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Traffic Prediction Paper Collection

In the paper collection, we collected traffic prediction papers published in the recent years (2016-2021) on 13 top conferences and journals, namely, AAAI, IJCAI, KDD, CIKM, ICDM, WWW, NIPS, ICLR, ICML, ICDE, SIGSPATIAL, IEEE TKDE and IEEE TITS. In addition, the surveys since 2016 and representative papers mentioned in the surveys are also included. We will continue to update the collection.

Surveys

2021

  1. Short-term Traffic Prediction with Deep Neural Networks: A Survey. Kyungeun Lee; Moonjung Eo; Euna Jung; Yoonjin Yoon; Wonjong Rhee. IEEE Access 2021. link
  2. Graph Neural Network for Traffic Forecasting: A Survey. Weiwei Jiang, Jiayun Luo. arXiv 2021. link
  3. A Survey on Trajectory Data Management, Analytics, and Learning. Sheng Wang, Zhifeng Bao, J. Shane Culpepper, Gao Cong. ACM Computing Surveys 2021. link

2020

  1. Deep Learning on Traffic Prediction: Methods, Analysis and Future Directions. Xueyan Yin, Genze Wu, Jinze Wei, Yanming Shen, Heng Qi, Baocai Yin. IEEE TITS 2020. link
  2. How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey. Jiexia Ye, Juanjuan Zhao, Kejiang Ye, Chengzhong Xu. IEEE TITS 2020. link
  3. A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges. David Alexander Tedjopurnomo, Zhifeng Bao, Baihua Zheng, Farhana Choudhury, AK Qin. IEEE TKDE 2020. link
  4. Urban flow prediction from spatiotemporal data using machine learning: A survey. Peng Xie, Tianrui Li, Jia Liu, Shengdong Du, Xin Yang, Junbo Zhang. Information Fusion 2020. link
  5. Urban big data fusion based on deep learning: An overview. Jia Liu, Tianrui Li, Peng Xie, Shengdong Du, Fei Teng, Xin Yang. Information Fusion 2020. link

2019

  1. Big Data Analytics in Intelligent Transportation Systems: A Survey. Li Zhu, Fei Richard Yu, Yige Wang, Bin Ning, Tao Tang. IEEE TITS 2019. link
  2. Deep Learning for Spatio-Temporal Data Mining: A Survey. Senzhang Wang, Jiannong Cao, and Philip S. Yu. IEEE TKDE 2019. link
  3. A Survey of Hybrid Deep Learning Methods for Traffic Flow Prediction. Yan Shi,Haoran Feng,Xiongfei Geng,Xingui Tang,Yongcai Wang. ACM ICAIP 2019. link

2018

  1. Survey on traffic prediction in smart cities. Attila M Nagy, Vilmos Simon. Pervasive and Mobile Computing 2018. link
  2. A Brief Overview of Machine Learning Methods for Short-term Traffic Forecasting and Future Directions. Yaguang Li, Cyrus Shahabi. SIGSPATIAL 2018. link
  3. Spatio-temporal data mining: A survey of problems and methods. Atluri, Gowtham, Anuj Karpatne, and Vipin Kumar. ACM Computing Surveys 2018. link

AAAI

2024

  1. ModWaveMLP: MLP-Based Mode Decomposition and Wavelet Denoising Model to Defeat Complex Structures in Traffic Forecasting. Ke Sun(Central South University), Pei Liu, Pengfei Li, Zhifang Liao. AAAI 2024. link
  2. Spatio-Temporal Pivotal Graph Neural Networks for Traffic Flow Forecasting. Weiyang Kong(UESTC), Ziyu Guo, Yubao Liu. AAAI 2024. link
  3. Towards Dynamic Spatial-Temporal Graph Learning: A Decoupled Perspective. Binwu Wang(USTC), Pengkun Wang, Yudong Zhang, Xu Wang, Zhengyang Zhou, Lei Bai, Yang Wang. AAAI 2024. link
  4. Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data. Yucheng Wang(NTU), Yuecong Xu, Jianfei Yang, Min Wu, Xiaoli Li, Lihua Xie, Zhenghua Chen. AAAI 2024. link

2023

  1. PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction. Jiawei Jiang(BUAA), Chengkai Han, Wayne Xin Zhao, Jingyuan Wang. AAAI 2023. link
  2. Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction. Jiahao Ji(BUAA), Jingyuan Wang, Chao Huang, Junjie Wu, Boren Xu, Zhenhe Wu, Junbo Zhang, Yu Zheng. AAAI 2023. link
  3. Spatio-Temporal Meta-Graph Learning for Traffic Forecasting. Renhe Jiang(The University of Tokyo), Zhaonan Wang, Jiawei Yong, Puneet Jeph, Quanjun Chen, Yasumasa Kobayashi, Xuan Song, Shintaro Fukushima, Toyotaro Suzumura. AAAI 2023. link
  4. Trafformer: Unify Time and Space in Traffic Prediction. Di Jin(Tianjin University), Jiayi Shi , Rui Wang , Yawen Li, Yuxiao Huang, Yu-Bin Yang. AAAI 2023. link
  5. Learning Decomposed Spatial Relations for Multi-Variate Time-Series Modeling. Yuchen Fang(SJTU), Kan Ren, Caihua Shan, Yifei Shen, You Li, Weinan Zhang, Yong Yu, Dongsheng Li. AAAI 2023. link
  6. Scalable Spatiotemporal Graph Neural Networks. Andrea Cini(Università della Svizzera italiana), Ivan Marisca, Filippo Maria Bianchi, Cesare Alippi. AAAI 2023. link
  7. Spatio-temporal Neural Structural Causal Models for Bike Flow Prediction. Pan Deng(BUAA), Yu Zhao, Junting Liu, Xiaofeng Jia, Mulan Wang. AAAI 2023. link
  8. Causal Conditional Hidden Markov Model for Multimodal Traffic Prediction. Yu Zhao(BUAA), Pan Deng, Junting Liu, Xiaofeng Jia, Mulan Wang. AAAI 2023. link
  9. Generic and Dynamic Graph Representation Learning for Crowd Flow Modeling. Liangzhe Han(BUAA), Ruixing Zhang, Leilei Sun, Bowen Du, Yanjie Fu, Tongyu Zhu. AAAI 2023. link
  10. Easy Begun Is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout. Hongjun Wang(SUST), Jiyuan Chen, Tong Pan, Zipei Fan, Xuan Song, Renhe Jiang, Lingyu Zhang, Yi Xie, Zhongyi Wang, Boyuan Zhang. AAAI 2023. link
  11. AutoSTL: Automated Spatio-Temporal Multi-Task Learning. Zijian Zhang(Jilin University), Xiangyu Zhao, Hao Miao, Chunxu Zhang, Hongwei Zhao, Junbo Zhang. AAAI 2023. link
  12. InParformer: Evolutionary Decomposition Transformers with Interactive Parallel Attention for Long-Term Time Series Forecasting. Haizhou Cao(CAS), Zhenhao Huang, Tiechui Yao, Jue Wang, Hui He, Yangang Wang. AAAI 2023. link
  13. NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. Cristian Challu(CMU), Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski. AAAI 2023. link
  14. Supervised Contrastive Few-Shot Learning for High-Frequency Time Series. Xi Chen(Alibaba Group), Cheng Ge, Ming Wang, Jin Wang. AAAI 2023. link
  15. Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting. Wei Fan(University of Central Florida), Pengyang Wang, Dongkun Wang, Dongjie Wang, Yuanchun Zhou, Yanjie Fu. AAAI 2023. link
  16. An Extreme-Adaptive Time Series Prediction Model Based on Probability-Enhanced LSTM Neural Networks. Yanhong Li(Santa Clara University), Jack Xu, David C. Anastasiu. AAAI 2023. link
  17. SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies. Fan Zhou(Ant Group), Chen Pan, Lintao Ma, Yu Liu, Shiyu Wang, James Zhang, Xinxin Zhu, Xuanwei Hu, Yunhua Hu, Yangfei Zheng, Lei Lei, Yun Hu. AAAI 2023. link
  18. WaveForM: Graph Enhanced Wavelet Learning for Long Sequence Forecasting of Multivariate Time Series. Fuhao Yang(BIT), Xin Li, Min Wang, Hongyu Zang, Wei Pang, Mingzhong Wang. AAAI 2023. link

2022

  1. STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction. Jiahao Ji(BUAA), Jingyuan Wang, Zhe Jiang, Jiawei Jiang, Hu Zhang. AAAI 2022. link
  2. Graph Neural Controlled Differential Equations for Traffic Forecasting. Jeongwhan Choi(Yonsei University), Hwangyong Choi, Jeehyun Hwang, Noseong Park. AAAI 2022. link
  3. Disentangled Spatiotemporal Graph Generative Model. Yuanqi Du(George Mason University), Xiaojie Guo, Hengning Cao, Yanfang Ye, Zhao Liang. AAAI 2022. link
  4. Reinforcement Learning Based Dynamic Model Combination for Time Series Forecasting. Yuwei Fu(McGill University), Di Wu, Benoit Boulet. AAAI 2022. link
  5. TS2Vec: Towards Universal Representation of Time Series. Zhihan Yue(PKU), Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang, Yunhai Tong, Bixiong Xu. AAAI 2022. link
  6. CATN: Cross Attentive Tree-Aware Network for Multivariate Time Series Forecasting. Hui He(Beijing Institute of Technology), Qi Zhang, Simeng Bai, Kun Yi, Zhendong Niu. AAAI 2022. link

2021

  1. Hierarchical Graph Convolution Networks for Traffic Forecasting. Kan Guo, Yongli Hu, Yanfeng Sun, Sean Qian, Junbin Gao, Baocai Yin. AAAI 2021. link
  2. Traffic Flow Prediction with Vehicle Trajectories. Mingqian Li, Panrong Tong, Mo Li, Zhongming Jin, Jianqiang Huang, Xian-Sheng Hua. AAAI 2021. link
  3. Bike-Repositioning Using Volunteers: Crowd Sourcing with Choice Restriction. Jinjia Huang, Mabel C. Chou, Chung-Piaw Teo. AAAI 2021. link
  4. AttnMove: History Enhanced Trajectory Recovery via Attentional Network. Tong Xia, Yunhan Qi, Jie Feng, Fengli Xu, Funing Sun, Diansheng Guo, Yong Li. AAAI 2021. link
  5. How Do We Move: Modeling Human Movement with System Dynamics. Hua Wei, Dongkuan Xu, Junjie Liang, Zhenhui Li. AAAI 2021. link
  6. Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting. Mengzhang Li, Zhanxing Zhu. AAAI 2021. link
  7. Disentangled Multi-Relational Graph Convolutional Network for Pedestrian Trajectory Prediction. Inhwan Bae, Hae-Gon Jeon. AAAI 2021. link
  8. FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting. Boris N. Oreshkin, Arezou Amini, Lucy Coyle, Mark Coates. AAAI 2021. link
  9. Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network. Xiyue Zhang, Chao Huang, Yong Xu, Lianghao Xia, Peng Dai, Liefeng Bo, Junbo Zhang, Yu Zheng. AAAI 2021. link
  10. Pre-Training Context and Time Aware Location Embeddings from Spatial-Temporal Trajectories for User Next Location Prediction. Yan Lin, Huaiyu Wan, Shengnan Guo, Youfang Lin. AAAI 2021. link
  11. Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Prediction. Qiang Zhou, Jingjing Gu, Xinjiang Lu, Fuzhen Zhuang, Yanchao Zhao, Qiuhong Wang, Xiao Zhang. AAAI 2021. link
  12. Community-Aware Multi-Task Transportation Demand Prediction. Hao Liu, Qiyu Wu, Fuzhen Zhuang, Xinjiang Lu, Dejing Dou, Hui Xiong. AAAI 2021. link
  13. Temporal Pyramid Network for Pedestrian Trajectory Prediction with Multi-Supervision. Rongqin Liang, Yuanman Li, Xia Li, Yi Tang, Jiantao Zhou, Wenbin Zou. AAAI 2021. link
  14. CARPe Posterum: A Convolutional Approach for Real-Time Pedestrian Path Prediction. Matias Mendieta, Hamed Tabkhi. AAAI 2021. link
  15. Coupled Layer-Wise Graph Convolution for Transportation Demand Prediction. Junchen Ye, Leilei Sun, Bowen Du, Yanjie Fu, Hui Xiong. AAAI 2021. link
  16. Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models. Rongye Shi, Zhaobin Mo, Xuan Di. AAAI 2021. link
  17. GSNet: Learning Spatial-Temporal Correlations from Geographical and Semantic Aspects for Traffic Accident Risk Forecasting. Beibei Wang, Youfang Lin, Shengnan Guo, Huaiyu Wan. AAAI 2021. link
  18. Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series. Yinjun Wu(University of Pennsylvania), Jingchao Ni, Wei Cheng, Bo Zong, Dongjin Song, Zhengzhang Chen, Yanchi Liu, Xuchao Zhang, Haifeng Chen, Susan Davidson. AAAI 2021. link
  19. Deep Switching Auto-Regressive Factorization: Application to Time Series Forecasting. Amirreza Farnoosh(Northeastern University), Bahar Azari, Sarah Ostadabbas. AAAI 2021. link
  20. Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting. Nam Nguyen(IBM Research), Brian Quanz. AAAI 2021. link
  21. Meta-Learning Framework with Applications to Zero-Shot Time-Series Forecasting. Boris N. Oreshkin(Element AI), Dmitri Carpov, Nicolas Chapados, Yoshua Bengio. AAAI 2021. link
  22. Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. Haoyi Zhou(BUAA), Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, Wancai Zhang. AAAI 2021. link

