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1–41 von 41 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

    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

    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)

    Machine learning algorithms in ship design optimization

    Peri, Daniele | Taylor & Francis Verlag | 2024
    Schlagwörter: machine 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 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

    Learning Drivers’ Behavior to Improve Adaptive Cruise Control

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

    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

    An automatic methodology to measure drivers’ behavior in public transport

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

    Asynchronous n-step Q-learning adaptive traffic signal control

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

    Using kinematic driving data to detect sleep apnea treatment adherence

    McDonald, Anthony D. / Lee, John D. / Aksan, Nazan S. et al. | Taylor & Francis Verlag | 2017
    Schlagwörter: machine learning

    Learning-based traffic signal control algorithms with neighborhood information sharing: An application for sustainable mobility

    Aziz, H. M. Abdul / Zhu, Feng / Ukkusuri, Satish V. | Taylor & Francis Verlag | 2018
    Schlagwörter: reinforcement learning

    Estimating cycle-level real-time traffic movements at signalized intersections

    Mahmoud, Nada / Abdel-Aty, Mohamed / Cai, Qing et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: machine learning

    Detection and Classification of Vehicles by Measurement of Road-Pavement Vibration and by Means of Supervised Machine Learning

    Stocker, Markus / Silvonen, Paula / Rönkkö, Mauno et al. | Taylor & Francis Verlag | 2016
    Schlagwörter: Machine Learning

    Neural-network-based modelling and analysis for time series prediction of ship motion

    Li, Guoyuan / Kawan, Bikram / Wang, Hao et al. | Taylor & Francis Verlag | 2017
    Schlagwörter: learning strategy

    Electric vehicle charging demand forecasting using deep learning model

    Yi, Zhiyan / Liu, Xiaoyue Cathy / Wei, Ran et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: deep learning

    Arterial corridor travel time prediction under non-recurring conditions

    Shafiei, Sajjad / Wang, Eileen / Grzybowska, Hanna et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: machine learning

    Deep Architecture for Citywide Travel Time Estimation Incorporating Contextual Information

    Tang, Kun / Chen, Shuyan / Khattak, Aemal J. et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: deep learning

    Multimode trip information detection using personal trajectory data

    Yang, Fei / Yao, Zhenxing / Cheng, Yang et al. | Taylor & Francis Verlag | 2016
    Schlagwörter: machine learning algorithm

    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

    Problems Encountered during Implementation of the Backpropagation

    Kim, Daehyon | Taylor & Francis Verlag | 1999
    Schlagwörter: Learning rate , Learning mode

    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

    Driver’s black box: a system for driver risk assessment using machine learning and fuzzy logic

    Yuksel, A. S. / Atmaca, S. | Taylor & Francis Verlag | 2021
    Schlagwörter: machine learning

    Multiagent reinforcement learning for autonomous driving in traffic zones with unsignalized intersections

    Spatharis, Christos / Blekas, Konstantinos | Taylor & Francis Verlag | 2024
    Schlagwörter: multiagent reinforcement learning

    Price incentive strategy for the E-scooter sharing service using deep reinforcement learning

    Yun, Hyunsoo / Kim, Eui-Jin / Ham, Seung Woo et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: Deep reinforcement learning

    The influence of alternative data smoothing prediction techniques on the performance of a two-stage short-term urban travel time prediction framework

    Guo, Fangce / Krishnan, Rajesh / Polak, John | Taylor & Francis Verlag | 2017
    Schlagwörter: machine learning method

    A human-centric machine learning based personalized route choice prediction in navigation systems

    Sun, Bingrong / Gong, Lin / Shim, Jisup et al. | Taylor & Francis Verlag | 2023
    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

    Identifying traffic conditions from non-traffic related sources

    Chamby-Diaz, Jorge C. / Estevam, Rhuam Sena / Bazzan, Ana L. C. | Taylor & Francis Verlag | 2020
    Schlagwörter: machine learning

    Multi-view crowd congestion monitoring system based on an ensemble of convolutional neural network classifiers

    Li, Yan / Sarvi, Majid / Khoshelham, Kourosh et al. | Taylor & Francis Verlag | 2020
    Schlagwörter: ensemble learning

    A data-driven method for flight time estimation based on air traffic pattern identification and prediction

    Yang, Chunwei / Zhang, Junfeng / Gui, Xuhao et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: machine learning

    Inferring safety critical events from vehicle kinematics in naturalistic driving environment: Application of deep learning Algorithms

    Khattak, Zulqarnain H. / Rios-Torres, Jackeline / Fontaine, Michael D. et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: deep learning

    Machine learning based real-time prediction of freeway crash risk using crowdsourced probe vehicle data

    Zhang, Zihe / Nie, Qifan / Liu, Jun et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: machine 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

    Traffic Route Generation and Adaptation Using Case-Based Reasoning

    Whitsitt, Andrew J. / Travis, Larry E. | Taylor & Francis Verlag | 1996
    Schlagwörter: automatic learning