论坛网站设计,网站建设亿玛酷信赖,wordpress xss漏洞,开发平台英文2024 KDD#xff08; ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 知识发现和数据挖掘会议#xff09;在2024年8月25日-29日在西班牙巴塞罗那举行。
本文总结了KDD2024有关时空数据(Spatial-temporal) 的相关论文#xff0c;如有疏漏#xff0c;欢迎大…2024 KDD ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 知识发现和数据挖掘会议在2024年8月25日-29日在西班牙巴塞罗那举行。
本文总结了KDD2024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, 生成拥堵预测定价预测气象预测轨迹生成预测异常检测信控优化等
ADS track中有2个session中与时空数据城市计算紧密相关Spatiotemporal Applications 与 Urban Mobility还有一些其余session中有一些做的时空数据任务。 KDD 2024 时空数据(Spatial-temporal) ADS论文总结 Spatiotemporal Applications Transportation Marketplace Rate Forecast Using Signature TransformMARLP: Time-series Forecasting Control for Agricultural Managed Aquifer RechargeDiffusion Model-based Mobile Traffic Generation with Open Data for Network Planning and OptimizationLaDe: The First Comprehensive Last-mile Express Dataset from IndustryUrbanGPT: Spatio-Temporal Large Language ModelsSpatio-Temporal Consistency Enhanced Differential Network for Interpretable Indoor Temperature PredictionWeather Knows What Will Occur: Urban Public Nuisance Events Prediction and Control with Meteorological Assistance Urban Mobility Interpretable Cascading Mixture-of-Experts for Urban Traffic Congestion PredictionTrajRecovery: An Efficient Vehicle Trajectory Recovery Framework based on Urban-Scale Traffic Camera RecordsDuMapNet: An End-to-End Vectorization System for City-Scale Lane-Level Map GenerationAn Offline Meta Black-box Optimization Framework for Adaptive Design of Urban Traffic Light Management SystemsFedGTP: Exploiting Inter-Client Spatial Dependency in Federated Graph-based Traffic PredictionPEMBOT: Pareto-Ensembled Multi-task Boosted Trees 其他 FusionSF: Fuse Heterogeneous Modalities in a Vector Quantized Framework for Robust Solar Power Forecasting 【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
Spatiotemporal Applications
1. Transportation Marketplace Rate Forecast Using Signature Transform
ACM链接https://dl.acm.org/doi/abs/10.1145/3637528.3671637
链接https://arxiv.org/abs/2401.04857
作者Haotian Gu (University of California, Berkeley); Xin Guo (Worldwide Operations Research Science, Amazon.com Inc., University of California, Berkeley); Timothy L. Jacobs (Worldwide Operations Research Science, Amazon.com Inc.); Philip Kaminsky (Worldwide Operations Research Science, Amazon.com Inc., University of California, Berkeley); Xinyu Li (University of California, Berkeley)
关键词运价预测 2. MARLP: Time-series Forecasting Control for Agricultural Managed Aquifer Recharge
ACM链接https://dl.acm.org/doi/abs/10.1145/3637528.3671533
链接https://arxiv.org/abs/2407.01005
作者Yuning Chen (University of California, Merced); Kang Yang (University of California, Merced); Zhiyu An (University of California, Merced); Brady Holder (University of California, Agriculture and Natural Resources); Luke Paloutzian (University of California, Agriculture and Natural Resources); Khaled M. Bali (University of California, Agriculture and Natural Resources); Wan Du (University of California, Merced)
关键词时序预测因果学习模型预测控制 3. Diffusion Model-based Mobile Traffic Generation with Open Data for Network Planning and Optimization
ACM链接https://dl.acm.org/doi/abs/10.1145/3637528.3671544
作者Haoye Chai (Department of Electronic Engineering, BNRist, Tsinghua University); Tao Jiang (Research Center of 6G Mobile Communications, School of Cyber Science and Engineering, Huazhong University of Science and Technology); Li Yu (Chinamobile Research Institute)