2020

  1. Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series. Zhi-Xuan Tan, Harold Soh, Desmond Ong. AAAI 2020. link
  2. GMAN: A Graph Multi-Attention Network for Traffic Prediction. Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, Jianzhong Qi. AAAI 2020. link
  3. MetaLight: Value-Based Meta-Reinforcement Learning for Traffic Signal Control. Xinshi Zang, Huaxiu Yao, Guanjie Zheng, Nan Xu, Kai Xu, Zhenhui Li. AAAI 2020. link
  4. An Attentional Recurrent Neural Network for Personalized Next Location Recommendation. Qing Guo, Zhu Sun, Jie Zhang, Yin-Leng Theng. AAAI 2020. link
  5. Tensor Completion for Weakly-Dependent Data on Graph for Metro Passenger Flow Prediction. Ziyue Li, Nurettin Dorukhan Sergin, Hao Yan, Chen Zhang, Fugee Tsung. AAAI 2020. link
  6. Learning to Generate Maps from Trajectories. Sijie Ruan, Cheng Long, Jie Bao, Chunyang Li, Zisheng Yu, Ruiyuan Li, Yuxuan Liang, Tianfu He, Yu Zheng. AAAI 2020. link
  7. Urban2Vec: Incorporating Street View Imagery and POIs for Multi-Modal Urban Neighborhood Embedding. Zhecheng Wang, Haoyuan Li, Ram Rajagopal. AAAI 2020. link
  8. Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets. Ziqiang Cheng, Yang Yang, Wei Wang, Wenjie Hu, Yueting Zhuang, Guojie Song. AAAI 2020. link
  9. Tensorized LSTM with Adaptive Shared Memory for Learning Trends in Multivariate Time Series. Dongkuan Xu, Wei Cheng, Bo Zong, Dongjin Song, Jingchao Ni, Wenchao Yu, Yanchi Liu, Haifeng Chen, Xiang Zhang. AAAI 2020. link
  10. Self-Attention ConvLSTM for Spatiotemporal Prediction. Zhihui Lin, Maomao Li, Zhuobin Zheng, Yangyang Cheng, Chun Yuan. AAAI 2020. link
  11. Spatio-Temporal Graph Structure Learning for Traffic Forecasting. Qi Zhang, Jianlong Chang, Gaofeng Meng, Shiming Xiang, Chunhong Pan. AAAI 2020. link
  12. Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting. Chao Song, Youfang Lin, Shengnan Guo, Huaiyu Wan. AAAI 2020. link
  13. Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development. Yongshun Gong, Zhibin Li, Jian Zhang, Wei Liu, Jinfeng Yi. AAAI 2020. link
  14. Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting. Weiqi Chen, Ling Chen, Yu Xie, Wei Cao, Yusong Gao, Xiaojie Feng. AAAI 2020. link
  15. Learning Geo-Contextual Embeddings for Commuting Flow Prediction. Zhicheng Liu, Fabio Miranda, Weiting Xiong, Junyan Yang, Qiao Wang, Cláudio T. Silva. AAAI 2020. link
  16. OMuLeT: Online Multi-Lead Time Location Prediction for Hurricane Trajectory Forecasting. Ding Wang, Boyang Liu, Pang-Ning Tan, Lifeng Luo. AAAI 2020. link
  17. RiskOracle: A Minute-Level Citywide Traffic Accident Forecasting Framework. Zhengyang Zhou, Yang Wang, Xike Xie, Lianliang Chen, Hengchang Liu. AAAI 2020. link
  18. Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation. Sun, Tieyun Qian, Tong Chen, Yile Liang, Quoc Viet Hung Nguyen, Hongzhi Yin. AAAI 2020. link
  19. Toward A Thousand Lights: Decentralized Deep Reinforcement Learning for Large-Scale Traffic Signal Control. Chacha Chen, Hua Wei, Nan Xu, Guanjie Zheng, Ming Yang, Yuanhao Xiong, Kai Xu, Zhenhui Li. AAAI 2020. link
  20. Real-Time Route Search by Locations. Lisi Chen, Shuo Shang, Tao Guo. AAAI 2020. link
  21. Enhancing Personalized Trip Recommendation with Attractive Routes. Jiqing Gu, Chao Song, Wenjun Jiang, Xiaomin Wang, Ming Liu. AAAI 2020. link

2019

  1. Joint Representation Learning for Multi-Modal Transportation Recommendation. Liu H, Li T, Hu R, et al. AAAI 2019. link
  2. DeepSTN+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis. Ziqian Lin, Jie Feng, Ziyang Lu, Yong Li, Depeng Jin. AAAI 2019. link
  3. Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction. Huaxiu Yao, Xianfeng Tang, Hua Wei, Guanjie Zheng, Zhenhui Li. AAAI 2019. link
  4. DeepETA: A Spatial-Temporal Sequential Neural Network Model for Estimating Time of Arrival in Package Delivery System. Fan Wu, Lixia Wu. AAAI 2019. link
  5. Congestion Graphs for Automated Time Predictions. Arik Senderovich, J. Christopher Beck, Avigdor Gal, Matthias Weidlich. AAAI 2019. link
  6. TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents. Yuexin Ma, Xinge Zhu, Sibo Zhang, Ruigang Yang, Wenping Wang, Dinesh Manocha. AAAI. AAAI 2019. link
  7. Predicting Hurricane Trajectories Using a Recurrent Neural Network. Sheila Alemany, Jonathan Beltran, Adrián Pérez, Sam Ganzfried. AAAI 2019. link
  8. Neural Collective Graphical Models for Estimating Spatio-Temporal Population Flow from Aggregated Data. Tomoharu Iwata, Hitoshi Shimizu. AAAI 2019. link
  9. Gated Residual Recurrent Graph Neural Networks for Traffic Prediction. Cen Chen, Kenli Li, Sin G. Teo, Xiaofeng Zou, Kang Wang, Jie Wang, Zeng Zeng. AAAI 2019. link
  10. Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting. Zulong Diao, Xin Wang, Dafang Zhang, Yingru Liu, Kun Xie, Shaoyao He. AAAI 2019. link
  11. Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, Huaiyu Wan. AAAI 2019. link
  12. A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data. Chuxu Zhang, Dongjin Song, Yuncong Chen, Xinyang, Cristian Lumezanu, Wei Cheng, Jingchao Ni, Bo Zong, Haifeng Chen, Nitesh V. Chawla. AAAI 2019. link
  13. Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation. Pengpeng Zhao, Haifeng Zhu, Yanchi Liu, Jiajie Xu, Zhixu Li, Fuzhen Zhuang, Victor S. Sheng, Xiaofang Zhou. AAAI 2019. link
  14. Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting. Xu Geng, Yaguang Li, Leye Wang, Lingyu Zhang, Qiang Yang, Jieping Ye, Yan Liu. AAAI 2019. link
  15. Preference-Aware Task Assignment in Spatial Crowdsourcing. Yan Zhao, Jinfu Xia, Guanfeng Liu, Han Su, Defu Lian, Shuo Shang, Kai Zheng. AAAI 2019. link
  16. Preference-Aware Task Assignment in On-Demand Taxi Dispatching: An Online Stable Matching Approach. Boming Zhao, Pan Xu, Yexuan Shi, Yongxin Tong, Zimu Zhou, Yuxiang Zeng. AAAI 2019. link
  17. A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems. Ling Pan, Qingpeng Cai, Zhixuan Fang, Pingzhong Tang, Longbo Huang. AAAI 2019. link

2018

  1. Algorithms for Trip-Vehicle Assignment in Ride-Sharing. Xiaohui Bei, Shengyu Zhang. AAAI 2018. link
  2. Privacy Preserving Point-of-Interest Recommendation Using Decentralized Matrix Factorization. Chaochao Chen, Ziqi Liu, Peilin Zhao, Jun Zhou, Xiaolong Li. AAAI 2018. link
  3. DeepUrbanMomentum: An Online Deep-Learning System for Short-Term Urban Mobility Prediction. Renhe Jiang, Xuan Song, Zipei Fan, Tianqi Xia, Quanjun Chen, Satoshi Miyazawa, Ryosuke Shibasaki. AAAI 2018. link
  4. Predicting Vehicular Travel Times by Modeling Heterogeneous Influences Between Arterial Roads. Avinash Achar, Venkatesh Sarangan, Rohith Regikumar, Anand Sivasubramaniam. AAAI 2018. link
  5. When Will You Arrive? Estimating Travel Time Based on Deep Neural Networks. Dong Wang, Junbo Zhang, Wei Cao, Jian Li, Yu Zheng. AAAI 2018. link
  6. Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction. Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, Zhenhui Li. AAAI 2018. link

2017

  1. SenseRun: Real-Time Running Routes Recommendation towards Providing Pleasant Running Experiences. Jiayu Long, Jia Jia, Han Xu. AAAI 2017. link
  2. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. Junbo Zhang, Yu Zheng, Dekang Qi. AAAI 2017. link

2016 and before

  1. Inferring a Personalized Next Point-of-Interest Recommendation Model with Latent Behavior Patterns. Jing He, Xin Li, Lejian Liao, Dandan Song, William K. Cheung. AAAI 2016. link
  2. STELLAR: Spatial-Temporal Latent Ranking for Successive Point-of-Interest Recommendation. Shenglin Zhao, Tong Zhao, Haiqin Yang, Michael R. Lyu, Irwin King. AAAI 2016. link
  3. Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts. Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan. AAAI 2016. link
  4. Learning Deep Representation from Big and Heterogeneous Data for Traffic Accident Inference. Quanjun Chen, Xuan Song, Harutoshi Yamada, Ryosuke Shibasaki. AAAI 2016. link

IJCAI

2022

  1. FOGS: First-Order Gradient Supervision with Learning-based Graph for Traffic Flow Forecasting. Xuan Rao(UESTC), Hao Wang, Shuo Shang, Liang Zhang, Jing Li, Peng Han. IJCAI 2022. link
  2. Long-term Spatio-Temporal Forecasting via Dynamic Multiple-Graph Attention. Wei Shao(Arizona State University), Zhiling Jin, Shuo Wang, Yufan Kang, Xiao Xiao, Hamid Menouar, Zhaofeng Zhang, Junshan Zhang, Flora Salim. IJCAI 2022. link
  3. Physics-Informed Long-Sequence Forecasting From Multi-Resolution Spatiotemporal Data. Chuizheng Meng(USC), Hao Niu, Guillaume Habault, Roberto Legaspi, Shinya Wada, Chihiro Ono, Yan Liu. IJCAI 2022. link
  4. When Transfer Learning Meets Cross-City Urban Flow Prediction: Spatio-Temporal Adaptation Matters. Ziquan Fang(ZJU), Dongen Wu, Lu Pan, Lu Chen, Yunjun Gao. IJCAI 2022. link
  5. Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting. Hongyuan Yu(Ant Group), Ting Li, Weichen Yu, Jianguo Li, Yan Huang, Liang Wang, Alex Liu. IJCAI 2022. link
  6. Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting. Razvan-Gabriel Cirstea, Chenjuan Guo, Bin Yang, Tung Kieu, Xuanyi Dong, Shirui Pan. IJCAI 2022. link
  7. Spatial-temporal Transformer Network with Self-supervised Learning for Traffic Flow Prediction. Zhangzhi Peng(East China Jiaotong University), Xiaohui Huang. IJCAI 2022 Workshop. link

2021

  1. Multimodal Transformer Networks for Pedestrian Trajectory Prediction. Ziyi Yin, Ruijin Liu, Zhiliang Xiong, Zejian Yuan. IJCAI 2021. link
  2. Objective-aware Traffic Simulation via Inverse Reinforcement Learning. Guanjie Zheng, Hanyang Liu, Kai Xu, Zhenhui Li. IJCAI 2021. link
  3. Incorporating Queueing Dynamics into Schedule-Driven Traffic Control. Hsu-Chieh Hu, Allen M. Hawkes, Stephen F. Smith. IJCAI 2021. link
  4. Alleviating Road Traffic Congestion with Artificial Intelligence. Guni Sharon. IJCAI 2021. link
  5. Traffic Congestion Alleviation over Dynamic Road Networks: Continuous Optimal Route Combination for Trip Query Streams. Ke Li, Lisi Chen, Shuo Shang, Panos Kalnis, Bin Yao. IJCAI 2021. link
  6. Dynamic Lane Traffic Signal Control with Group Attention and Multi-Timescale Reinforcement Learning. Qize Jiang, Jingze Li, Weiwei Sun, Baihua Zheng. IJCAI 2021. link
  7. TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning. Xu Chen, Junshan Wang, Kunqing Xie. IJCAI 2021. link
  8. Predicting Traffic Congestion Evolution: A Deep Meta Learning Approach. Yidan Sun, Guiyuan Jiang, Siew Kei Lam, Peilan He. IJCAI 2021. link
  9. Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning. L Xia, C Huang, Y Xu, P Dai, L Bo, X Zhang, T Chen. IJCAI 2021. link
  10. Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation. Yang Li, Tong Chen, Yadan Luo, Hongzhi Yin, Zi Huang. IJCAI 2021. link
  11. Location Predicts You: Location Prediction via Bi-direction Speculation and Dual-level Association. Xixi Li, Ruimin Hu, Zheng Wang, Toshihiko Yamasaki. IJCAI 2021. link
  12. Time-Series Representation Learning via Temporal and Contextual Contrasting. Emadeldeen Eldele(NTU), Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li, Cuntai Guan. IJCAI 2021. link

2020

  1. Location Prediction over Sparse User Mobility Traces Using RNNs: Flashback in Hidden States!. Dingqi Yang, Benjamin Fankhauser, Paolo Rosso, and Philippe Cudre-Mauroux. IJCAI 2020. link
  2. Towards Alleviating Traffic Congestion: Optimal Route Planning for Massive-Scale Trips. Ke Li, Lisi Chen, Shuo Shang. IJCAI 2020. link
  3. Cross-Interaction Hierarchical Attention Networks for Urban Anomaly Prediction. Chao Huang, Chuxu Zhang, Peng Dai, Liefeng Bo. IJCAI 2020. link
  4. Enhancing Urban Flow Maps via Neural ODEs. Fan Zhou, Liang Li, Ting Zhong, Goce Trajcevski, Kunpeng Zhang, Jiahao Wang. IJCAI 2020. link
  5. LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks. Rongzhou Huang, Chuyin Huang, Yubao Liu, Genan Dai, Weiyang Kong. IJCAI 2020. link
  6. MaCAR: Urban Traffic Light Control via Active Multi-agent Communication and Action Rectification. Zhengxu Yu, Shuxian Liang, Long Wei, Zhongming Jin, Jianqiang Huang, Deng Cai, Xiaofei He, Xian-Sheng Hua. IJCAI 2020. link
  7. Trajectory Similarity Learning with Auxiliary Supervision and Optimal Matching. Hanyuan Zhang, Xinyu Zhang, Qize Jiang, Baihua Zheng, Zhenbang Sun, Weiwei Sun, Changhu Wang. IJCAI 2020. link
  8. A Sequential Convolution Network for Population Flow Prediction with Explicitly Correlation Modelling. Jie Feng, Ziqian Lin, Tong Xia, Funing Sun, Diansheng Guo, Yong Li. IJCAI 2020. link

