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1–50 von 184 Ergebnissen
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    Traffic speed prediction for intelligent transportation system based on a deep feature fusion model

    Li, Linchao / Qu, Xu / Zhang, Jian et al. | Taylor & Francis Verlag | 2019
    Schlagwörter: deep learning , machine learning

    Design of Reinforcement Learning Parameters for Seamless Application of Adaptive Traffic Signal Control

    El-Tantawy, Samah / Abdulhai, Baher / Abdelgawad, Hossam | Taylor & Francis Verlag | 2014
    Schlagwörter: Reinforcement Learning , Temporal Difference 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

    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

    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

    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

    Artificial intelligence for traffic signal control based solely on video images

    Jeon, Hyunjeong / Lee, Jincheol / Sohn, Keemin | Taylor & Francis Verlag | 2018
    Schlagwörter: deep learning , reinforcement learning (RL)

    The construction of a neural network proxy model for ship hull design based on multi-fidelity datasets and the parameter freezing strategy

    Ao, Yu / Li, Shaofan / Li, Yunbo et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: Deep learning , machine 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

    Trajectory tracking for autonomous vehicles on varying road surfaces by friction-adaptive nonlinear model predictive control

    Berntorp, K. / Quirynen, R. / Uno, T. et al. | Taylor & Francis Verlag | 2020
    Schlagwörter: parameter learning

    Shipping market forecasting by forecast combination mechanism

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

    Towards multi-agent reinforcement learning for integrated network of optimal traffic controllers (MARLIN-OTC)

    El-Tantawy, Samah / Abdulhai, Baher | Taylor & Francis Verlag | 2010
    Schlagwörter: Reinforcement Learning , Multi-Agent Reinforcement 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

    Machine learning algorithms in ship design optimization

    Peri, Daniele | Taylor & Francis Verlag | 2024
    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

    Network-wide traffic signal control based on the discovery of critical nodes and deep reinforcement learning

    Xu, Ming / Wu, Jianping / Huang, Ling et al. | Taylor & Francis Verlag | 2020
    Schlagwörter: deep reinforcement 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

    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

    A robust machine learning structure for driving events recognition using smartphone motion sensors

    Zarei Yazd, Mahdi / Taheri Sarteshnizi, Iman / Samimi, Amir et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: machine learning

    Understanding e-bicycle overtaking strategy: insights from inverse reinforcement learning modelling

    Yue, Lishengsa / Abdel-Aty, Mohamed / Zaki, Mohamed H. et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: inverse reinforcement 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

    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

    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

    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

    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

    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

    Learning Drivers’ Behavior to Improve Adaptive Cruise Control

    Rosenfeld, Avi / Bareket, Zevi / Goldman, Claudia V. et al. | Taylor & Francis Verlag | 2015
    Schlagwörter: Machine 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

    Alighting stop determination using two-step algorithms in bus transit systems

    Yan, Fenfan / Yang, Chao / Ukkusuri, Satish V. | Taylor & Francis Verlag | 2019
    Schlagwörter: machine learning

    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

    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

    A unified framework of proactive self-learning dynamic pricing for high-occupancy/toll lanes

    Lou, Yingyan | Taylor & Francis Verlag | 2013
    Schlagwörter: self-learning

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

    Jiang, Xia / Zhang, Jian / Li, Dan | Taylor & Francis Verlag | 2023
    Schlagwörter: reinforcement learning

    Efficient Exploitation of Existing Corporate Knowledge in Conceptual Ship Design

    Erikstad, Stein Ove / NTNU | Taylor & Francis Verlag | 2007
    Schlagwörter: 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

    STD-Yolov5: a ship-type detection model based on improved Yolov5

    Ning, Yue / Zhao, Lining / Zhang, Can et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: deep 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

    Traffic congestion forecasting using multilayered deep neural network

    Kumar, Kranti / Kumar, Manoj / Das, Pritikana | Taylor & Francis Verlag | 2024
    Schlagwörter: Deep 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

    An automatic methodology to measure drivers’ behavior in public transport

    Catalán, Hernán / Lobel, Hans / Herrera, Juan Carlos | Taylor & Francis Verlag | 2024
    Schlagwörter: machine learning

    Forecasting road traffic conditions using a context-based random forest algorithm

    Evans, Jonny / Waterson, Ben / Hamilton, Andrew | Taylor & Francis Verlag | 2019
    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

    Asynchronous n-step Q-learning adaptive traffic signal control

    Genders, Wade / Razavi, Saiedeh | Taylor & Francis Verlag | 2019
    Schlagwörter: reinforcement learning