & Francis Verlag | 2022
    Schlagwörter: machine learning

    Modeling freight mode choice using machine learning classifiers: a comparative study using Commodity Flow Survey (CFS) data

    Uddin, Majbah / Anowar, Sabreena / Eluru, Naveen | Taylor & Francis Verlag | 2021
    Schlagwörter: machine learning

    Microscopic modeling of cyclists on off-street paths: a stochastic imitation learning approach

    Mohammed, Hossameldin / Sayed, Tarek / Bigazzi, Alexander | Taylor & Francis Verlag | 2022
    Schlagwörter: machine learning

    Method for automated detection of outliers in crash simulations

    Kracker, David / Dhanasekaran, Revan Kumar / Schumacher, Axel et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: machine learning

    Marine accident learning with Fuzzy Cognitive Maps: a method to model and weight human-related contributing factors into maritime accidents

    Navas de Maya, Beatriz / Kurt, R. E. | Taylor & Francis Verlag | 2022
    Schlagwörter: marine accident learning with fuzzy cognitive maps

    Machine learning techniques to predict reactionary delays and other associated key performance indicators on British railway network

    Taleongpong, Panukorn / Hu, Simon / Jiang, Zhoutong et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: machine learning

    Leveraging autonomous vehicles in mixed-autonomy traffic networks with reinforcement learning-controlled intersections

    Mosharafian, Sahand / Afzali, Shirin / Mohammadpour Velni, Javad | Taylor & Francis Verlag | 2023
    Schlagwörter: Reinforcement learning

    Learning through policy transfer? Reviewing a decade of scholarship for the field of transport

    Freier Zugriff
    Glaser, Meredith / Bertolini, Luca / te Brömmelstroet, Marco et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: policy learning , learning

    Learning electric vehicle driver range anxiety with an initial state of charge-oriented gradient boosting approach

    Song, Yang / Hu, Xianbiao | Taylor & Francis Verlag | 2023
    Schlagwörter: ensemble learning

    Joint learning of video images and physiological signals for lane-changing behavior prediction

    Gao, Jun / Yi, Jiangang / Murphey, Yi Lu | Taylor & Francis Verlag | 2022
    Schlagwörter: deep learning

    Integration of machine learning and statistical models for crash frequency modeling

    Zhou, Dongqin / Gayah, Vikash V. / Wood, Jonathan S. | Taylor & Francis Verlag | 2023
    Schlagwörter: Machine learning

    Integrating data-driven and simulation models to predict traffic state affected by road incidents

    Shafiei, Sajjad / Mihăiţă, Adriana-Simona / Nguyen, Hoang et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: machine learning

    Integrating built environment and parking policy for car commuting reduction: evidence from Beijing

    Wang, Xiaoquan / Yin, Chaoying / Zheng, Changjiang et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: machine learning model

    Integrated driver modelling considering state transition feature for individual adaptation of driver assistance systems

    Raksincharoensak, Pongsathorn / Khaisongkram, Wathanyoo / Nagai, Masao et al. | Taylor & Francis Verlag | 2010
    Schlagwörter: statistical machine learning