2019

  1. Travel Time Estimation without Road Networks: An Urban Morphological Layout Representation Approach. Wuwei Lan, Yanyan Xu, Bin Zhao. IJCAI 2019. link
  2. Graph WaveNet for Deep Spatial-Temporal Graph Modeling. Zonghan Wu, Guodong Long, Chengqi Zhang. IJCAI 2019. link
  3. GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction. Shen Fang, Qi Zhang, Gaofeng Meng, Shiming Xiang, Chunhong Pan. IJCAI 2019. link
  4. Cross-City Transfer Learning for Deep Spatio-Temporal Prediction. Leye Wang, Xu Geng, Xiaojuan Ma, Feng Liu, Qiang Yang. IJCAI 2019. link
  5. STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting. Lei Bai, Lina Yao, Salil S. Kanhere. IJCAI 2019. link

2018

  1. A Fast and Accurate Method for Estimating People Flow from Spatiotemporal Population Data. Yasunori Akagi, Takuya Nishimura , Takeshi Kurashima, Hiroyuki Toda. IJCAI 2018. link
  2. Estimating Latent People Flow without Tracking Individuals. Tomoharu Iwata, Takeshi Kurashima, Hiroyuki Toda, Naonori Ueda. IJCAI 2018. link
  3. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. Bing Yu, Haoteng Yin, Zhanxing Zhu. IJCAI 2018. link
  4. LC-RNN: A Deep Learning Model for Traffic Speed Prediction. Zhongjian Lv, Jiajie Xu, Kai Zheng, Hongzhi Yin, Pengpeng Zhao, Xiaofang Zhou. IJCAI 2018. link
  5. HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction. Dejiang Kong, Fei Wu. IJCAI 2018. link
  6. Predicting the Spatio-Temporal Evolution of Chronic Diseases in Population with Human Mobility Data. Yingzi Wang, Xiao Zhou, Anastasios Noulas, Cecilia Mascolo, Xing Xie, Enhong Chen. IJCAI 2018. link
  7. Spatio-Temporal Check-in Time Prediction with Recurrent Neural Network based Survival Analysis. Guolei Yang, Ying Cai, Chandan K. Reddy. IJCAI 2018. link

2017

  1. Learning User's Intrinsic and Extrinsic Interests for Point-of-Interest Recommendation: A Unified Approach. Huayu Li, Yong Ge, Defu Lian, Hao Liu. IJCAI 2017. link
  2. Category-aware Next Point-of-Interest Recommendation via Listwise Bayesian Personalized Ranking. Jing He, Xin Li, Lejian Liao. IJCAI 2017. link
  3. Understanding People Lifestyles: Construction of Urban Movement Knowledge Graph from GPS Trajectory. Nicholas Jing Yuan, Ruihua Song, Xing Xie, Qiang Ma. IJCAI 2017. link

2016 and before

  1. Exploring the Context of Locations for Personalized Location Recommendations. Xin Liu, Yong Liu, Xiaoli Li. IJCAI 2016. link
  2. Demand Prediction and Placement Optimization for Electric Vehicle Charging Stations. Ragavendran Gopalakrishnan, Arpita Biswas, Alefiya Lightwala, Skanda Vasudevan, Partha Dutta, Abhishek Tripathi. IJCAI 2016. link

KDD

2023

  1. Generative Causal Interpretation Model for Spatio-Temporal Representation Learning. Yu Zhao(BUAA), Pan Deng, Junting Liu, Xiaofeng Jia, Jianwei Zhang. KDD 2023. link
  2. Maintaining the Status Quo: Capturing Invariant Relations for OOD Spatiotemporal Learning. Zhengyang Zhou(USTC), Qihe Huang, Kuo Yang, Kun Wang, Xu Wang, Yudong Zhang, Yuxuan Liang, Yang Wang. KDD 2023. link
  3. Frigate: Frugal Spatio-temporal Forecasting on Road Networks. Mridul Gupta(Indian Institute of Technology), Hariprasad Kodamana, Sayan Ranu. KDD 2023. link
  4. Localised Adaptive Spatial-Temporal Graph Neural Network. Wenying Duan(Nanchang University), Xiaoxi He, Zimu Zhou, Lothar Thiele, Hong Rao. KDD 2023. link
  5. Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training. Fan Liu(HKUST), Weijia Zhang, Hao Liu. KDD 2023. link
  6. Transferable Graph Structure Learning for Graph-based Traffic Forecasting Across Cities. Yilun Jin(HKUST), Kai Chen, Qiang Yang. KDD 2023. link
  7. Pattern Expansion and Consolidation on Evolving Graphs for Continual Traffic Prediction. Binwu Wang(USTC), Yudong Zhang, Xu Wang, Pengkun Wang, Zhengyang Zhou, Lei Bai, Yang Wang. KDD 2023. link
  8. Large-scale Urban Cellular Traffic Generation via Knowledge-Enhanced GANs with Multi-Periodic Patterns. Shuodi Hui(THU), Huandong Wang, Tong Li, Xinghao Yang, Xing Wang, Junlan Feng, Lin Zhu, Chao Deng, Pan Hui, Depeng Jin, Yong Li. KDD 2023. link
  9. TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting. Vijay Ekambaram(IBM Research), Arindam Jati, Nam Nguyen, Phanwadee Sinthong, Jayant Kalagnanam. KDD 2023. link
  10. Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting. Arindam Jati(IBM Research), Vijay Ekambaram, Shaonli Pal, Brian Quanz, Wesley M. Gifford, Pavithra Harsha, Stuart Siegel, Sumanta Mukherjee, Chandra Narayanaswami. KDD 2023. link

2022

  1. Selective Cross-city Transfer Learning for Traffic Prediction via Source City Region Re-weighting. Yilun Jin(HKUST), Kai Chen, Qiang Yang. KDD 2022. link
  2. Modeling Network-level Traffic Flow Transitions on Sparse Data. Xiaoliang Lei(Xi'an Jiaotong University), Hao Mei, Bin Shi, Hua Wei. KDD 2022. link
  3. MSDR: Multi-Step Dependency Relation Networks for Spatial Temporal Forecasting. Dachuan Liu(UESTC), Jin Wang, Shuo Shang, Peng Han. KDD 2022. link
  4. Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction. Liangzhe Han(BUAA), Xiaojian Ma, Leilei Sun, Bowen Du, Yanjie Fu, Weifeng Lv, Hui Xiong. KDD 2022. link
  5. Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting. Junchen Ye(BUAA), Zihan Liu, Bowen Du, Leilei Sun, Weimiao Li, Yanjie Fu, Hui Xiong. KDD 2022. link
  6. Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting. Zezhi Shao(CAS), Zhao Zhang, Fei Wang, Yongjun Xu. KDD 2022. link
  7. Mining Spatio-Temporal Relations via Self-Paced Graph Contrastive Learning. Rongfan Li(UESTC), Ting Zhong, Xinke Jiang, Goce Trajcevski, Jin Wu, Fan Zhou. KDD 2022. link
  8. Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer. Bin Lu(SJTU), Xiaoying Gan, Weinan Zhang, Huaxiu Yao, Luoyi Fu, Xinbing Wang. KDD 2022. link
  9. Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks. Dingyi Zhuang(MIT), Shenhao Wang, Haris Koutsopoulos, Jinhua Zhao. KDD 2022. link
  10. Towards Learning Disentangled Representations for Time Series. Yuening Li(Texas A&M University), Zhengzhang Chen, Daochen Zha, Mengnan Du, Denghui Zhang, Haifeng Chen, Xia Hu. KDD 2022. link

2021

  1. TrajNet: A Trajectory-Based Deep Learning Model for Traffic Prediction. Bo Hui, Da Yan, Haiquan Chen, Wei-Shinn Ku. KDD 2021. link
  2. A Graph-based Approach for Trajectory Similarity Computation in Spatial Networks. Peng Han, Jin Wang, Di Yao, Shuo Shang, Xiangliang Zhang. KDD 2021. link
  3. Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting. Liangzhe Han, Bowen Du, Leilei Sun, Yanjie Fu, Yisheng Lv, Hui Xiong. KDD 2021. link
  4. ProgRPGAN: Progressive GAN for Route Planning. Tao-Yang Fu, Wang-Chien Lee. KDD 2021. link
  5. ST-Norm: Spatial and Temporal Normalization for Multi-variate Time Series Forecasting. Jinliang Deng, Xiusi Chen, Renhe Jiang, Xuan Song, Ivor W. Tsang. KDD 2021. link
  6. Network-Wide Traffic States Imputation Using Self-interested Coalitional Learning. Huiling Qin, Xianyuan Zhan, Yuanxun Li, Xiaodu Yang, Yu Zheng. KDD 2021. link
  7. Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting. Zheng Fang, Qingqing Long, Guojie Song, Kunqing Xie. KDD 2021. link
  8. MTrajRec: Map-Constrained Trajectory Recovery via Seq2Seq Multi-task Learning. Huimin Ren, Sijie Ruan, Yanhua Li, Jie Bao, Chuishi Meng, Ruiyuan Li, Yu Zheng. KDD 2021. link
  9. Quantifying Uncertainty in Deep Spatiotemporal Forecasting. Dongxia Wu(UCSD), Liyao Gao, Matteo Chinazzi, Xinyue Xiong, Alessandro Vespignani, Yi-An Ma, Rose Yu. KDD 2021. link
  10. A Transformer-based Framework for Multivariate Time Series Representation Learning. George Zerveas(Brown University), Srideepika Jayaraman, Dhaval Patel, Anuradha Bhamidipaty, Carsten Eickhoff. KDD 2021. link

2020

  1. Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data. Rui Dai, Shenkun Xu, Qian Gu, Chenguang Ji, Kaikui Liu. KDD 2020. link
  2. HetETA: Heterogeneous Information Network Embedding for Estimating Time of Arrival. Huiting Hong, Yucheng Lin, Xiaoqing Yang, Zang Li, Kung Fu, Zheng Wang, Xiaohu Qie, Jieping Ye. KDD 2020. link
  3. Dynamic Heterogeneous Graph Neural Network for Real-time Event Prediction. Wenjuan Luo, Han Zhang, Xiaodi Yang, Lin Bo, Xiaoqing Yang, Zang Li, Xiaohu Qie, Jieping Ye. KDD 2020. link
  4. Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks. Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, Chengqi Zhang. KDD 2020. link
  5. Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors. Daheng Wang, Meng Jiang, Munira Syed, Oliver Conway, Vishal Juneja, Sriram Subramanian, Nitesh V. Chawla. KDD 2020. link
  6. Fast RobustSTL: Efficient and Robust Seasonal-Trend Decomposition for Time Series with Complex Patterns. Qingsong Wen, Zhe Zhang, Yan Li, Liang Sun. KDD 2020. link
  7. Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data. Garrett Wilson, Janardhan Rao Doppa, Diane J. Cook. KDD 2020. link
  8. Personalized Prefix Embedding for POI Auto-Completion in the Search Engine of Baidu Maps. Jizhou Huang, Haifeng Wang, Miao Fan, An Zhuo, Ying Li. KDD 2020. link
  9. USAD: UnSupervised Anomaly Detection on Multivariate Time Series. Julien Audibert, Pietro Michiardi, Frédéric Guyard, Sébastien Marti, Maria A. Zuluaga. KDD 2020. link
  10. Efficiently Solving the Practical Vehicle Routing Problem: A Novel Joint Learning Approach. Lu Duan, Yang Zhan, Haoyuan Hu, Yu Gong, Jiangwen Wei, Xiaodong Zhang, Yinghui Xu. KDD 2020. link
  11. Geography-Aware Sequential Location Recommendation. Defu Lian, Yongji Wu, Yong Ge, Xing Xie, Enhong Chen. KDD 2020. link
  12. Polestar: An Intelligent, Efficient and National-Wide Public Transportation Routing Engine. Hao Liu, Ying Li, Yanjie Fu, Huaibo Mei, Jingbo Zhou, Xu Ma, Hui Xiong. KDD 2020. link
  13. AutoST: Efficient Neural Architecture Search for Spatio-Temporal Prediction. Ting Li, Junbo Zhang, Kainan Bao, Yuxuan Liang, Yexin Li, Yu Zheng. KDD 2020. link
  14. Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction. Haoxing Lin, Rufan Bai, Weijia Jia, Xinyu Yang, Yongjian You. KDD 2020. link
  15. BusTr: Predicting Bus Travel Times from Real-Time Traffic. Richard Barnes, Senaka Buthpitiya, James Cook, Alex Fabrikant, Andrew Tomkins, Fangzhou Xu. KDD 2020. link
  16. Improving Movement Predictions of Traffic Actors in Bird's-Eye View Models using GANs and Differentiable Trajectory Rasterization. Eason Wang, Henggang Cui, Sai Yalamanchi, Mohana Moorthy, Nemanja Djuric. KDD 2020. link
  17. Learning Effective Road Network Representation with Hierarchical Graph Neural Networks. Ning Wu, Wayne Xin Zhao, Jingyuan Wang, Dayan Pan. KDD 2020. link
  18. ST-SiameseNet: Spatio-Temporal Siamese Networks for Human Mobility Signature Identification. Huimin Ren, Menghai Pan, Yanhua Li, Xun Zhou, Jun Luo. KDD 2020. link
  19. ConSTGAT: Contextual Spatial-Temporal Graph Attention Network for Travel Time Estimation at Baidu Maps. Xiaomin Fang, Jizhou Huang, Fan Wang, Lingke Zeng, Haijin Liang, Haifeng Wang. KDD 2020. link
  20. CompactETA: A Fast Inference System for Travel Time Prediction. Kun Fu, Fanlin Meng, Jieping Ye, Zheng Wang. KDD 2020. link
  21. Doing in One Go: Delivery Time Inference Based on Couriers' Trajectories. Sijie Ruan, Zi Xiong, Cheng Long, Yiheng Chen, Jie Bao, Tianfu He, Ruiyuan Li, Shengnan Wu, Zhongyuan Jiang, Yu Zheng. KDD 2020. link
  22. Attention based Multi-Modal New Product Sales Time-series Forecasting. Vijay Ekambaram, Kushagra Manglik, Sumanta Mukherjee, Surya Shravan Kumar Sajja, Satyam Dwivedi, Vikas Raykar. KDD 2020. link
  23. Competitive Analysis for Points of Interest. Shuangli Li, Jingbo Zhou, Tong Xu, Hao Liu, Xinjiang Lu, Hui Xiong. KDD 2020. link
  24. Delivery Scope: A New Way of Restaurant Retrieval for On-demand Food Delivery Service. Xuetao Ding, Runfeng Zhang, Zhen Mao, Ke Xing, Fangxiao Du, Xingyu Liu, Guoxing Wei, Feifan Yin, Renqing He, Zhizhao Sun. KDD 2020. link
  25. City Metro Network Expansion with Reinforcement Learning. Yu Wei, Minjia Mao, Xi Zhao, Jianhua Zou, Ping An. KDD 2020. link
  26. Curb-GAN: Conditional Urban Traffic Estimation through Spatio-Temporal Generative Adversarial Networks. Yingxue Zhang, Yanhua Li, Xun Zhou, Xiangnan Kong, Jun Luo. KDD 2020. link

