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HMDRL: Hierarchical Mixed Deep Reinforcement Learning to Balance Vehicle Supply and Demand 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 页码: 12
作者:  Xi, Jinhao;  Zhu, Fenghua;  Ye, Peijun;  Lv, Yisheng;  Tang, Haina;  Wang, Fei-Yue
Adobe PDF(3316Kb)  |  收藏  |  浏览/下载：247/30  |  提交时间：2022/09/19
deep reinforcement learning  online ride-hailing system  hierarchical repositioning framework  parallel coordination mechanism  mixed state  
A Multi-Stream Feature Fusion Approach for Traffic Prediction 期刊论文
IEEE Transactions on Intelligent Transportation Systems, 2022, 卷号: 23, 期号: 2, 页码: 1456-1466
作者:  Zhishuai Li;  Gang Xiong;  Yonglin Tian;  Yisheng Lv;  Yuanyuan Chen;  Pan Hui;  Xiang Su
Adobe PDF(3248Kb)  |  收藏  |  浏览/下载：362/63  |  提交时间：2020/10/15
Traffic prediction, graph convolutional neural network, deep learning, multi-stream  
AdapGL: An adaptive graph learning algorithm for traffic prediction based on spatiotemporal neural networks 期刊论文
Transportation Research Part C, 2022, 期号: 99, 页码: 1-1
作者:  Wei Zhang;  Fenghua Zhu;  Yisheng Lv;  Chang Tan;  Wen Liu;  Xin Zhang;  Fei-Yue Wang
Adobe PDF(2619Kb)  |  收藏  |  浏览/下载：355/116  |  提交时间：2022/04/08
Adaptive graph learning, Traffic prediction, Graph convolutional network, Expectation maximization, Deep learning  
MLRNN: Taxi Demand Prediction Based on Multi-Level Deep Learning and Regional Heterogeneity Analysis 期刊论文
IEEE Transactions on Intelligent Transportation Systems, 2021, 卷号: 0, 期号: 0, 页码: 0
作者:  Chizhan Zhang;  Fenghua Zhu;  Yisheng Lv;  Peijun Ye;  Feiyue Wang
Adobe PDF(4431Kb)  |  收藏  |  浏览/下载：222/50  |  提交时间：2021/06/16
Taxi demand prediction  taxi zone clustering  heterogeneity analysis  deep learning  
Taxi Demand Prediction Using Parallel Multi-Task Learning Model 期刊论文
IEEE Transactions on Intelligent Transportation Systems, 2020, 卷号: 99, 期号: 1, 页码: 1-10
作者:  Chizhan Zhang;  Fenghua Zhu;  Xiao Wang;  Leilei Sun;  Haina Tang;  Yisheng Lv
浏览  |  Adobe PDF(3947Kb)  |  收藏  |  浏览/下载：200/81  |  提交时间：2020/10/15
Taxi demand prediction, pick-up/drop-off demand, multi-task learning, LSTM, deep learning