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1–50 von 120 Ergebnissen
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    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

    GPS-based citywide traffic congestion forecasting using CNN-RNN and C3D hybrid model

    Guo, Jingqiu / Liu, Yangzexi / Yang, Qingyan (Ken) et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: deep 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

    Few-Shot traffic prediction based on transferring prior knowledge from local network

    Yu, Lin / Guo, Fangce / Sivakumar, Aruna et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: Few-shot learning , Transfer learning

    Deep reinforcement learning in dynamic positioning control: by rewarding small response of riser angles

    Wang, Fang / Bai, Yong / Bai, Jie et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: Reinforcement learning , Q-learning

    Deep Q learning-based traffic signal control algorithms: Model development and evaluation with field data

    Wang, Hao / Yuan, Yun / Yang, Xianfeng Terry et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: deep reinforcement learning , Q-learning

    A cold-start-free reinforcement learning approach for traffic signal control

    Xiao, Nan / Yu, Liang / Yu, Jinqiang et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: deep learning , reinforcement 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

    Detecting transportation modes using smartphone data and GIS information: evaluating alternative algorithms for an integrated smartphone-based travel diary imputation

    Liu, Yicong / Miller, Eric / Habib, Khandker Nurul | Taylor & Francis Verlag | 2022
    Schlagwörter: machine learning , tree-based ensemble learning

    Shipping market forecasting by forecast combination mechanism

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

    A data-driven lane-changing behavior detection system based on sequence learning

    Gao, Jun / Murphey, Yi Lu / Yi, Jiangang et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: sequence 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

    A bibliometric analysis and review on reinforcement learning for transportation applications

    Li, Can / Bai, Lei / Yao, Lina et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: Machine learning

    A data-driven approach to characterize the impact of connected and autonomous vehicles on traffic flow

    Parsa, Amir Bahador / Shabanpour, Ramin / Mohammadian, Abolfazl (Kouros) et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: machine learning

    Convolutional neural network for detecting railway fastener defects using a developed 3D laser system

    Zhan, You / Dai, Xianxing / Yang, Enhui et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: deep 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

    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

    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

    Condition assessment of high-speed railway track structure based on sparse Bayesian extreme learning machine and Bayesian hypothesis testing

    Wang, Senrong / Gao, Jingze / Lin, Chao et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: sparse bayesian learning , extreme learning machine

    Calibrating microscopic traffic simulators using machine learning and particle swarm optimization

    Liu, Yanchen / Zou, Bo / Ni, Anning et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: machine learning

    An evacuation guidance model for pedestrians with limited vision

    Han, Yanbin / Liu, Hong / Li, Liang | Taylor & Francis Verlag | 2023
    Schlagwörter: reinforcement learning

    Graph attention temporal convolutional network for traffic speed forecasting on road networks

    Zhang, Ke / He, Fang / Zhang, Zhengchao et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: deep learning

    Application of machine learning algorithms in lane-changing model for intelligent vehicles exiting to off-ramp

    Dong, Changyin / Wang, Hao / Li, Ye et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: machine learning

    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

    Eco-driving at signalized intersections: a parameterized reinforcement learning approach

    Jiang, Xia / Zhang, Jian / Li, Dan | Taylor & Francis Verlag | 2023
    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

    Application of mayfly algorithm for prediction of removed sediment in hydro-suction dredging systems

    Mahdavi-Meymand, Amin / Zounemat-Kermani, Mohammad | Taylor & Francis Verlag | 2023
    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

    Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2021

    Schüppstuhl, Thorsten | Katalog Medizin | 2022
    Schlagwörter: Machine 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

    Dynamic modelling and control of a twin-rotor system using adaptive neuro-fuzzy inference system techniques

    Omar, M / Zaidan, M A / Tokhi, M O | SAGE Publications | 2012
    Schlagwörter: online 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

    Oppositional particle swarm optimization algorithm and its application to fault monitor

    Ma, Haiping / Lin, Shengdong / Jin, Baogen | Tema Archiv | 2009
    Schlagwörter: Lernen

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

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

    Consumer learning behavior in choosing electric motorcycles

    Sung, Yen-Ching | Taylor & Francis Verlag | 2010
    Schlagwörter: Bayesian learning

    Designing a lightweight 1D convolutional neural network with Bayesian optimization for wheel flat detection using carbody accelerations

    Shi, Dachuan / Ye, Yunguang / Gillwald, Marco et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: machine learning

    Cruise dynamic pricing based on SARSA algorithm

    Wang, Jing / Yang, Dong / Chen, Kaimin et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: reinforcement 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

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

    Vadhwani, Diya / Thakor, Devendra | Taylor & Francis Verlag | 2023
    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

    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

    Framework for development of the Scheduler for Activities, Locations, and Travel (SALT) model

    Hesam Hafezi, Mohammad / Sultana Daisy, Naznin / Millward, Hugh et al. | Taylor & Francis Verlag | 2022
    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

    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

    DRL-based adaptive signal control for bus priority service under connected vehicle environment

    Zhang, Xinshao / He, Zhaocheng / Zhu, Yiting et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: deep reinforcement learning

    Incorporating congestion patterns into spatio-temporal deep learning algorithms

    Leiser, Neil / Yildirimoglu, Mehmet | Taylor & Francis Verlag | 2021
    Schlagwörter: machine learning

    An interpretable machine learning framework to understand bikeshare demand before and during the COVID-19 pandemic in New York City

    Uddin, Majbah / Hwang, Ho-Ling / Hasnine, Md Sami | Taylor & Francis Verlag | 2023
    Schlagwörter: machine learning

    An autonomous location prediction model for maritime transport applications: a case study of Persian Gulf

    Khalilabadi, Mohammad Reza | Taylor & Francis Verlag | 2023
    Schlagwörter: Machine learning

    Assessing influential factors for lane change behavior using full real-world vehicle-by-vehicle data

    Basso, Franco / Cifuentes, Álvaro / Cuevas-Pavincich, Francisca et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: Interpretable Machine Learning