2019

  1. Empowering A Search Algorithms with Neural Networks for Personalized Route Recommendation.* J Wang, N Wu, WX Zhao, F Peng, X Lin. KDD 2019. link
  2. Hydra: A Personalized and Context-Aware Multi-Modal Transportation Recommendation System. Liu H, Tong Y, Zhang P, et al. KDD 2019. link
  3. Predicting dynamic embedding trajectory in temporal interaction networks. Srijan Kumar, Xikun Zhang, Jure Leskovec. KDD 2019. link
  4. Urban traffic prediction from spatio-temporal data using deep meta learning. Zheyi Pan, Yuxuan Liang, Weifeng Wang, Yong Yu, Yu Zheng, Junbo Zhang. KDD 2019. link
  5. Deepurbanevent: A system for predicting citywide crowd dynamics at big events. Renhe Jiang, Xuan Song, Dou Huang, Xiaoya Song, Tianqi Xia, Zekun Cai, Zhaonan Wang, Kyoung-Sook Kim, Ryosuke Shibasaki. KDD 2019. link
  6. Large-scale user visits understanding and forecasting with deep spatial-temporal tensor factorization framework. Xiaoyang Ma, Lan Zhang, Lan Xu, Zhicheng Liu, Ge Chen, Zhili Xiao, Yang Wang, Zhengtao Wu. KDD 2019. link
  7. Lightnet: A dual spatiotemporal encoder network model for lightning prediction. Yangli-ao Geng, Qingyong Li, Tianyang Lin, Lei Jiang, Liangtao Xu, Dong Zheng, Wen Yao, Weitao Lyu, Yijun Zhang. KDD 2019. link
  8. Origin-destination matrix prediction via graph convolution: a new perspective of passenger demand modeling. Yuandong Wang, Hongzhi Yin, Hongxu Chen, Tianyu Wo, Jie Xu, Kai Zheng. KDD 2019. link
  9. Co-prediction of multiple transportation demands based on deep spatio-temporal neural network. Junchen Ye, Leilei Sun, Bowen Du, Yanjie Fu, Xinran Tong, Hui Xiong. KDD 2019. link
  10. Deep mixture point processes: Spatio-temporal event prediction with rich contextual information. Maya Okawa, Tomoharu Iwata, Takeshi Kurashima, Yusuke Tanaka, Hiroyuki Toda, Naonori Ueda. KDD 2019. link

2018

  1. Intellilight: A reinforcement learning approach for intelligent traffic light control. Hua Wei, Guanjie Zheng, Huaxiu Yao, Zhenhui Li. KDD 2018. link
  2. Stepdeep: A novel spatial-temporal mobility event prediction framework based on deep neural network. Shen, Xiaodan Liang, Yufeng Ouyang, Miaofeng Liu, Weimin Zheng, Kathleen M. Carley. KDD 2018. link
  3. Deep sequence learning with auxiliary information for traffic prediction. Binbing Liao, Jingqing Zhang, Chao Wu, Douglas McIlwraith, Tong Chen, Shengwen Yang, Yike Guo, Fei Wu. KDD 2018. link
  4. A dynamic pipeline for spatio-temporal fire risk prediction. Bhavkaran Singh Walia, Qianyi Hu, Jeffrey Chen, Fangyan Chen, Jessica Lee, Nathan Kuo, Palak Narang, Jason Batts, Geoffrey Arnold, Michael Madaio. KDD 2018. link

2017

  1. Triovecevent: Embedding-based online local event detection in geo-tagged tweet streams. Chao Zhang, Liyuan Liu, Dongming Lei, Quan Yuan, Honglei Zhuang, Tim Hanratty, Jiawei Han. KDD 2017. link
  2. Point-of-interest demand modeling with human mobility patterns. Yanchi Liu, Chuanren Liu, Xinjiang Lu, Mingfei Teng, Hengshu Zhu, Hui Xiong. KDD 2017. link
  3. Human mobility synchronization and trip purpose detection with mixture of hawkes processes. Pengfei Wang, Yanjie Fu, Guannan Liu, Wenqing Hu, Charu C. Aggarwal. KDD 2017. link
  4. The simpler the better: a unified approach to predicting original taxi demands based on large-scale online platforms. Yongxin Tong, Yuqiang Chen, Zimu Zhou, Lei Chen, Jie Wang, Qiang Yang, Jieping Ye, Weifeng Lv. KDD 2017. link
  5. Functional zone based hierarchical demand prediction for bike system expansion. Junming Liu, Leilei Sun, Qiao Li, Jingci Ming, Yanchi Liu, Hui Xiong. KDD 2017. link
  6. Planning bike lanes based on sharing-bikes' trajectories. Jie Bao, Tianfu He, Sijie Ruan, Yanhua Li, Yu Zheng. KDD 2017. link

2016 and before

  1. Hierarchical incomplete multi-source feature learning for spatiotemporal event forecasting. Liang Zhao, Jieping Ye, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan. KDD 2016. link
  2. Unified point-of-interest recommendation with temporal interval assessment. Yanchi Liu, Chuanren Liu, Bin Liu, Meng Qu, Hui Xiong. KDD 2016. link
  3. Latent space model for road networks to predict time-varying traffic. Prithu Banerjee, Pranali Yawalkar, Sayan Ranu. KDD 2016. link
  4. Discovering Spatio-Temporal Causal Interactions in Traffic Data Streams. Wei Liu(USYD), Yu Zheng, Sanjay Chawla, Jing Yuan, Xing Xie. KDD 2011. link

CIKM

2023

  1. Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting. Qian Ma(CUHK), Zijian Zhang, Xiangyu Zhao, Haoliang Li, Hongwei Zhao, Yiqi Wang, Zitao Liu, Wanyu Wang. CIKM 2023. link
  2. Cross-city Few-Shot Traffic Forecasting via Traffic Pattern Bank. Zhanyu Liu(SJTU), Guanjie Zheng, Yanwei Yu. CIKM 2023. link
  3. Explainable Spatio-Temporal Graph Neural Networks. Jiabin Tang(HKU), Lianghao Xia, Chao Huang. CIKM 2023. link
  4. Spatial-Temporal Graph Boosting Networks: Enhancing Spatial-Temporal Graph Neural Networks via Gradient Boosting. Yujie Fan(Visa Research), Chin-Chia Michael Yeh, Huiyuan Chen, Yan Zheng, Liang Wang, Junpeng Wang, Xin Dai, Zhongfang Zhuang, Wei Zhang. CIKM 2023. link
  5. Mask- and Contrast-Enhanced Spatio-Temporal Learning for Urban Flow Prediction. Xu Zhang(Qilu University of Technology), Yongshun Gong, Xinxin Zhang, Xiaoming Wu, Chengqi Zhang, Xiangjun Dong. CIKM 2023. link
  6. Region Profile Enhanced Urban Spatio-Temporal Prediction via Adaptive Meta-Learning. Jie Chen(Shanghai University), Tong Liu, Ruiyuan Li. CIKM 2023. link
  7. Enhancing Spatio-temporal Traffic Prediction through Urban Human Activity Analysis. Sumin Han(KAIST), Youngjun Park, Minji Lee, Jisun An, Dongman Lee. CIKM 2023. link
  8. Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction. Xinke Jiang(PKU), Dingyi Zhuang, Xianghui Zhang, Hao Chen, Jiayuan Luo, Xiaowei Gao. CIKM 2023. link
  9. ST-MoE: Spatio-Temporal Mixture-of-Experts for Debiasing in Traffic Prediction. Shuhao Li(FDU), Yue Cui, Yan Zhao, Weidong Yang, Ruiyuan Zhang, Xiaofang Zhou. CIKM 2023. link
  10. Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting. Hangchen Liu(Southern University of Science and Technology), Zheng Dong, Renhe Jiang, Jiewen Deng, Jinliang Deng, Quanjun Chen, Xuan Song. CIKM 2023. link
  11. MLPST: MLP is All You Need for Spatio-Temporal Prediction. Zijian Zhang(Jilin University), Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S. Joe Qin, Hongwei Zhao. CIKM 2023. link
  12. PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction. Zijian Zhang(Jilin University), Xiangyu Zhao, Qidong Liu, Chunxu Zhang, Qian Ma, Wanyu Wang, Hongwei Zhao, Yiqi Wang, Zitao Liu. CIKM 2023. link
  13. Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting. Juyong Jiang(HKUST), Binqing Wu, Ling Chen, Kai Zhang, Sunghun Kim. CIKM 2023. link
  14. Spatio-Temporal Meta Contrastive Learning. Jiabin Tang(HKU), Lianghao Xia, Jie Hu, Chao Huang. CIKM 2023. link
  15. CARPG: Cross-City Knowledge Transfer for Traffic Accident Prediction via Attentive Region-Level Parameter Generation. Guang Yang(Rutgers University), Yuequn Zhang, Jinquan Hang, Xinyue Feng, Zejun Xie, Desheng Zhang, Yu Yang. CIKM 2023. link
  16. Adaptive Graph Neural Diffusion for Traffic Demand Forecasting. Yiling Wu(Peng Cheng Laboratory), Xinfeng Zhang, Yaowei Wang. CIKM 2023. link
  17. Clustering-property Matters: A Cluster-aware Network for Large Scale Multivariate Time Series Forecasting. Yuan Wang(CAS), Zezhi Shao, Tao Sun, Chengqing Yu, Yongjun Xu, Fei Wang. CIKM 2023. link
  18. DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Prediction. Chengqing Yu(CAS), Fei Wang, Zezhi Shao, Tao Sun, Lin Wu, Yongjun Xu. CIKM 2023. link
  19. GCformer: An Efficient Solution for Accurate and Scalable Long-Term Multivariate Time Series Forecasting. Yanjun Zhao(Xi'an Jiaotong University), Ziqing Ma, Tian Zhou, Mengni Ye, Liang Sun, Yi Qian. CIKM 2023. link

2022

  1. Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities. Yihong Tang(HKU), Ao Qu, Andy H.F. Chow, William H.K. Lam, S.C. Wong, Wei Ma. CIKM 2022. link
  2. Adaptive Graph Spatial-Temporal Transformer Network for Traffic Forecasting. Aosong Feng(Yale), Leandros Tassiulas. CIKM 2022. link
  3. ST-GAT: A Spatio-Temporal Graph Attention Network for Accurate Traffic Speed Prediction. Junho Song(Hanyang University), Jiwon Son, Dong-hyuk Seo, Kyungsik Han, Namhyuk Kim, Sang-Wook Kim. CIKM 2022. link
  4. Automated Spatio-Temporal Synchronous Modeling with Multiple Graphs for Traffic Prediction. Fuxian Li(THU), Huan Yan, Guangyin Jin, Yue Liu, Yong Li, Depeng Jin. CIKM 2022. link
  5. Residual Correction in Real-Time Traffic Forecasting. Daejin Kim(KAIST), Youngin Cho, Dongmin Kim, Cheonbok Park, Jaegul Choo. CIKM 2022. link
  6. Memory Augmented Graph Learning Networks for Multivariate Time Series Forecasting. Xiangyue Liu(Central South University), Xinqi Lyu, Xiangchi Zhang, Jianliang Gao, Jiamin Chen. CIKM 2022. link
  7. Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting. Zezhi Shao(CAS), Zhao Zhang, Fei Wang, Wei Wei, Yongjun Xu. CIKM 2022. link

2021

  1. DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction. Renhe Jiang(University of Tokyo), Du Yin, Zhaonan Wang, Yizhuo Wang, Jiewen Deng, Hangchen Liu, Zekun Cai, Jinliang Deng, Xuan Song, Ryosuke Shibasaki. CIKM 2021. link
  2. Multivariate and Propagation Graph Attention Network for Spatial-Temporal Prediction with Outdoor Cellular Traffic. Chung-Yi Lin(National Taiwan University), Hung-Ting Su, Shen-Lung Tung, Winston H. Hsu. CIKM 2021. link