关键词交通数据生成扩散模型卫星图像 4. LaDe: The First Comprehensive Last-mile Express Dataset from Industry
ACM链接https://dl.acm.org/doi/abs/10.1145/3637528.3671548
链接https://arxiv.org/abs/2306.10675
代码https://github.com/wenhaomin/LaDe
作者Lixia Wu (Cainiao Network); Haomin Wen (School of Computer and Information Technology, Beijing Jiaotong University, Cainiao Network); Haoyuan Hu (Cainiao Network); Xiaowei Mao (School of Computer and Information Technology, Beijing Jiaotong University, Cainiao Network); Yutong Xia (National University of Singapore); Ergang Shan (Cainiao Network); Jianbin Zheng (Artificial Intelligence Department, Cainiao Network); Junhong Lou (Cainiao Network); Yuxuan Liang (Hong Kong University of Science and Technology (Guangzhou)); Liuqing Yang (Hong Kong University of Science and Technology (Guangzhou)); Roger Zimmermann (National University of Singapore); Youfang Lin (School of Computer and Information Technology, Beijing Jiaotong Univercity); Huaiyu Wan (School of Computer and Information Technology, Beijing Jiaotong University)
关键词物流数据集最后一公里配送 5. UrbanGPT: Spatio-Temporal Large Language Models
ACM链接https://dl.acm.org/doi/abs/10.1145/3637528.3671578
链接https://arxiv.org/abs/2403.00813
代码https://github.com/HKUDS/UrbanGPT
作者Zhonghang Li (South China University of Technology, The University of Hong Kong); Lianghao Xia (The University of Hong Kong); Jiabin Tang (The University of Hong Kong); Yong Xu (South China University of Technology); Lei Shi (Baidu Inc.); Long Xia (Baidu Inc.); Dawei Yin (Baidu Inc.); Chao Huang (The University of Hong Kong)
关键词交通预测大模型
备注没有部署的ADS 6. Spatio-Temporal Consistency Enhanced Differential Network for Interpretable Indoor Temperature Prediction
ACM链接https://dl.acm.org/doi/abs/10.1145/3637528.3671608
作者Dekang Qi (Southwest Jiaotong University, JD iCity, JD Technology); Xiuwen Yi (JD iCity, JD Technology, JD Intelligent Cities Research); Chengjie Guo (Xidian University); Yanyong Huang (Southwestern University of Finance and Economics); Junbo Zhang (JD iCity, JD Technology, JD Intelligent Cities Research); Tianrui Li (Southwest Jiaotong University); Yu Zheng (JD iCity, JD Technology, JD Intelligent Cities Research)
关键词室内温度预测可解释性预测时空一致性 7. Weather Knows What Will Occur: Urban Public Nuisance Events Prediction and Control with Meteorological Assistance
ACM链接https://dl.acm.org/doi/abs/10.1145/3637528.3671639
作者Yi Xie (Fudan University); Tianyu Qiu (Fudan University); Yun Xiong (Fudan University); Xiuqi Huang (Shanghai Jiaotong University); Xiaofeng Gao (Shanghai Jiao Tong University); Chao Chen (Sorbonne Université – Faculté des Sciences (Paris VI)); Qiang Wang (Shanghai Center for Meteorological Disaster Prevention Technology); Haihong Li (Shanghai Center for Meteorological Disaster Prevention Technology)
关键词气象辅助的城市事件预测 Urban Mobility
8. Interpretable Cascading Mixture-of-Experts for Urban Traffic Congestion Prediction
链接https://arxiv.org/abs/2406.12923
ACM链接https://dl.acm.org/doi/abs/10.1145/3637528.3671507
作者Wenzhao Jiang (The Hong Kong University of Science and Technology (Guangzhou)); Jindong Han (The Hong Kong University of Science and Technology); Hao Liu (The Hong Kong University of Science and Technology (Guangzhou), The Hong Kong University of Science and Technology); Tao Tao (Didichuxing Co. Ltd); Naiqiang Tan (Didichuxing Co. Ltd); Hui Xiong (The Hong Kong University of Science and Technology (Guangzhou), The Hong Kong University of Science and Technology)
