2024 IJCAI（International Joint Conference on Artificial Intelligence, 国际人工智能联合会议）在2024年8月3日-9日在韩国济州岛举行。

本文总结了IJCAI2024有关时空数据(Spatial-temporal) 的相关论文，如有疏漏，欢迎大家补充。

时空数据Topic：时空（交通）预测，气象预测，轨迹表示学习，轨迹恢复，信控优化，POI等

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1. Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting

链接：https://arxiv.org/abs/2312.00516

代码：https://github.com/Jimmy-7664/STD-MAE

作者：Haotian Gao, Renhe Jiang, Zheng Dong, Jinliang Deng, Yuxin Ma, Xuan Song

机构：东京大学，南方科技大学，悉尼大学

关键词：时空预测，自监督预训练，时空解耦，掩码自编码器，异质性

2. Multi-Modality Spatio-Temporal Forecasting via Self-Supervised Learning

链接：https://arxiv.org/abs/2405.03255

代码：https://github.com/beginner-sketch/MoSSL

作者：Jiewen Deng, Renhe Jiang, Jiaqi Zhang, Xuan Song

机构：南方科技大学，东京大学，吉林大学

关键词：多模态，时空栅格预测，自监督

3. SaSDim:Self-Adaptive Noise Scaling Diffusion Model for Spatial Time Series Imputation

链接：https://arxiv.org/abs/2309.01988

作者：Shunyang Zhang, Senzhang Wang, Xianzhen Tan, Renzhi Wang, Ruochen Liu, Jian Zhang, Jianxin Wang

机构：中南大学

关键词：时空插补，扩散模型

4. WeatherGNN: Exploiting Complicated Relationships in Numerical Weather Prediction Bias Correction

链接：https://arxiv.org/abs/2310.05517

作者：Binqing Wu, Weiqi Chen, Wenwei Wang, Bingqing Peng, Liang Sun, Ling Chen

机构：阿里巴巴达摩院，浙江大学

关键词：气象预测，空间依赖性，NWP

5. X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer as Meta Multi-Agent Reinforcement Learner

链接：https://arxiv.org/abs/2404.12090

代码：https://github.com/AnonymousID-submission/X-Light

作者：Haoyuan Jiang, Ziyue Li, Hua Wei, Xuantang Xiong, Jingqing Ruan, Jiaming Lu, Hangyu Mao, Rui Zhao

机构：百度，科隆大学，亚利桑那州立大学，中科院自动化所，复旦大学，商汤，启元研究院

关键词：信控优化，跨城市可迁移性，元强化学习

6. Full Bayesian Significance Testing for Neural Networks in Traffic Forecasting

作者：Zehua Liu, Jingyuan Wang, Zimeng Li, Yue He

机构：北京航空航天大学，清华大学

关键词：交通预测，贝叶斯网络，显著性检测，不确定性量化

7. Towards Robust Trajectory Representations: Isolating Environmental Confounders with Causal Learning

链接：https://arxiv.org/abs/2404.14073

作者：Kang Luo, Yuanshao Zhu, Wei Chen, Kun Wang, Zhengyang Zhou, Sijie Ruan, Yuxuan Liang

机构：香港科技大学（广州），中国科学技术大学，北京理工大学

关键词：轨迹表示学习，稳健性，因果学习

8. Make Graph Neural Networks Great Again: A Generic Integration Paradigm of Topology-Free Patterns for Traffic Speed Prediction

链接：https://arxiv.org/abs/2406.16992

代码：https://github.com/ibizatomorrow/DCST

作者：Yicheng Zhou, Pengfei Wang, Hao Dong, Denghui Zhang, Dingqi Yang, Yanjie Fu, Pengyang Wang

关键词：交通速度预测，GNN,知识蒸馏

机构：澳门大学，中国科学院，斯蒂文斯理工学院，亚利桑那州立大学

9. Reframing Spatial Reasoning Evaluation in Language Models: A Real-World Simulation Benchmark for Qualitative Reasoning

链接：https://arxiv.org/abs/2405.15064

代码：https://github.com/Fangjun-Li/RoomSpace

作者：Fangjun Li, David Hogg, Anthony Cohn

机构：利兹大学

关键词：空间推理，语言模型

10. Personalized Federated Learning for Cross-city Traffic Prediction

作者：Yu Zhang, Hua Lu, Ning Liu, Yonghui Xu, Qingzhong Li, Lizhen Cui

关键词：交通预测，联邦学习，跨城市

11. Make Bricks with a Little Straw: Large-Scale Spatio-Temporal Graph Learning with Restricted GPU-Memory Capacity

作者：Binwu Wang, Pengkun Wang, Zhengyang Zhou, Zhe Zhao, Wei Xu, Yang Wang

机构：中国科学技术大学

关键词：交通预测，大规模时空图，子图

12. A Graph-based Representation Framework for Trajectory Recovery via Spatiotemporal Interval-Informed Seq2Seq

作者：Yaya Zhao, Kaiqi Zhao, Zhiqian Chen, Yuanyuan Zhang, Yalei Du, Xiaoling Lu

关键词：轨迹恢复，图表示框架

13. Learning Hierarchy-Enhanced POI Category Representations Using Disentangled Mobility Sequences

作者：Hongwei Jia, Meng Chen, Weiming Huang, Kai Zhao, Yongshun Gong

关键词：POI分类，表示学习

14. Exploring Urban Semantics: A Multimodal Model for POI Semantic Annotation with Street View Images and Place Names

作者：Dabin Zhang, Meng Chen, Weiming Huang, Yongshun Gong, Kai Zhao

关键词：POI分类，多模态

15. Counterfactual User Sequence Synthesis Augmented with Continuous Time Dynamic Preference Modeling for Sequential POI Recommendation

作者：Lianyong Qi, Yuwen Liu, Weiming Liu, Shichao Pei, Xiaolong Xu, Xuyun Zhang, Yingjie Wang, Wanchun Dou

关键词：POI推荐，反事实

16. KDDC: Knowledge-Driven Disentangled Causal Metric Learning for Pre-Travel Out-of-Town Recommendation

作者：Yinghui Liu, Guojiang Shen, Chengyong Cui, Zhenzhen Zhao, Xiao Han, Jiaxin Du, Xiangyu Zhao, Xiangjie Kong

关键词：POI推荐，旅行推荐

17. Enhancing Fine-Grained Urban Flow Inference via Incremental Neural Operator

作者：Qiang Gao, Xiaolong Song, Li Huang, Goce Trajcevski, Fan Zhou, Xueqin Chen

17. Enhancing Fine-Grained Urban Flow Inference via Incremental Neural Operator

作者：Qiang Gao, Xiaolong Song, Li Huang, Goce Trajcevski, Fan Zhou, Xueqin Chen

关键词：细粒度城市流量推理，算子学习

🌟【紧跟前沿】“时空探索之旅”与你一起探索时空奥秘！🚀
欢迎大家关注时空探索之旅时空探索之旅