2020

  1. STP-TrellisNets: Spatial-Temporal Parallel TrellisNets for Metro Station Passenger Flow Prediction. Junjie Ou, Jiahui Sun, Yichen Zhu, Haiming Jin, Yijuan Liu, Fan Zhang, Jianqiang Huang, Xinbing Wang. CIKM 2020. link
  2. Knowledge Adaption for Demand Prediction based on Multi-task Memory Neural Network. Can Li, Lei Bai, Wei Liu, Lina Yao, S. Travis Waller. CIKM 2020. link
  3. A Joint Inverse Reinforcement Learning and Deep Learning Model for Drivers' Behavioral Prediction. Guojun Wu, Yanhua Li, Shikai Luo, Ge Song, Qichao Wang, Jing He, Jieping Ye, Xiaohu Qie, Hongtu Zhu. CIKM 2020. link
  4. Deep Spatio-Temporal Multiple Domain Fusion Network for Urban Anomalies Detection. Ruiqiang Liu, Shuai Zhao, Bo Cheng, Hao Yang, Haina Tang, Taoyu Li. CIKM 2020. link
  5. DATSING: Data Augmented Time Series Forecasting with Adversarial Domain Adaptation. Hailin Hu, MingJian Tang, Chengcheng Bai. CIKM 2020. link
  6. Learning Graph-Based Geographical Latent Representation for Point-of-Interest Recommendation. Buru Chang, Gwanghoon Jang, Seoyoon Kim, Jaewoo Kang. CIKM 2020. link
  7. Magellan: A Personalized Travel Recommendation System Using Transaction Data. Konik Kothari, Dhruv Gelda, Wei Zhang, Hao Yang. CIKM 2020. link
  8. Generating Full Spatiotemporal Vehicular Paths: A Data Fusion Approach. Nan Xiao, Nan Hu, Liang Yu, Cheng Long. CIKM 2020. link
  9. Multi-task Adversarial Spatial-Temporal Networks for Crowd Flow Prediction. Senzhang Wang, Hao Miao, Hao Chen, Zhiqiu Huang. CIKM 2020. link
  10. Spatiotemporal Adaptive Gated Graph Convolution Network for Urban Traffic Flow Forecasting. Bin Lu, Xiaoying Gan, Haiming Jin, Luoyi Fu, Haisong Zhang. CIKM 2020. link
  11. Spatial-Temporal Convolutional Graph Attention Networks for Citywide Traffic Flow Forecasting. Xiyue Zhang, Chao Huang, Yong Xu, Lianghao Xia. CIKM 2020. link
  12. Deep Graph Convolutional Networks for Incident-Driven Traffic Speed Prediction. Qinge Xie, Tiancheng Guo, Yang Chen, Yu Xiao, Xin Wang, Ben Y. Zhao. CIKM 2020. link
  13. ST-GRAT: A Novel Spatio-temporal Graph Attention Networks for Accurately Forecasting Dynamically Changing Road Speed. Cheonbok Park, Chunggi Lee, Hyojin Bahng, Yunwon Tae, Seungmin Jin, Kihwan Kim, Sungahn Ko, Jaegul Choo. CIKM 2020. link
  14. A Reproducibility Study of Deep and Surface Machine Learning Methods for Human-related Trajectory Prediction. Bardh Prenkaj, Paola Velardi, Damiano Distante, Stefano Faralli. CIKM 2020. link
  15. Elevated Road Network: A Metric Learning Method for Recognizing Whether a Vehicle is on an Elevated Road. Xiaobing Zhang, Hailiang Xu, Jian Yang, Jia Sun, Fan Chen, Leiyun Li. CIKM 2020. link
  16. STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation. Nicholas Lim, Bryan Hooi, See-Kiong Ng, Xueou Wang, Yong Liang Goh, Renrong Weng, Jagannadan Varadarajan. CIKM 2020. link
  17. GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning. Huichu Zhang, Chang Liu, Weinan Zhang, Guanjie Zheng, Yong Yu. CIKM 2020. link
  18. InterNet: Multistep Traffic Forecasting by Interacting Spatial and Temporal Features. Yilian Xin, Dezhuang Miao, Mengxia Zhu, Cheqing Jin, Xuesong Lu. CIKM 2020. link
  19. Smarter and Safer Traffic Signal Controlling via Deep Reinforcement Learning. Bingquan Yu, Jinqiu Guo, Qinpei Zhao, Jiangfeng Li, Weixiong Rao. CIKM 2020. link

2019

  1. Spatio-Temporal Graph Convolutional and Recurrent Networks for Citywide Passenger Demand Prediction. Lei Bai, Lina Yao, Salil S. Kanhere, Xianzhi Wang, Wei Liu, Zheng Yang. CIKM 2019. link
  2. Long- and Short-term Preference Learning for Next POI Recommendation. Yuxia Wu, Ke Li, Guoshuai Zhao, Xueming Qian. CIKM 2019. link
  3. Exploring The Interaction Effects for Temporal Spatial Behavior Prediction. Huan Yang, Tianyuan Liu, Yuqing Sun, Elisa Bertino. CIKM 2019. link
  4. Deep Dynamic Fusion Network for Traffic Accident Forecasting. Chao Huang, Chuxu Zhang, Peng Dai, Liefeng Bo. CIKM 2019. link
  5. Learning Traffic Signal Control from Demonstrations. Yuanhao Xiong, Guanjie Zheng, Kai Xu, Zhenhui Li. CIKM 2019. link
  6. Learning Phase Competition for Traffic Signal Control. Guanjie Zheng, Yuanhao Xiong, Xinshi Zang, Jie Feng, Hua Wei, Huichu Zhang, Yong Li, Kai Xu, Zhenhui Li. CIKM 2019. link
  7. CoLight: Learning Network-level Cooperation for Traffic Signal Control. Hua Wei, Nan Xu, Huichu Zhang, Guanjie Zheng, Xinshi Zang, Chacha Chen, Weinan Zhang, Yanmin Zhu, Kai Xu, Zhenhui Li. CIKM 2019. link
  8. Unsupervised Representation Learning of Spatial Data via Multimodal Embedding. Porter Jenkins, Ahmad Farag, Suhang Wang, Zhenhui Li. CIKM 2019. link
  9. DeepIST: Deep Image-based Spatio-Temporal Network for Travel Time Estimation. Tao-Yang Fu, Wang-Chien Lee. CIKM 2019. link
  10. Personalized Route Description Based On Historical Trajectories. Han Su, Guanglin Cong, Wei Chen, Bolong Zheng, Kai Zheng. CIKM 2019. link
  11. Matrix Factorization for Spatio-Temporal Neural Networks with Applications to Urban Flow Prediction. Zheyi Pan, Zhaoyuan Wang, Weifeng Wang, Yong Yu, Junbo Zhang, Yu Zheng. CIKM 2019. link
  12. STAR: Spatio-Temporal Taxonomy-Aware Tag Recommendation for Citizen Complaints. Jingyue Gao, Yuanduo He, Yasha Wang, Xiting Wang, Jiangtao Wang, Guangju Peng, Xu Chu. CIKM 2019. link
  13. CityTraffic: Modeling Citywide Traffic via Neural Memorization and Generalization Approach. Xiuwen Yi, Zhewen Duan, Ting Li, Tianrui Li, Junbo Zhang, Yu Zheng. CIKM 2019. link
  14. Learning to Effectively Estimate the Travel Time for Fastest Route Recommendation. Ning Wu, Jingyuan Wang, Wayne Xin Zhao, Yang Jin. CIKM 2019. link
  15. Temporal network embedding with micro-and macro-dynamics. Yuanfu Lu, Xiao Wang, Chuan Shi, Philip S. Yu, Yanfang Ye. CIKM 2019. link
  16. Towards explainable representation of time-evolving graphs via spatial-temporal graph attention networks. Zhining Liu, Dawei Zhou, Jingrui He. CIKM 2019. link
  17. Query processing techniques for big spatial-keyword data. Ahmed R. Mahmood, Walid G. Aref. CIKM 2019. link
  18. Modeling temporal-spatial correlations for crime prediction. Xiangyu Zhao, Jiliang Tang. CIKM 2019. link
  19. Learning Region Similarity over Spatial Knowledge Graphs with Hierarchical Types and Semantic Relations. Xiongnan Jin, Byungkook Oh, Sanghak Lee, Dongho Lee, Kyong-Ho Lee, Liang Chen. CIKM 2019. link

2018

  1. Traffic-cascade: Mining and visualizing lifecycles of traffic congestion events using public bus trajectories. Agus Trisnajaya Kwee, Meng-Fen Chiang, Philips Kokoh Prasetyo, Ee-Peng Lim. CIKM 2018. link
  2. On Prediction of User Destination by Sub-Trajectory Understanding: A Deep Learning based Approach. Jing Zhao, Jiajie Xu, Rui Zhou, Pengpeng Zhao, Chengfei Liu, Feng Zhu. CIKM 2018. link
  3. Recurrent Spatio-Temporal Point Process for Check-in Time Prediction. Guolei Yang, Ying Cai, Chandan K. Reddy. CIKM 2018. link
  4. Network-wide Crowd Flow Prediction of Sydney Trains via Customized Online Non-negative Matrix Factorization. Yongshun Gong, Zhibin Li, Jian Zhang, Wei Liu, Yu Zheng, Christina Kirsch. CIKM 2018. link
  5. Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence. Chen Ma, Yingxue Zhang, Qinglong Wang, Xue Liu. CIKM 2018. link

2017

  1. Urbanity: A System for Interactive Exploration of Urban Dynamics from Streaming Human Sensing Data. Mengxiong Liu, Zhengchao Liu, Chao Zhang, Keyang Zhang, Quan Yuan, Tim Hanratty, Jiawei Han. CIKM 2017. link
  2. SERM: A Recurrent Model for Next Location Prediction in Semantic Trajectories. Di Yao, Chao Zhang, Jian-Hui Huang, Jingping Bi. CIKM 2017. link
  3. Destination-aware task assignment in spatial crowdsourcing. Yan Zhao, Yang Li, Yu Wang, Han Su, Kai Zheng. CIKM 2017. link

2016 and before

  1. Urban Traffic Prediction through the Second Use of Inexpensive Big Data from Buildings. Zimu Zheng, Dan Wang, Jian Pei, Yi Yuan, Cheng Fan, Linda Fu Xiao. CIKM 2016. link
  2. Inferring Traffic Incident Start Time with Loop Sensor Data. Mingxuan Yue, Liyue Fan, Cyrus Shahabi. CIKM 2016. link
  3. Collective Traffic Prediction with Partially Observed Traffic History using Location-Based Social Media. Xinyue Liu, Xiangnan Kong, Yanhua Li. CIKM 2016. link
  4. Near Real-time Geolocation Prediction in Twitter Streams via Matrix Factorization Based Regression. Nghia Duong-Trung, Nicolas Schilling, Lars Schmidt-Thieme. CIKM 2016. link
  5. Improving Personalized Trip Recommendation by Avoiding Crowds. Xiaoting Wang, Christopher Leckie, Jeffrey Chan, Kwan Hui Lim, Tharshan Vaithianathan. CIKM 2016. link
  6. Learning Graph-based POI Embedding for Location-based Recommendation. Min Xie, Hongzhi Yin, Hao Wang, Fanjiang Xu, Weitong Chen, Sen Wang. CIKM 2016. link
  7. Learning Points and Routes to Recommend Trajectories. Dawei Chen, Cheng Soon Ong, Lexing Xie. CIKM 2016. link

ICDM

2023

  1. Uncertainty-aware Traffic Prediction under Missing Data. Hao Mei(Arizona State University), Junxian Li, Zhiming Liang, Guanjie Zheng, Bin Shi, Hua Wei. ICDM 2023. link
  2. Spatio-Temporal Hypergraph Neural ODE Network for Traffic Forecasting. Chengzhi Yao(Sun Yat-Sen University), Zhi Li, Junbo Wang. ICDM 2023. link
  3. STSD: Modeling Spatial Temporal Staticity and Dynamicity in Traffic Forecasting. Guanghui Zhu(Nanjing University), Haojun Hou, Peiliang Wang, Chunfeng Yuan, Yihua Huang. ICDM 2023. link
  4. Boosting Urban Prediction via Addressing Spatial-Temporal Distribution Shift. Xuanming Hu(Arizona State University), Wei Fan, Kun Yi, Pengfei Wang, Yuanbo Xu, Yanjie Fu, Pengyang Wang. ICDM 2023. link

2022

  1. Origin-Destination Traffic Prediction based on Hybrid Spatio-Temporal Network. Tingyang Chen(Huazhong University of Science and Technology), Lugang Nie, Jiwei Pan, Lai Tu, Bolong Zheng, Xiang Bai. ICDM 2022. link
  2. Higher-Order Masked Graph Neural Networks for Traffic Flow Prediction. Kaixin Yuan(Xidian University), Jing Liu, Jian Lou. ICDM 2022. link
  3. STrans-GAN: Spatially-Transferable Generative Adversarial Networks for Urban Traffic Estimation. Yingxue Zhang(Binghamton University), Yanhua Li, Xun Zhou, Xiangnan Kong, Jun Luo. ICDM 2022. link
  4. Exploiting Hierarchical Correlations for Cross-City Cross-Mode Traffic Flow Prediction. Yan Chen(NUAA), Jingjing Gu, Fuzhen Zhuang, Xinjiang Lu, Ming Sun. ICDM 2022. link

2021

  1. Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting. Song Yang(The University of Auckland), Jiamou Liu, Kaiqi Zhao. ICDM 2021. link
  2. Trajectory WaveNet: A Trajectory-Based Model for Traffic Forecasting. Bo Hui(Auburn University), Da Yan, Haiquan Chen, Wei-Shinn Ku. ICDM 2021. link
  3. Adaptive Spatio-Temporal Convolutional Network for Traffic Prediction. Mingyang Zhang(HKUST), Yong Li, Funing Sun, Diansheng Guo, Pan Hui. ICDM 2021. link
  4. TEST-GCN: Topologically Enhanced Spatial-Temporal Graph Convolutional Networks for Traffic Forecasting. Muhammad Afif Ali(Grab-NUS AI Lab), Suriyanarayanan Venkatesan, Victor Liang, Hannes Kruppa. ICDM 2021. link
  5. Temporal Multi-view Graph Convolutional Networks for Citywide Traffic Volume Inference. Shaojie Dai(Ocean University of China), Jinshuai Wang, Chao Huang, Yanwei Yu, Junyu Dong. ICDM 2021. link
  6. LIFE: Learning Individual Features for Multivariate Time Series Prediction with Missing Values. Zhao-Yu Zhang(Nanjing University), Shao-Qun Zhang, Yuan Jiang, Zhi-Hua Zhou. ICDM 2021. link

2020

  1. FreqST: Exploiting Frequency Information in Spatiotemporal Modeling for Traffic Prediction. Xian Zhou, Yanyan Shen, Linpeng Huang. ICDM 2020. link
  2. Multi-Attention 3D Residual Neural Network for Origin-Destination Crowd Flow Prediction. Jiaman Ma, Jeffrey Chan, Sutharshan Rajasegarar, Goce Ristanoski, Christopher Leckie. ICDM 2020. link
  3. Modeling Personalized Out-of-Town Distances in Location Recommendation. Daizong Ding, Mi Zhang, Xudong Pan, Min Yang, Xiangnan He. ICDM 2020. link
  4. Interpretable Spatiotemporal Deep Learning Model for Traffic Flow Prediction based on Potential Energy Fields. Jiahao Ji, Jingyuan Wang, Zhe Jiang, Jingtian Ma, Hu Zhang. ICDM 2020. link
  5. cST-ML: Continuous Spatial-Temporal Meta-Learning for Traffic Dynamics Prediction. Yingxue Zhang, Yanhua Li, Xun Zhou, Jun Luo. ICDM 2020. link
  6. TSSRGCN: Temporal Spectral Spatial Retrieval Graph Convolutional Network for Traffic Flow Forecasting. Xu Chen, Yuanxing Zhang, Lun Du, Zheng Fang, Yi Ren, Kaigui Bian, Kunqing Xie. ICDM 2020. link
  7. STGCN: A Spatial-Temporal Aware Graph Learning Method for POI Recommendation. Haoyu Han, Mengdi Zhang, Min Hou, Fuzheng Zhang, Zhongyuan Wang, Enhong Chen, Hongwei Wang, Jianhui Ma, Qi Liu. ICDM 2020. link
  8. Building Autocorrelation-Aware Representations for Fine-Scale Spatiotemporal Prediction. Yijun Lin, Yao-Yi Chiang, Meredith Franklin, Sandrah P. Eckel, José Luis Ambite. ICDM 2020. link

