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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

    Machine learning algorithms in ship design optimization

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

    Modeling the impact of COVID-19 on transportation at later stage of the pandemic: A case study of Utah

    Gong, Yaobang / Isom, Tanner / Lu, Pan et al. | Taylor & Francis Verlag | 2024
    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

    Learning Drivers’ Behavior to Improve Adaptive Cruise Control

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

    Sensitivity analysis of driving event classification using smartphone motion data: case of classifier type, sensor bundling, and data acquisition rate

    Sarteshnizi, Iman Taheri / Tavakkoli Khomeini, Farbod / Khedri, Borna et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: machine 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

    Travel mode choice: a data fusion model using machine learning methods and evidence from travel diary survey data

    Chang, Ximing / Wu, Jianjun / Liu, Hao et al. | Taylor & Francis Verlag | 2019
    Schlagwörter: machine 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

    Investigating an SVM-driven, one-class approach to estimating ship systems condition

    Lazakis, Iraklis / Gkerekos, Christos / Theotokatos, Gerasimos | Taylor & Francis Verlag | 2019
    Schlagwörter: machine learning

    Crash classification based on manner of collision: a comparative analysis

    Mahmud, Asif / Sengupta, Agnimitra / Gayah, Vikash V. | Taylor & Francis Verlag | 2024
    Schlagwörter: machine learning

    A data-driven dynamics simulation framework for railway vehicles

    Nie, Yinyu / Tang, Zhao / Liu, Fengjia et al. | Taylor & Francis Verlag | 2018
    Schlagwörter: machine learning

    Equality of public transit connectivity: the influence of mass rapid transit services on individual buildings for Singapore

    Li, Zengxiang / Ren, Shen / Hu, Nan et al. | Taylor & Francis Verlag | 2019
    Schlagwörter: machine learning

    Small-amplitude bogie hunting identification method for high-speed trains based on machine learning

    Guo, Jinying / Zhang, Gexiang / Shi, Huailong et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: machine learning

    Friction-adaptive stochastic nonlinear model predictive control for autonomous vehicles

    Vaskov, Sean / Quirynen, Rien / Menner, Marcel et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: machine learning

    A machine learning based optimisation method to evaluate the crushing behaviours of square tubes with rectangular-hole-type initiators

    Liang, Rui / Xu, Fengxiang / Liu, Na et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: Machine learning

    In-store shopping trip predictions and impact factors during COVID-19 emergencies

    Imran, Md Ashraful / Hyun, Kate | Taylor & Francis Verlag | 2024
    Schlagwörter: machine learning

    Analyzing multi-factor effects on travel well-being, including non-linear relationship and interaction

    Yu, Hongmei / Ye, Xiaofei / Liu, Lining et al. | Taylor & Francis Verlag | 2024
    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

    Multimode trip information detection using personal trajectory data

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

    A behavioral analysis of freight mode choice decisions

    Samimi, Amir / Kawamura, Kazuya / Mohammadian, Abolfazl | Taylor & Francis Verlag | 2011
    Schlagwörter: machine learning

    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

    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

    Research on the air brake system of heavy haul trains based on neural network

    Zhou, Xinyi / Cheng, Shu / Yu, Tianjian et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: machine learning

    A novel algorithm for ship characteristic points extraction based on density clustering

    Li, Weifeng / Zhang, Haoda / Shi, Guoyou et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: machine learning

    Household travel mode choice estimation with large-scale data—an empirical analysis based on mobility data in Milan

    Liang, Leilei / Xu, Meng / Grant-Muller, Susan et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: machine learning

    Data-driven train set crash dynamics simulation

    Tang, Zhao / Zhu, Yunrui / Nie, Yinyu et al. | Taylor & Francis Verlag | 2017
    Schlagwörter: machine learning

    Leveraging machine learning to predict rail corrugation level from axle-box acceleration measurements on commercial vehicles

    Hassanieh, Wael / Chehade, Abdallah / Facchinetti, Alan et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: machine 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 passenger types from commuter eigentravel matrices

    Legara, Erika Fille T. / Monterola, Christopher P. | Taylor & Francis Verlag | 2018
    Schlagwörter: machine learning

    Comparative study of statistical and machine learning methods for streetcar incident duration analysis

    Zhu, Siying | Taylor & Francis Verlag | 2024
    Schlagwörter: statistical and machine learning methods

    A residential location search model based on the reasons for moving out

    Orvin, Muntahith Mehadil / Fatmi, Mahmudur Rahman | Taylor & Francis Verlag | 2024
    Schlagwörter: machine learning

    Application of Macro Element Method (MEM) for faster automotive crash safety design during concept stage

    Narayana, Peddi Sai Rama / Prakash, Raghu V. / Gunti, Srinivas et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: machine 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

    Simulation-driven design of a fast monohull

    Freier Zugriff
    Harries, Stefan / Ahmed, Osama / Uharek, Sebastian | Taylor & Francis Verlag | 2024
    Schlagwörter: machine learning (ML)

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