| Multistep speed prediction on traffic networks: A deep learning approach considering spatio-temporal dependencies Z Zhang, M Li, X Lin, Y Wang, F He Transportation research part C: emerging technologies 105, 297-322, 2019 | 268* | 2019 |
| Multi-vehicle routing problems with soft time windows: A multi-agent reinforcement learning approach K Zhang, F He, Z Zhang, X Lin, M Li Transportation Research Part C: Emerging Technologies 121, 102861, 2020 | 143 | 2020 |
| Network-wide traffic flow estimation with insufficient volume detection and crowdsourcing data Z Zhang, M Li, X Lin, Y Wang Transportation Research Part C: Emerging Technologies 121, 102870, 2020 | 57 | 2020 |
| A customized deep learning approach to integrate network-scale online traffic data imputation and prediction Z Zhang, X Lin, M Li, Y Wang Transportation Research Part C: Emerging Technologies 132, 103372, 2021 | 54 | 2021 |
| Graph attention temporal convolutional network for traffic speed forecasting on road networks K Zhang, F He, Z Zhang, X Lin, M Li Transportmetrica B: transport dynamics 9 (1), 153-171, 2021 | 45 | 2021 |
| 居住区共享泊位资源优化配置模型及算法 姚恩建， 张正超， 张嘉霖， 薛飞， 罗烨堃 交通运输系统工程与信息 17 (2), 160-167, 2016 | 33* | 2016 |
| A customized deep neural network approach to investigate travel mode choice with interpretable utility information Z Zhang, C Ji, Y Wang, Y Yang Journal of Advanced Transportation 2020, 2020 | 14 | 2020 |
| High‐performance traffic speed forecasting based on spatiotemporal clustering of road segments Z Zhang, F He, X Lin, Y Wang, M Li IET Intelligent Transport Systems, 2021 | 10 | 2021 |
| Clustering Approach for Trajectory Anomaly Detection Z Zhang, M Li, F He, Y Wang CICTP 2020, 113-124, 2020 | 2 | 2020 |
Xi LinUniversity of MichiganVerified email at umich.edu