2019

  1. TrafficGAN: Off-Deployment Traffic Estimation with Traffic Generative Adversarial Networks. Yingxue Zhang, Yanhua Li, Xun Zhou, Xiangnan Kong, Jun Luo. ICDM 2019. link
  2. Boosted trajectory calibration for traffic state estimation. Xitong Zhang, Liyang Xie, Zheng Wang, Jiayu Zhou. ICDM 2019. link

2018

  1. An integrated model for crime prediction using temporal and spatial factors. Fei Yi, Zhiwen Yu, Fuzhen Zhuang, Xiao Zhang, Hui Xiong. ICDM 2018. link
  2. Next point-of-interest recommendation with temporal and multi-level context attention. Ranzhen Li, Yanyan Shen, Yanmin Zhu. ICDM 2018. link
  3. Exploiting spatio-temporal correlations with multiple 3d convolutional neural networks for citywide vehicle flow prediction. Cen Chen, Kenli Li, Sin G. Teo, Guizi Chen, Xiaofeng Zou, Xulei Yang, Ramaseshan C. Vijay, Jiashi Feng, Zeng Zeng. ICDM 2018. link
  4. Outlier detection in urban traffic flow distributions. Youcef Djenouri, Arthur Zimek, Marco Chiarandini. ICDM 2018. link

2017

  1. Exploiting Hierarchical Structures for POI Recommendation. Pengpeng Zhao, Xiefeng Xu, Yanchi Liu, Ziting Zhou, Kai Zheng, Victor S. Sheng, Hui Xiong. ICDM 2017. link
  2. Situation Aware Multi-task Learning for Traffic Prediction. Dingxiong Deng, Cyrus Shahabi, Ugur Demiryurek, Linhong Zhu. ICDM 2017. link
  3. Spatio-Temporal Neural Networks for Space-Time Series Forecasting and Relations Discovery. Ali Ziat, Edouard Delasalles, Ludovic Denoyer, Patrick Gallinari. ICDM 2017. link
  4. Autoregressive Tensor Factorization for Spatio-Temporal Predictions. Koh Takeuchi, Hisashi Kashima, Naonori Ueda. ICDM 2017. link

2016 and before

  1. POI Recommendation: A Temporal Matching between POI Popularity and User Regularity. Zijun Yao, Yanjie Fu, Bin Liu, Yanchi Liu, Hui Xiong. ICDM 2016. link
  2. Regularized Content-Aware Tensor Factorization Meets Temporal-Aware Location Recommendation. Defu Lian, Zhenyu Zhang, Yong Ge, Fuzheng Zhang, Nicholas Jing Yuan, Xing Xie. ICDM 2016. link
  3. Traffic Speed Prediction and Congestion Source Exploration: A Deep Learning Method. Jingyuan Wang, Qian Gu, Junjie Wu, Guannan Liu, Zhang Xiong. ICDM 2016. link

WWW

2024

  1. Unveiling Delay Effects in Traffic Forecasting: A Perspective from Spatial-Temporal Delay Differential Equations. Qingqing Long(CAS), Zheng Fang, Chen Fang, Chong Chen, Pengfei Wang, Yuanchun Zhou. WWW 2024. link
  2. SSTKG: Simple Spatio-Temporal Knowledge Graph for Intepretable and Versatile Dynamic Information Embedding. Ruiyi Yang(University of New South Wales), Flora D. Salim, Hao Xue. WWW 2024. link

2023

  1. Automated Spatio-Temporal Graph Contrastive Learning. Qianru Zhang(HKU), Chao Huang, Lianghao Xia, Zheng Wang, Zhonghang Li, Siuming Yiu. WWW 2023. link
  2. INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging. Chuanpan Zheng(Xiamen University), Xiaoliang Fan, Cheng Wang, Jianzhong Qi, Chaochao Chen, Longbiao Chen. WWW 2023. link

2022

  1. Pyramid: Enabling Hierarchical Neural Networks with Edge Computing. Qiang He(Swinburne University of Technology), Zeqian Dong, Feifei Chen, Shuiguang Deng, Weifa Liang, Yun Yang. WWW 2022. link

2021

  1. Variable Interval Time Sequence Modeling for Career Trajectory Prediction: Deep Collaborative Perspective. Chao Wang, Hengshu Zhu, Qiming Hao, Keli Xiao, Hui Xiong. WWW 2021.
  2. STAN: Spatio-Temporal Attention Network for Next Location Recommendation. Yingtao Luo, Qiang Liu, and Zhaocheng Liu. WWW 2021. link
  3. DF-TAR: A Deep Fusion Network for Citywide Traffic Accident Risk Prediction with Dangerous Driving Behavior. Patara Trirat, Jae-Gil Lee. WWW 2021. link

2020

  1. Next Point-of-Interest Recommendation on Resource-Constrained Mobile Devices. Qinyong Wang, Hongzhi Yin, Tong Chen, Zi Huang, Hao Wang, Yanchang Zhao, Nguyen Quoc Viet Hung. WWW 2020. link
  2. Traffic Flow Prediction via Spatial Temporal Graph Neural Network. Xiaoyang Wang, Yao Ma, Yiqi Wang, Wei Jin, Xin Wang, Jiliang Tang, Caiyan Jia, Jian Yu. WWW 2020. link
  3. Hierarchically Structured Transformer Networks for Fine-Grained Spatial Event Forecasting. Xian Wu, Chao Huang, Chuxu Zhang, Nitesh V. Chawla. WWW 2020. link
  4. Towards Fine-grained Flow Forecasting: A Graph Attention Approach for Bike Sharing Systems. Suining He, Kang G. Shin. WWW 2020. link
  5. Dynamic Flow Distribution Prediction for Urban Dockless E-Scooter Sharing Reconfiguration. Suining He, Kang G. Shin. WWW 2020. link
  6. What is the Human Mobility in a New City: Transfer Mobility Knowledge Across Cities. Tianfu He, Jie Bao, Ruiyuan Li, Sijie Ruan, Yanhua Li, Li Song, Hui He, Yu Zheng. WWW 2020. link
  7. Mining Points-of-Interest for Explaining Urban Phenomena: A Scalable Variational Inference Approach. Christof Naumzik, Patrick Zoechbauer, Stefan Feuerriegel. WWW 2020. link
  8. A Category-Aware Deep Model for Successive POI Recommendation on Sparse Check-in Data. Fuqiang Yu, Lizhen Cui, Wei Guo, Xudong Lu, Qingzhong Li, Hua Lu. WWW 2020. link
  9. Twitter User Location Inference Based on Representation Learning and Label Propagation. Hechan Tian, Meng Zhang, Xiangyang Luo, Fenlin Liu, Yaqiong Qiao. WWW 2020.

2019

  1. Learning Travel Time Distributions with Deep Generative Model. Xiucheng Li, Gao Cong, Aixin Sun, Yun Cheng. WWW 2019. link
  2. R2SIGTP: a Novel Real-Time Recommendation System with Integration of Geography and Temporal Preference for Next Point-of-Interest. Xu Jiao, Yingyuan Xiao, Wenguang Zheng, Hongya Wang, Youzhi Jin. WWW 2019. link
  3. Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction. Huaxiu Yao, Yiding Liu, Ying Wei, Xianfeng Tang, Zhenhui Li. WWW 2019. link
  4. Predicting Human Mobility via Variational Attention. Qiang Gao, Fan Zhou, Goce Trajcevski, Kunpeng Zhang, Ting Zhong, Fengli Zhang. WWW 2019. link
  5. Context-aware Variational Trajectory Encoding and Human Mobility Inference. Fan Zhou, Xiaoli Yue, Goce Trajcevski, Ting Zhong, Kunpeng Zhang. WWW 2019. link
  6. Joint Modeling of Dense and Incomplete Trajectories for Citywide Traffic Volume Inference. Xianfeng Tang, Boqing Gong, Yanwei Yu, Huaxiu Yao, Yandong Li, Haiyong Xie, Xiaoyu Wang. WWW 2019. link

2018

  1. Spatio-Temporal Analysis for Smart City Data. Maria Bermudez-Edo, Payam Barnaghi. WWW 2018. link
  2. DeepMove: Predicting Human Mobility with Attentional Recurrent Networks. Feng, Jie and Li, Yong and Zhang, Chao and Sun, Funing and Meng, Fanchao and Guo, Ang and Jin, Depeng. WWW 2018. link

2017

  1. Regions, Periods, Activities: Uncovering Urban Dynamics via Cross-Modal Representation Learning. Chao Zhang, Keyang Zhang, Quan Yuan, Haoruo Peng, Yu Zheng, Tim Hanratty, Shaowen Wang, Jiawei Han. WWW 2017. link
  2. A General Model for Out-of-town Region Recommendation. Tuan-Anh Nguyen Pham, Xutao Li, Gao Cong. WWW 2017. link

2016 and before

  1. Exploiting Dining Preference for Restaurant Recommendation. Fuzheng Zhang, Nicholas Jing Yuan, Kai Zheng, Defu Lian, Xing Xie, Yong Rui. WWW 2016. link
  2. TribeFlow: Mining & Predicting User Trajectories. Flavio Figueiredo, Bruno Ribeiro, Jussara M. Almeida, Christos Faloutsos. WWW 2016. link
  3. Factorizing Personalized Markov Chains for Next-Basket Recommendation. Rendle, Steffen and Freudenthaler, Christoph and Schmidt-Thieme, Lars. WWW 2010. link

NIPS

2023

  1. GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks. Zhonghang Li(South China University of Technology), Lianghao Xia, Yong Xu, Chao Huang. NIPS 2023. link
  2. Taming Local Effects in Graph-based Spatiotemporal Forecasting. Andrea Cini(Università della Svizzera italiana), Ivan Marisca, Daniele Zambon, Cesare Alippi. NIPS 2023. link
  3. Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment. Yutong Xia(NUS), Yuxuan Liang, Haomin Wen, Xu Liu, Kun Wang, Zhengyang Zhou, Roger Zimmermann. NIPS 2023. link
  4. LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting. Xu Liu(NUS), Yutong Xia, Yuxuan Liang, Junfeng Hu, Yiwei Wang, Lei Bai, Chao Huang, Zhenguang Liu, Bryan Hooi, Roger Zimmermann. NIPS 2023. link
  5. OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning. Cheng Tan(ZJU), Siyuan Li, Zhangyang Gao, Wenfei Guan, Zedong Wang, Zicheng Liu, Lirong Wu, Stan Z. Li. NIPS 2023. link
  6. Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis. Abhinav Nippani(Northeastern University), Dongyue Li, Haotian Ju, Haris Koutsopoulos, Hongyang Zhang. NIPS 2023. link

2022

  1. Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models. Fan Liu(HKUST), Hao Liu, Wenzhao Jiang. NIPS 2022. link
  2. AZ-whiteness test: a test for signal uncorrelation on spatio-temporal graphs. Daniele Zambon(Università della Svizzera italiana), Cesare Alippi. NIPS 2022. link
  3. AutoST: Towards the Universal Modeling of Spatio-temporal Sequences. Jianxin Li(BUAA), Shuai Zhang, Hui Xiong, Haoyi Zhou. NIPS 2022. link
  4. SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction. Minhao Liu(CUHK), Ailing Zeng, Muxi Chen, Zhijian Xu, Qiuxia Lai, Lingna Ma, Qiang Xu. NIPS 2022. link
  5. FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting. Tian Zhou(Alibaba Group), Ziqing Ma, xue wang, Qingsong Wen, Liang Sun, Tao Yao, Wotao Yin, Rong Jin. NIPS 2022. link
  6. Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks. Yijing Liu(ZJU), Qinxian Liu, Jian-Wei Zhang, Haozhe Feng, Zhongwei Wang, Zihan Zhou, Wei Chen. NIPS 2022. link
  7. Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement. Yan Li( Baidu Research), Xinjiang Lu, Yaqing Wang, Dejing Dou. NIPS 2022. link

2021

  1. Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting. Haixu Wu(THU), Jiehui Xu, Jianmin Wang, Mingsheng Long. NIPS 2021. link

2020

  1. EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning. Jiachen Li, Fan Yang, Masayoshi Tomizuka, Chiho Choi. NIPS 2020. link
  2. Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning. Younggyo Seo, Kimin Lee, Ignasi Clavera Gilaberte, Thanard Kurutach, Jinwoo Shin, Pieter Abbeel. NIPS 2020.
  3. Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning. Pan Li, Yanbang Wang, Hongwei Wang, Jure Leskovec. NIPS 2020.
  4. Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing. Arthur Delarue, Ross Anderson, Christian Tjandraatmadja. NIPS 2020.
  5. Adaptive Probing Policies for Shortest Path Routing. Aditya Bhaskara, Sreenivas Gollapudi, Kostas Kollias, Kamesh Munagala. NIPS 2020.
  6. AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control. Afshin Oroojlooy, MohammadReza Nazari, Davood Hajinezhad, Jorge Silva. NIPS 2020. link
  7. Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. Lei Bai, Lina Yao, Can Li, Xianzhi Wang, Can Wang. NIPS 2020. link
  8. Multi-agent Trajectory Prediction with Fuzzy Query Attention. Nitin Kamra, Hao Zhu, Dweep Kumarbhai Trivedi, Ming Zhang, Yan Liu. NIPS 2020. link
  9. Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting. Defu Cao(PKU), Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Conguri Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, Qi Zhang. NIPS 2020. link

2019

  1. STREETS: A Novel Camera Network Dataset for Traffic Flow. Corey Snyder, Minh Do. NIPS 2019. link

2017

  1. PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs. Yunbo Wang, Mingsheng Long, Jianmin Wang, Zhifeng Gao, Philip S. Yu. NIPS 2017. link

