Fetching the paper…
Reading the bibliography…
Accurate traffic forecasting is crucial for effective urban planning and transportation management, enabling efficient resource allocation and enhanced travel experiences.
Language Models Are Few-Shot Learners. In NeurIPS . 1877–1901
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, et al · 1901
Earlier work this paper cites.
GLU Variants Improve Transformer
Noam Shazeer. 2020 · 2002
Earlier work this paper cites.
Instance Normalization: The Missing Ingredient for Fast Stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. 2017 · 2017
Earlier work this paper cites.
Attention is all you need. In NeurIPS
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting. In SDM . 777–785
Rose Yu, Yaguang Li, Cyrus Shahabi, Ugur Demiryurek, and Yan Liu. 2017 · 2017
Earlier work this paper cites.
Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In ICLR
Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018 · 2018
Earlier work this paper cites.
Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In IJCAI
Bing Yu, Haoteng Yin, et al · 2018
Earlier work this paper cites.
Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. In AAAI . 922–929
Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. 2019 · 2019
Earlier work this paper cites.
Graph wavenet for deep spatial-temporal graph modeling. In IJCAI
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019 · 2019
Earlier work this paper cites.
Learning from multiple cities: A meta-learning approach for spatial-temporal prediction. In WWW . 2181–2191
Huaxiu Yao, Yiding Liu, Ying Wei, Xianfeng Tang, and Zhenhui Li. 2019 · 2019
Earlier work this paper cites.
Root mean square layer normalization. In NeurIPS
Biao Zhang and Rico Sennrich. 2019 · 2019
Earlier work this paper cites.
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. In NeurIPS . 17804–17815
Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 2020 · 2020
Earlier work this paper cites.
Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting. In AAAI . 914–921
Chao Song, Youfang Lin, Shengnan Guo, and Huaiyu Wan. 2020 · 2020
Earlier work this paper cites.
A survey on modern deep neural network for traffic prediction: Trends, methods and challenges
David Alexander Tedjopurnomo, Zhifeng Bao, Baihua Zheng, Farhana Murtaza Choudhury, and Alex Kai Qin. 2020 · 2020
Earlier work this paper cites.
Traffic Flow Prediction via Spatial Temporal Graph Neural Network. In WWW . 1082–1092
Xiaoyang Wang, Yao Ma, Yiqi Wang, Wei Jin, Xin Wang, Jiliang Tang, Caiyan Jia, and Jian Yu. 2020 · 2020
Earlier work this paper cites.
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks. In KDD . 753–763
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, and Chengqi Zhang. 2020 · 2020
Earlier work this paper cites.
T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction
Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng, and Haifeng Li. 2020 · 2020
Cited alongside, same era.
GMAN: A Graph Multi-Attention Network for Traffic Prediction. In AAAI . 1234–1241
Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, and Jianzhong Qi. 2020 · 2020
Cited alongside, same era.
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In ICLR
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021 · 2021
Cited alongside, same era.
Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting. In KDD . 364–373
Zheng Fang, Qingqing Long, Guojie Song, and Kunqing Xie. 2021 · 2021
Cited alongside, same era.
Dynamic and Multi-Faceted Spatio-Temporal Deep Learning for Traffic Speed Forecasting. In KDD . 547–555
Liangzhe Han, Bowen Du, Leilei Sun, Yanjie Fu, Yisheng Lv, and Hui Xiong. 2021 · 2021
Cited alongside, same era.
PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction
Jiawei Jiang, Chengkai Han, Wayne Xin Zhao, and Jingyuan Wang. 2023 · 2023
Later among the works it cites.
Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
Guangyin Jin, Yuxuan Liang, Yuchen Fang, Zezhi Shao, Jincai Huang, Junbo Zhang, and Yu Zheng. 2023a · 2023
Later among the works it cites.
Multivariate Time Series Forecasting With Dynamic Graph Neural ODEs
Ming Jin, Yu Zheng, Yuan-Fang Li, Siheng Chen, Bin Yang, and Shirui Pan. 2023b · 2023
Later among the works it cites.