关键词拥堵预测混合专家系统 9. TrajRecovery: An Efficient Vehicle Trajectory Recovery Framework based on Urban-Scale Traffic Camera Records
ACM链接https://dl.acm.org/doi/abs/10.1145/3637528.3671558
作者Dongen Wu (Zhejiang University); Ziquan Fang (Zhejiang University); Qichen Sun (Zhejiang University); Lu Chen (Zhejiang University); Haiyang Hu (Zhejiang University); Fei Wang (Zhejiang University); Yunjun Gao (Zhejiang University)
关键词轨迹恢复 10. DuMapNet: An End-to-End Vectorization System for City-Scale Lane-Level Map Generation
ACM链接https://dl.acm.org/doi/abs/10.1145/3637528.3671579
链接https://arxiv.org/abs/2406.14255
代码https://github.com/XiyanLiu/DuMapNet
作者Deguo Xia (Tsinghua University, Baidu Inc.); Weiming Zhang (Baidu Inc.); Xiyan Liu (Baidu Inc.); Wei Zhang (Baidu Inc.); Chenting Gong (Baidu Inc.); Jizhou Huang (Baidu Inc.); Mengmeng Yang (Tsinghua University); Diange Yang (Tsinghua University)
关键词城市车道级别地图生成 11. An Offline Meta Black-box Optimization Framework for Adaptive Design of Urban Traffic Light Management Systems
ACM链接https://dl.acm.org/doi/abs/10.1145/3637528.3671606
链接https://arxiv.org/abs/2408.07327
作者Taeyoung Yun (KAIST); Kanghoon Lee (KAIST); Sujin Yun (KAIST); Ilmyung Kim (Korea Telecom); Won-Woo Jung (Korea Telecom); Min-Cheol Kwon (Korea Telecom); Kyujin Choi (Korea Telecom); Yoohyeon Lee (Korea Telecom); Jinkyoo Park (KAIST)
关键词交通灯元学习黑盒优化 12. FedGTP: Exploiting Inter-Client Spatial Dependency in Federated Graph-based Traffic Prediction
链接https://zhouzimu.github.io/paper/kdd24-yang.pdf
ACM链接https://dl.acm.org/doi/abs/10.1145/3637528.3671613
代码https://github.com/LarryHawkingYoung/KDD2024_FedGTP
作者Linghua Yang (SKLCCSE Lab, Beihang University); Wantong Chen (SKLCCSE Lab, Beihang University); Xiaoxi He (Faculty of Science and Technology, University of Macau); Shuyue Wei (SKLCCSE Lab, Beihang University); Yi Xu (SKLCCSE Lab, Institute of Artificial Intelligence, Beihang University); Zimu Zhou (School of Data Science, City University of Hong Kong); Yongxin Tong (SKLCCSE Lab, Beihang University)
关键词交通预测联邦学习 13. PEMBOT: Pareto-Ensembled Multi-task Boosted Trees
链接https://www.amazon.science/publications/pembot-pareto-ensembled-multi-task-boosted-trees
ACM链接https://dl.acm.org/doi/abs/10.1145/3637528.3671619
作者Gokul Swamy (International Machine Learning, Amazon); Anoop Saladi (International Machine Learning, Amazon); Arunita Das (International Machine Learning, Amazon); Shobhit Niranjan (International Machine Learning, Amazon)
关键词帕累托最优多任务 其他
14. FusionSF: Fuse Heterogeneous Modalities in a Vector Quantized Framework for Robust Solar Power Forecasting
链接https://arxiv.org/abs/2402.05823
ACM链接https://dl.acm.org/doi/abs/10.1145/3637528.3671509
作者Ziqing Ma (DAMO Academy, Alibaba Group); Wenwei Wang (DAMO Academy, Alibaba Group); Tian Zhou (DAMO Academy, Alibaba Group); Chao Chen (DAMO Academy, Central South University); Bingqing Peng (DAMO Academy, Alibaba Group); Liang Sun (DAMO Academy, Alibaba Group); Rong Jin (DAMO Academy, Alibaba Group)
关键词太阳能预测模态聚合向量量化零样本 相关链接
; Bingqing Peng (DAMO Academy, Alibaba Group); Liang Sun (DAMO Academy, Alibaba Group); Rong Jin (DAMO Academy, Alibaba Group)
关键词太阳能预测模态聚合向量量化零样本
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相关链接
KDD 2024 Applied Data Science Paperhttps://kdd2024.kdd.org/applied-data-science-track-papers/
【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