ICLR

2024

  1. TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts. Hyunwook Lee(Ulsan National Institute of Science and Technology), Sungahn Ko. ICLR 2024. link
  2. Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation. Yuan Yuan(THU), Chenyang Shao, Jingtao Ding, Depeng Jin, Yong Li. ICLR 2024. link
  3. Biased Temporal Convolution Graph Network for Time Series Forecasting with Missing Values. Xiaodan Chen(HIT), Xiucheng Li, Bo Liu, Zhijun Li. ICLR 2024. link
  4. STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction. Dennis Wu(Northwestern University), Jerry Yao-Chieh Hu, Weijian Li, Bo-Yu Chen, Han Liu. ICLR 2024. link
  5. TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting. Shiyu Wang(Ant Group), Haixu Wu, Xiaoming Shi, Tengge Hu, Huakun Luo, Lintao Ma, James Y. Zhang, Jun Zhou. ICLR 2024. link

2023

  1. SimST: A GNN-Free Spatio-Temporal Learning Framework for Traffic Forecasting. Xu Liu(NUS), Yuxuan Liang, Chao Huang, Hengchang Hu, Yushi Cao, Bryan Hooi, Roger Zimmermann. ICLR 2023. link
  2. Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting. Yunhao Zhang(SJTU), Junchi Yan. ICLR 2023. link
  3. Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting. Mohammad Amin Shabani, Amir H. Abdi, Lili Meng, Tristan Sylvain. ICLR 2023. link
  4. MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting. Huiqiang Wang(Sichuan University), Jian Peng, Feihu Huang, Jince Wang, Junhui Chen, Yifei Xiao. ICLR 2023. link
  5. A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. Yuqi Nie(Princeton University), Nam H Nguyen, Phanwadee Sinthong, Jayant Kalagnanam. ICLR 2023. link
  6. Learning Fast and Slow for Online Time Series Forecasting. Quang Pham(Salesforce Research Asia), Chenghao Liu, Doyen Sahoo, Steven Hoi. ICLR 2023. link
  7. Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms. Linbo Liu(AWS AI Labs), Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Jun Huan. ICLR 2023. link
  8. Temporal Dependencies in Feature Importance for Time Series Prediction. Kin Kwan Leung(Layer 6 AI), Clayton Rooke, Jonathan Smith, Saba Zuberi, Maksims Volkovs. ICLR 2023. link

2022

  1. Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting. Hyunwook Lee(Ulsan National Institute of Science and Technology), Seungmin Jin, Hyeshin Chu, Hongkyu Lim, Sungahn Ko. ICLR 2022. link
  2. TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting. Yuzhou Chen(Princeton University), Ignacio Segovia-Dominguez, Baris Coskunuzer, Yulia Gel. ICLR 2022. link
  3. CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting. Gerald Woo(Salesforce Research Asia), Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi. ICLR 2022. link
  4. Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting. Shizhan Liu(Ant Group), Hang Yu, Cong Liao, Jianguo Li, Weiyao Lin, Alex X. Liu, Schahram Dustdar. ICLR 2022. link

2021

  1. Learning a Latent Search Space for Routing Problems using Variational Autoencoders. André Hottung, Bhanu Bhandari, Kevin Tierney. ICLR 2021.
  2. Trajectory Prediction using Equivariant Continuous Convolution. Robin Walters, Jinxi Li, Rose Yu. ICLR 2021.
  3. HalentNet: Multimodal Trajectory Forecasting with Hallucinative Intents. Deyao Zhu, Mohamed Zahran, Li Erran Li, Mohamed Elhoseiny. ICLR 2021.
  4. Discrete Graph Structure Learning for Forecasting Multiple Time Series. Chao Shang, Jie Chen, Jinbo Bi. ICLR 2021. link
  5. Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows. Kashif Rasul(Zalando Research), Abdul-Saboor Sheikh, Ingmar Schuster, Urs Bergmann, Roland Vollgraf. ICLR 2021. [link](Kashif Rasul, Abdul-Saboor Sheikh, Ingmar Schuster, Urs Bergmann, Roland Vollgraf)
  6. Multi-Time Attention Networks for Irregularly Sampled Time Series. Satya Narayan Shukla(University of Massachusetts Amherst), Benjamin M. Marlin. ICLR 2021. link
  7. Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding. Sana Tonekaboni(University of Toronto), Danny Eytan, Anna Goldenberg. ICLR 2021. link

2020

  1. A Learning-based Iterative Method for Solving Vehicle Routing Problems. Hao Lu, Xingwen Zhang, Shuang Yang. ICLR 2020.
  2. Diverse Trajectory Forecasting with Determinantal Point Processes. Ye Yuan, Kris M. Kitani. ICLR 2020.
  3. Diverse Trajectory Forecasting with Determinantal Point Processes. Ye Yuan, Kris M. Kitani. ICLR 2020. link

2018

  1. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu. ICLR 2018. link

ICML

2023

  1. Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation. Qianru Zhang(HKU), Chao Huang, Lianghao Xia, Zheng Wang, Siu Ming Yiu, Ruihua Han. ICML 2023. link

2022

  1. DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting. Shiyong Lan(Sichuan University), Yitong Ma, Weikang Huang, Wenwu Wang, Hongyu Yang, Pyang Li. ICML 2022. link
  2. FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting. Tian Zhou(Alibaba Group), Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, Rong Jin. ICML 2022. link
  3. Domain Adaptation for Time Series Forecasting via Attention Sharing. Xiaoyong Jin(AWS AI Labs), Youngsuk Park, Danielle C. Maddix, Hao Wang, Yuyang Wang. ICML 2022. link
  4. Utilizing Expert Features for Contrastive Learning of Time-Series Representations. Manuel Nonnenmacher(Robert Bosch GmbH), Lukas Oldenburg, Ingo Steinwart, David Reeb. ICML 2022. link
  5. Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion. Ling Yang(PKU), Shenda Hong. ICML 2022. link

2021

  1. Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting. Yuzhou Chen(Southern Methodist University), Ignacio Segovia-Dominguez, Yulia R. Gel. ICML 2021. link
  2. Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting. Kashif Rasul(Zalando Research), Calvin Seward, Ingmar Schuster, Roland Vollgraf. ICML 2021. link

ICDE

2023

  1. Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting. Yusheng Zhao(PKU), Xiao Luo, Wei Ju, Chong Chen, Xian-Sheng Hua, Ming Zhang. ICDE 2023. link
  2. When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks. Yuchen Fang(BUPT), Yanjun Qin, Haiyong Luo, Fang Zhao, Bingbing Xu, Liang Zeng, Chenxing Wang. ICDE 2023. link
  3. Self-Supervised Spatial-Temporal Bottleneck Attentive Network for Efficient Long-term Traffic Forecasting. Shengnan Guo(BJTU), Letian Gong, Chenyun Wang, Zeyu Zhou, Zekai Shen, Yiheng Huang, Youfang Lin, Huaiyu Wan. ICDE 2023. link
  4. Uncertainty Quantification for Traffic Forecasting: A Unified Approach. Weizhu Qian(Aalborg University), Dalin Zhang, Yan Zhao, Kai Zheng, James J.Q. Yu. ICDE 2023. link
  5. ROI-demand Traffic Prediction: A Pre-train, Query and Fine-tune Framework. Weijie Shi(Soochow University), Jiajie Xu, Junhua Fang, Pingfu Chao, An Liu, Xiaofang Zhou. ICDE 2023. link

2022

  1. A Data-Driven Spatial-Temporal Graph Neural Network for Docked Bike Prediction. Guanyao Li(HKUST), Xiaofeng Wang, Gunarto Sindoro Njoo, Shuhan Zhong, S.-H. Gary Chan, Chih-chieh hung, Wen-Chih Peng. ICDE 2022. link
  2. Towards Spatio-Temporal Aware Traffic Time Series Forecasting. Razvan-Gabriel Cirstea(Aalborg University), Bin Yang, Chenjuan Guo, Tung Kieu, Shirui Pan. ICDE 2022. link
  3. APOTS: A Model for Adversarial Prediction of Traffic Speed. Namhyuk Kim(Hyundai Motor Company), Junho Song, Siyoung Lee, Jaewon Choe, Kyungsik Han, Sunghwan Park, Sang-Wook Kim. ICDE 2022. link

2021

  1. Modeling Citywide Crowd Flows using Attentive Convolutional LSTM. Chi Harold Liu(BIT), Chengzhe Piao, Xiaoxin Ma, Ye Yuan, Jian Tang, Guoren Wang, Kin K. Leung. ICDE 2021. link
  2. An Effective Joint Prediction Model for Travel Demands and Traffic Flows. Haitao Yuan(THU), Guoliang Li, Zhifeng Bao, Ling Feng. ICDE 2021. link

2020

  1. Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network. Hongzhi Shi(THU), Quanming Yao, Qi Guo, Yaguang Li, Lingyu Zhang, Jieping Ye, Yong Li, Yan Liu. ICDE 2020. link
  2. Stochastic Origin-Destination Matrix Forecasting Using Dual-Stage Graph Convolutional, Recurrent Neural Networks. Jilin Hu(Inception Institute of Artificial Intellegence), Bin Yang, Chenjuan Guo, Christian S. Jensen, Hui Xiong. ICDE 2020. link

2019 and before

  1. Personalized route recommendation using big trajectory data. J Dai, B Yang, C Guo, Z Ding. ICDE 2015. link
  2. Discovering popular routes from trajectories. Z Chen, HT Shen, X Zhou. ICDE 2011. link

SIGSPATIAL

2022

  1. When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?. Xu Liu(NUS), Yuxuan Liang, Chao Huang, Yu Zheng, Bryan Hooi, Roger Zimmermann. SIGSPATIAL 2022. link

2021

  1. Hierarchical Neural Architecture Search for Travel Time Estimation. Guangyin Jin, Huan Yan, Fuxian Li, Yong Li, Jincai Huang. SIGSPATIAL 2021.
  2. Complementary Fusion of Deep Network and Tree Model for ETA Prediction. YuRui Huang, Jie Zhang, HengDa Bao, Yang Yang, Jian Yang. SIGSPATIAL 2021.
  3. Travel Time Estimation Based on Neural Network with Auxiliary Loss. Yunchong Gan, Haoyu Zhang, Mingjie Wang. SIGSPATIAL 2021.
  4. Multi-View Spatial-Temporal Model for Travel Time Estimation. Zichuan Liu, Zhaoyang Wu, Meng Wang, Rui Zhang. SIGSPATIAL 2021.
  5. Hierarchical Positional Approach for ETA Prediction. Tomoki Saito, Shinichi Tanimoto, Fumihiko Takahashi. SIGSPATIAL 2021.
  6. Vehicle Trajectory Recovery on Road Network Based on Traffic Camera Video Data. Zongyu Lin, Guozhen Zhang, Zhiqun He, Jie Feng, Wei Wu, Yong Li. SIGSPATIAL 2021.
  7. Tiering in Contraction and Edge Hierarchies for Stochastic Route Planning. Payas Rajan, Chinya V. Ravishankar. SIGSPATIAL 2021.
  8. Online Route Replanning for Scalable System-Optimal Route Planning. Robert J. Fitzgerald, Farnoush Banaei Kashani. SIGSPATIAL 2021.
  9. Robust Routing Using Electrical Flows. Ali Kemal Sinop, Lisa Fawcett, Sreenivas Gollapudi, Kostas Kollias. SIGSPATIAL 2021.
  10. Route Reconstruction from Traffic Flow via Representative Trajectories. Bram Custers, Wouter Meulemans, Bettina Speckmann, Kevin Verbeek. SIGSPATIAL 2021.
  11. Integration Model for Estimated Time of Arriva. Xuewei Guo, Shenglong Zhang. SIGSPATIAL 2021.
  12. Estimated Time of Arrival Prediction via Modeling the Spatial-Temporal Interactions between Links and Crosses. Xiaowei Mao, Tianyue Cai, Wenchuang Peng, Huaiyu Wan. SIGSPATIAL 2021.
  13. Geo-Attention Network for Traffic Condition Prediction and Travel Time Estimation. Jiezhang Li, Wanyi Zhou, Zebin Chen, Yue-Jiao Gong. SIGSPATIAL 2021.

2020

  1. Graph Convolutional Networks with Kalman Filtering for Traffic Prediction. Fanglan Chen, Zhiqian Chen, Subhodip Biswas, Shuo Lei, Naren Ramakrishnan, Chang-Tien Lu. SIGSPATIAL 2020. link
  2. DualSIN: Dual Sequential Interaction Network for Human Intentional Mobility Prediction. Quanjun Chen, Renhe Jiang, Chuang Yang, Zekun Cai, Zipei Fan, Kota Tsubouchi, Ryosuke Shibasaki, Xuan Song. SIGSPATIAL 2020. link
  3. Predicting Human Mobility with Federated Learning. Anliang Li, Shuang Wang, Wenzhu Li, Shengnan Liu, Siyuan Zhang. SIGSPATIAL 2020. link
  4. Enabling Finer Grained Place Embeddings using Spatial Hierarchy from Human Mobility Trajectories. Toru Shimizu, Takahiro Yabe, Kota Tsubouchi. SIGSPATIAL 2020.
  5. Predictive Collision Management for Time and Risk Dependent Path Planning. Carsten Hahn, Sebastian Feld, Hannes Schroter. SIGSPATIAL 2020.
  6. Learning Behavioral Representations of Human Mobility. Maria Luisa Damiani, Andrea Acquaviva, Fatima Hachem, Matteo Rossini. SIGSPATIAL 2020.