Segment anything. In CVPR . 4015–4026
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
Later among the works it cites.
GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks. In NeurIPS
Zhonghang Li, Lianghao Xia, Yong Xu, and Chao Huang. 2023 · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deep learning on traffic prediction: Methods, analysis, and future directions
Xueyan Yin, Genze Wu, Jinze Wei, Yanming Shen, Heng Qi, and Baocai Yin. 2021 · 2021
Cited alongside, same era.
Towards Spatio- Temporal Aware Traffic Time Series Forecasting. In ICDE . 2900–2913
Razvan-Gabriel Cirstea, Bin Yang, Chenjuan Guo, Tung Kieu, and Shirui Pan. 2022 · 2022
Cited alongside, same era.
Masked Autoencoders Are Scalable Vision Learners. In CVPR . 15979–15988
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. 2022 · 2022
Cited alongside, same era.
Selective cross-city transfer learning for traffic prediction via source city region re-weighting. In KDD . 731–741
Yilun Jin, Kai Chen, and Qiang Yang. 2022 · 2022
Cited alongside, same era.
Modeling network-level traffic flow transitions on sparse data. In KDD . 835–845
Xiaoliang Lei, Hao Mei, Bin Shi, and Hua Wei. 2022 · 2022
Cited alongside, same era.
Mining spatio-temporal relations via self-paced graph contrastive learning. In KDD . 936–944
Rongfan Li, Ting Zhong, Xinke Jiang, Goce Trajcevski, Jin Wu, and Fan Zhou. 2022 · 2022
Cited alongside, same era.
MSDR: Multi-Step Dependency Relation Networks for Spatial Temporal Forecasting. In KDD . 1042–1050
Dachuan Liu, Jin Wang, Shuo Shang, and Peng Han. 2022 · 2022
Cited alongside, same era.
LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting. In Advances in Neural Information Processing Systems
Xu Liu, Yutong Xia, Yuxuan Liang, Junfeng Hu, Yiwei Wang, Lei Bai, Chao Huang, Zhenguang Liu, Bryan Hooi, and Roger Zimmermann. 2023 · 2023
Later among the works it cites.
A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. In ICLR
Yuqi Nie, Nam H Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam. 2023 · 2023
Later among the works it cites.
Spatio-temporal hypergraph learning for next POI recommendation. In SIGIR . 403–412
Xiaodong Yan, Tengwei Song, Yifeng Jiao, Jianshan He, Jiaotuan Wang, Ruopeng Li, and Wei Chu. 2023 · 2023
Later among the works it cites.
Automated Spatio-Temporal Graph Contrastive Learning. In WWW . 295–305
Qianru Zhang, Chao Huang, Lianghao Xia, Zheng Wang, Zhonghang Li, and Siuming Yiu. 2023 · 2023
Later among the works it cites.
Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting. ICDE
Yusheng Zhao, Xiao Luo, Wei Ju, Chong Chen, Xian-Sheng Hua, and Ming Zhang. 2023 · 2023
Later among the works it cites.
Open-ti: Open traffic intelligence with augmented language model
Longchao Da, Kuanru Liou, Tiejin Chen, Xuesong Zhou, Xiangyong Luo, Yezhou Yang, and Hua Wei. 2024 · 2024
Closest in time.
Towards Responsible and Reliable Traffic Flow Prediction with Large Language Models
Xusen Guo, Qiming Zhang, Junyue Jiang, Mingxing Peng, Hao, Yang, and Meixin Zhu. 2024 · 2024
Closest in time.
Spatial-Temporal Large Language Model for Traffic Prediction
Chenxi Liu, Sun Yang, Qianxiong Xu, Zhishuai Li, Cheng Long, Ziyue Li, and Rui Zhao. 2024 · 2024
Closest in time.
TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models
Yilong Ren, Yue Chen, Shuai Liu, Boyue Wang, Haiyang Yu, and Zhiyong Cui. 2024 · 2024
Closest in time.
UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction
Yuan Yuan, Jingtao Ding, Jie Feng, Depeng Jin, and Yong Li. 2024 · 2024
Closest in time.