no code implementations • 15 Nov 2023 • Guangyin Jin, Lingbo Liu, Fuxian Li, Jincai Huang
In particular, to fully exploit the periodic information, we also improve the intensity function calculation of the point process with a periodic gated mechanism.
3 code implementations • 9 Oct 2023 • Zezhi Shao, Fei Wang, Yongjun Xu, Wei Wei, Chengqing Yu, Zhao Zhang, Di Yao, Guangyin Jin, Xin Cao, Gao Cong, Christian S. Jensen, Xueqi Cheng
Moreover, based on the proposed BasicTS and rich heterogeneous MTS datasets, we conduct an exhaustive and reproducible performance and efficiency comparison of popular models, providing insights for researchers in selecting and designing MTS forecasting models.
no code implementations • 3 Sep 2023 • Haomin Wen, Youfang Lin, Lixia Wu, Xiaowei Mao, Tianyue Cai, Yunfeng Hou, Shengnan Guo, Yuxuan Liang, Guangyin Jin, Yiji Zhao, Roger Zimmermann, Jieping Ye, Huaiyu Wan
An emerging research area within these services is service Route\&Time Prediction (RTP), which aims to estimate the future service route as well as the arrival time of a given worker.
no code implementations • 27 Jul 2023 • Zezhi Shao, Fei Wang, Zhao Zhang, Yuchen Fang, Guangyin Jin, Yongjun Xu
Then, we propose a novel Hierarchical U-net TransFormer (HUTFormer) to address the issues of long-term traffic forecasting.
no code implementations • 25 Mar 2023 • Guangyin Jin, Yuxuan Liang, Yuchen Fang, Zezhi Shao, Jincai Huang, Junbo Zhang, Yu Zheng
STGNNs enable the extraction of complex spatio-temporal dependencies by integrating graph neural networks (GNNs) and various temporal learning methods.
1 code implementation • 22 Jul 2022 • Guangyin Jin, Fuxian Li, Jinlei Zhang, Mudan Wang, Jincai Huang
To overcome these limitations, we propose an automated dilated spatio-temporal synchronous graph network, named Auto-DSTSGN for traffic prediction.
no code implementations • 10 Feb 2022 • Jinlei Zhang, Hua Li, Lixing Yang, Guangyin Jin, Jianguo Qi, Ziyou Gao
To overcome these limitations, we propose a novel deep learning-based spatiotemporal graph generative adversarial network (STG-GAN) model with higher prediction accuracy, higher efficiency, and lower memory occupancy to predict short-term passenger flows of the URT network.
no code implementations • 19 Aug 2021 • Jinlei Zhang, Feng Chen, Lixing Yang, Wei Ma, Guangyin Jin, Ziyou Gao
This paper focuses on an essential and hard problem to estimate the network-wide link travel time and station waiting time using the automatic fare collection (AFC) data in the URT system, which is beneficial to better understand the system-wide real-time operation state.
no code implementations • 28 May 2021 • Guangyin Jin, Huan Yan, Fuxian Li, Jincai Huang, Yong Li
To address the above problems, a novel graph-based deep learning framework for travel time estimation is proposed in this paper, namely Spatio-Temporal Dual Graph Neural Networks (STDGNN).
1 code implementation • 30 Apr 2021 • Fuxian Li, Jie Feng, Huan Yan, Guangyin Jin, Depeng Jin, Yong Li
Additionally, there is a severe lack of fair comparison among different methods on the same datasets.
Ranked #2 on Traffic Prediction on NE-BJ
no code implementations • 30 Jul 2020 • Guangyin Jin, Zhexu Xi, Hengyu Sha, Yanghe Feng, Jincai Huang
Urban ride-hailing demand prediction is a crucial but challenging task for intelligent transportation system construction.