2019

  1. Context-based Markov Model toward Spatio-Temporal Prediction with Realistic Dataset. Kota Tsubouchi, Tomoki Saito, Masamichi Shimosaka. SIGSPATIAL 2019. link
  2. Traffic speed prediction with convolutional neural network adapted for non-linear spatio-temporal dynamics. Shen Ren, Bo Yang, Liye Zhang, Zengxiang Li. SIGSPATIAL 2019. link
  3. Predicting traffic accidents with event recorder data. oshiaki Takimoto, Yusuke Tanaka, Takeshi Kurashima, Shuhei Yamamoto, Maya Okawa, Hiroyuki Toda. SIGSPATIAL 2019. link
  4. FairST: Equitable Spatial and Temporal Demand Prediction for New Mobility Systems. An Yan, Bill Howe. SIGSPATIAL 2019. link

2018

  1. Bike flow prediction with multi-graph convolutional networks. Di Chai, Leye Wang, Qiang Yang. SIGSPATIAL 2018. link
  2. A Seq2Seq learning approach for modeling semantic trajectories and predicting the next location. Antonios Karatzoglou, Adrian Jablonski, Michael Beigl. SIGSPATIAL 2018. link

2017

  1. Urban Travel Time Prediction using a Small Number of GPS Floating Cars. Yang Li, Dimitrios Gunopulos, Cewu Lu, Leonidas Guibas. SIGSPATIAL 2017. link

2016 and before

  1. FCCF: forecasting citywide crowd flows based on big data. Minh X. Hoang, Yu Zheng, Ambuj K. Singh. SIGSPATIAL 2016. link
  2. DNN-based prediction model for spatio-temporal data. Junbo Zhang, Yu Zheng, Dekang Qi, Ruiyuan Li, Xiuwen Yi. SIGSPATIAL 2016. link
  3. Personalized route planning in road networks. S Funke, S Storandt. SIGSPATIAL 2015. link

IEEE TKDE

2024

  1. Towards a Unified Understanding of Uncertainty Quantification in Traffic Flow Forecasting. Weizhu Qian(Soochow University), Yan Zhao, Dalin Zhang, Bowei Chen, Kai Zheng, Xiaofang Zhou. IEEE TKDE 2024. link
  2. CityTrans: Domain-Adversarial Training With Knowledge Transfer for Spatio-Temporal Prediction Across Cities. Xiaocao Ouyang(Southwest Jiaotong University), Yan Yang, Wei Zhou, Yiling Zhang, Hao Wang, Wei Huang. IEEE TKDE 2024. link
  3. Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting. Chuanpan Zheng(Xiamen University), Xiaoliang Fan, Shirui Pan, Haibing Jin, Zhaopeng Peng, Zonghan Wu, Cheng Wang, Philip S. Yu. IEEE TKDE 2024. link

2023

  1. Traffic Accident Risk Prediction via Multi-View Multi-Task Spatio-Temporal Networks. Senzhang Wang(Central South University), Jiaqiang Zhang, Jiyue Li, Hao Miao, Jiannong Cao. IEEE TKDE 2023. link
  2. Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction. Peng Xie(Southwest Jiaotong University), Minbo Ma, Tianrui Li, Shenggong Ji, Shengdong Du, Zeng Yu, Junbo Zhang. IEEE TKDE 2023. link
  3. A Lightweight and Accurate Spatial-Temporal Transformer for Traffic Forecasting. Guanyao Li(HKUST), Shuhan Zhong, Xingdong Deng, Letian Xiang, S.-H. Gary Chan, Ruiyuan Li, Yang Liu, Ming Zhang, Chih-Chieh Hung, Wen-Chih Peng. IEEE TKDE 2023. link
  4. Cross-City Multi-Granular Adaptive Transfer Learning for Traffic Flow Prediction. Jiqian Mo(University of Macau), Zhiguo Gong. IEEE TKDE 2023. link
  5. Traffic Flow Prediction Based on Spatiotemporal Potential Energy Fields. Jingyuan Wang(BUAA), Jiahao Ji, Zhe Jiang, Leilei Sun. IEEE TKDE 2023. link
  6. DMGAN: Dynamic Multi-Hop Graph Attention Network for Traffic Forecasting. Rui Li(HIT), Fan Zhang, Tong Li, Ning Zhang, Tingting Zhang. IEEE TKDE 2023. link
  7. Forecasting Fine-Grained Urban Flows Via Spatio-Temporal Contrastive Self-Supervision. Hao Qu(Shandong University), Yongshun Gong, Meng Chen, Junbo Zhang, Yu Zheng, Yilong Yin. IEEE TKDE 2023. link
  8. A Graph and Attentive Multi-Path Convolutional Network for Traffic Prediction. Jianzhong Qi(University of Melbourne), Zhuowei Zhao, Egemen Tanin, Tingru Cui, Neema Nassir, Majid Sarvi. IEEE TKDE 2023. link
  9. STP-TrellisNets+: Spatial-Temporal Parallel TrellisNets for Multi-Step Metro Station Passenger Flow Prediction. Junjie Ou(SJTU), Jiahui Sun, Yichen Zhu, Haiming Jin, Yijuan Liu, Fan Zhang, Jianqiang Huang, Xinbing Wang. IEEE TKDE 2023. link
  10. ST-ExpertNet: A Deep Expert Framework for Traffic Prediction. Hongjun Wang(Southern University of Science and Technology), Jiyuan Chen, Zipei Fan, Zhiwen Zhang, Zekun Cai, Xuan Song. IEEE TKDE 2023. link
  11. Exploring the Generalizability of Spatio-Temporal Traffic Prediction: Meta-Modeling and an Analytic Framework. Leye Wang(PKU), Di Chai, Xuanzhe Liu, Liyue Chen, Kai Chen. IEEE TKDE 2023. link
  12. Modeling Spatial Nonstationarity via Deformable Convolutions for Deep Traffic Flow Prediction. Wei Zeng(CAS), Chengqiao Lin, Kang Liu, Juncong Lin, Anthony K. H. Tung. IEEE TKDE 2023. link
  13. Multivariate Correlation Matrix-Based Deep Learning Model With Enhanced Heuristic Optimization for Short-Term Traffic Forecasting. Shuai Zhang(Zhejiang University of Finance and Economics), Kun Zhu, Wenyu Zhang. IEEE TKDE 2023. link
  14. SOUP: Spatial-Temporal Demand Forecasting and Competitive Supply in Transportation. Bolong Zheng(Huazhong University of Science and Technology), Qi Hu, Lingfeng Ming, Jilin Hu, Lu Chen, Kai Zheng, Christian S. Jensen. IEEE TKDE 2023. link
  15. Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting. Ling Chen(ZJU), Donghui Chen, Zongjiang Shang, Binqing Wu, Cen Zheng, Bo Wen, Wei Zhang. IEEE TKDE 2023. link

2022

  1. Mixed-Order Relation-Aware Recurrent Neural Networks for Spatio-Temporal Forecasting. Yuxuan Liang(NUS), Kun Ouyang, Yiwei Wang, Zheyi Pan, Yifang Yin, Hongyang Chen, Junbo Zhang, Yu Zheng, David S. Rosenblum, Roger Zimmermann. IEEE TKDE 2023. link
  2. Traffic Anomaly Prediction Based on Joint Static-Dynamic Spatio-Temporal Evolutionary Learning. Xiaoming Liu(Xi’an Jiaotong University), Zhanwei Zhang, Lingjuan Lyu, Zhaohan Zhang, Shuai Xiao, Chao Shen, Philip S. Yu. IEEE TKDE 2022. link
  3. STP-TrellisNets+: Spatial-Temporal Parallel TrellisNets for Multi-Step Metro Station Passenger Flow Prediction. Junjie Ou(SJTU), Jiahui Sun, Yichen Zhu, Haiming Jin, Yijuan Liu, Fan Zhang, Jianqiang Huang, Xinbing Wang. IEEE TKDE 2022. link
  4. Forecasting Fine-Grained Urban Flows Via Spatio-Temporal Contrastive Self-Supervision. Hao Qu(Shandong University), Yongshun Gong, Meng Chen, Junbo Zhang, Yu Zheng, Yilong Yin. IEEE TKDE 2022. link
  5. Traffic Flow Prediction Based on Spatiotemporal Potential Energy Fields. Jingyuan Wang(BUAA), Jiahao Ji, Zhe Jiang, Leilei Sun. IEEE TKDE 2022. link
  6. DMGAN: Dynamic Multi-Hop Graph Attention Network for Traffic Forecasting. Rui Li(HIT), Fan Zhang, Tong Li, Ning Zhang, Tingting Zhang. IEEE TKDE 2022. link
  7. A Graph and Attentive Multi-Path Convolutional Network for Traffic Prediction. Jianzhong Qi(University of Melbourne), Zhuowei Zhao, Egemen Tanin, Tingru Cui, Neema Nassir, Majid Sarvi. IEEE TKDE 2022. link
  8. ST-ExpertNet: A Deep Expert Framework for Traffic Prediction. Hongjun Wang(Southern University of Science and Technology), Jiyuan Chen, Zipei Fan, Zhiwen Zhang, Zekun Cai, Xuan Song. IEEE TKDE 2022. link
  9. A Multi-View Multi-Task Learning Framework for Multi-Variate Time Series Forecasting. Jinliang Deng(Southern University of Science and Technology), Xiusi Chen, Renhe Jiang, Xuan Song, Ivor W. Tsang. IEEE TKDE 2022. link
  10. Multivariate Time Series Forecasting With Dynamic Graph Neural ODEs. Ming Jin(Monash University), Yu Zheng, Yuan-Fang Li, Siheng Chen, Bin Yang, Shirui Pan. IEEE TKDE 2022. link

2021

  1. Deep Trajectory Recovery with Fine-Grained Calibration using Kalman Filter. Jingyuan Wang, Ning Wu, Xinxi Lu, Wayne Xin Zhao, Kai Feng. IEEE TKDE 2021.
  2. Route Recommendations for Intelligent Transportation Services. Yong Ge, Huayu Li, Alexander Tuzhilin. IEEE TKDE 2021.
  3. Multi-Level Attention Networks for Multi-Step Citywide Passenger Demands Prediction. Xian Zhou, Yanyan Shen, Linpeng Huang, Tianzi Zang, Yanmin Zhu. IEEE TKDE 2021. link
  4. Predicting Taxi and Uber Demand in Cities: Approaching the Limit of Predictability. Kai Zhao, Denis Khryashchev, Huy T. Vo. IEEE TKDE 2021. link
  5. Forecasting Gathering Events through Trajectory Destination Prediction: A Dynamic Hybrid Model. Amin Vahedian Khezerlou, Xun Zhou, Ling Tong, Yanhua Li, Jun Luo. IEEE TKDE 2021. link
  6. TIPS: Mining Top-K Locations to Minimize User-Inconvenience for Trajectory-Aware Services. Shubhadip Mitra, Priya Saraf, Arnab Bhattacharya. IEEE TKDE 2021. link
  7. Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting. Shengnan Guo(BJTU), Youfang Lin, Huaiyu Wan, Xiucheng Li, Gao Cong. IEEE TKDE 2021. link
  8. DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction. Renhe Jiang(The University of Tokyo), Zekun Cai, Zhaonan Wang, Chuang Yang, Zipei Fan, Quanjun Chen, Kota Tsubouchi, Xuan Song, Ryosuke Shibasaki. IEEE TKDE 2021. link
  9. Modeling Spatial Nonstationarity via Deformable Convolutions for Deep Traffic Flow Prediction. Wei Zeng(CAS), Chengqiao Lin, Kang Liu, Juncong Lin, Anthony K. H. Tung. IEEE TKDE 2021. link
  10. Dynamic Auto-Structuring Graph Neural Network: A Joint Learning Framework for Origin-Destination Demand Prediction. Dapeng Zhang(Southwestern University of Finance and Economics), Feng Xiao. IEEE TKDE 2021. link

2020

  1. An Efficient Destination Prediction Approach Based on Future Trajectory Prediction and Transition Matrix Optimization. Zhou Yang, Heli Sun, Jianbin Huang, Zhongbin Sun, Hui Xiong, Shaojie Qiao, Ziyu Guan, Xiaolin Jia. IEEE TKDE 2020. link
  2. BRIGHT—Drift-Aware Demand Predictions for Taxi Networks. Amal Saadallah, Luís Moreira-Matias, Ricardo Sousa, Jihed Khiari, Erik Jenelius, João Gama. IEEE TKDE 2020. link
  3. Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning. Junbo Zhang, Yu Zheng, Junkai Sun, Dekang Qi. IEEE TKDE 2020. link
  4. Citywide Bike Usage Prediction in a Bike-Sharing System. Yexin Li, Yu Zheng. IEEE TKDE 2020. link
  5. A Joint Two-Phase Time-Sensitive Regularized Collaborative Ranking Model for Point of Interest Recommendation. Mohammad Aliannejadi, Dimitrios Rafailidis, Fabio Crestani. IEEE TKDE 2020. link
  6. Spatio-Temporal Meta Learning for Urban Traffic Prediction. Zheyi Pan(SJTU), Wentao Zhang, Yuxuan Liang, Weinan Zhang, Yong Yu, Junbo Zhang, Yu Zheng. IEEE TKDE 2020. link
  7. Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks. Junkai Sun(JD Intelligent Cities Research), Junbo Zhang, Qiaofei Li, Xiuwen Yi, Yuxuan Liang, Yu Zheng. IEEE TKDE 2020. link
  8. Foresee Urban Sparse Traffic Accidents: A Spatiotemporal Multi-Granularity Perspective. Zhengyang Zhou(USTC), Yang Wang, Xike Xie, Lianliang Chen, Chaochao Zhu. IEEE TKDE 2020. link

2019

  1. Hierarchical Multi-Clue Modelling for POI Popularity Prediction with Heterogeneous Tourist Information. Yang Yang, Yaqian Duan, Xinze Wang, Zi Huang, Ning Xie, Heng Tao Shen. IEEE TKDE 2019. link
  2. Representing Urban Forms: A Collective Learning Model with Heterogeneous Human Mobility Data. Yanjie Fu, Guannan Liu, YOng Ge, Pengyang Wang, Hengshu Zhu, Chunxiao Li, Hui Xiong. IEEE TKDE 2019. link

2018

  1. Capturing the Spatiotemporal Evolution in Road Traffic Networks. Tarique Anwar, Chengfei Liu, Hai L. Vu, Md. Saiful Islam, Timos Sellis. IEEE TKDE 2018. link
  2. Road Traffic Speed Prediction: A Probabilistic Model Fusing Multi-Source Data. Lu Lin, Jianxin Li, Feng Chen, Jieping Ye, Jinpeng Huai. IEEE TKDE 2018. link

2017

  1. Citywide Traffic Volume Estimation Using Trajectory Data. Xianyuan Zhan, Yu Zheng, Xiuwen Yi, Satish V. Ukkusuri. IEEE TKDE 2017. link
  2. Trajectory Community Discovery and Recommendation by Multi-Source Diffusion Modeling. Siyuan Liu, Shuhui Wang. IEEE TKDE 2017. link
  3. Spatial-Aware Hierarchical Collaborative Deep Learning for POI Recommendation. Hongzhi Yin, Weiqing Wang, Hao Wang, Ling Chen, Xiaofang Zhou. IEEE TKDE 2017. link

2016 and before

  1. Adapting to User Interest Drift for POI Recommendation. Hongzhi Yin, Xiaofang Zhou, Bin Cui, