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Liu, Shan   He, Renqing   Yang, Hai   Auswahl aufheben

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    Recursive logit-based meta-inverse reinforcement learning for driver-preferred route planning

    Zhang, Pujun / Lei, Dazhou / Liu, Shan et al. | Elsevier | 2024
    Schlagwörter: Inverse reinforcement learning , Meta-learning

    Anomalous ride-hailing driver detection with deep transfer inverse reinforcement learning

    Liu, Shan / Wang, Zhengli / Zhang, Ya et al. | Elsevier | 2023
    Schlagwörter: Inverse reinforcement learning , Transfer learning

    Region-Aware Hierarchical Graph Contrastive Learning for Ride-Hailing Driver Profiling

    Chen, Kehua / Han, Jindong / Feng, Siyuan et al. | Elsevier | 2023
    Schlagwörter: Representation learning , Contrastive learning

    Learning two-dimensional merging behaviour from vehicle trajectories with imitation learning

    Sun, Jie / Yang, Hai | Elsevier | 2024
    Schlagwörter: Imitation learning , Adversarial inverse reinforcement learning

    AdaBoost-Bagging deep inverse reinforcement learning for autonomous taxi cruising route and speed planning

    Liu, Shan / Zhang, Ya / Wang, Zhengli et al. | Elsevier | 2023
    Schlagwörter: Inverse reinforcement learning , Ensemble learning

    Integrating probabilistic tensor factorization with Bayesian supervised learning for dynamic ridesharing pattern analysis

    Zhu, Zheng / Sun, Lijun / Chen, Xiqun et al. | Elsevier | 2020
    Schlagwörter: Supervised learning

    Joint predictions of multi-modal ride-hailing demands: A deep multi-task multi-graph learning-based approach

    Ke, Jintao / Feng, Siyuan / Zhu, Zheng et al. | Elsevier | 2021
    Schlagwörter: Deep multi-task learning

    Semantic-fused multi-granularity cross-city traffic prediction

    Chen, Kehua / Liang, Yuxuan / Han, Jindong et al. | Elsevier | 2024
    Schlagwörter: Transfer learning

    Integrating Dijkstra’s algorithm into deep inverse reinforcement learning for food delivery route planning

    Liu, Shan / Jiang, Hai / Chen, Shuiping et al. | Elsevier | 2020
    Schlagwörter: Inverse reinforcement learning

    Physics of day-to-day network flow dynamics

    Xiao, Feng / Yang, Hai / Ye, Hongbo | Elsevier | 2016
    Schlagwörter: User learning

    Designing van-based mobile battery swapping and rebalancing services for dockless ebike-sharing systems based on the dueling double deep Q-network

    Xu, Meng / Di, Yining / Zhu, Zheng et al. | Elsevier | 2022
    Schlagwörter: Reinforcement learning

    Personalized route recommendation for ride-hailing with deep inverse reinforcement learning and real-time traffic conditions

    Liu, Shan / Jiang, Hai | Elsevier | 2022
    Schlagwörter: Inverse reinforcement learning

    Coordinating ride-sourcing and public transport services with a reinforcement learning approach

    Feng, Siyuan / Duan, Peibo / Ke, Jintao et al. | Elsevier | 2022
    Schlagwörter: Reinforcement learning

    Day-to-day dynamics with advanced traveler information

    Ye, Hongbo / Xiao, Feng / Yang, Hai | Elsevier | 2020
    Schlagwörter: Traveler learning and prediction

    A Bayesian clustering ensemble Gaussian process model for network-wide traffic flow clustering and prediction

    Zhu, Zheng / Xu, Meng / Ke, Jintao et al. | Elsevier | 2023
    Schlagwörter: Statistical learning

    Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network

    Ke, Jintao / Qin, Xiaoran / Yang, Hai et al. | Elsevier | 2020
    Schlagwörter: Deep learning model

    PCA-based missing information imputation for real-time crash likelihood prediction under imbalanced data

    Ke, Jintao / Zhang, Shuaichao / Yang, Hai et al. | Taylor & Francis Verlag | 2019
    Schlagwörter: cost-sensitive learning

    Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach

    Ke, Jintao / Zheng, Hongyu / Yang, Hai et al. | Elsevier | 2017
    Schlagwörter: Deep learning (DL)

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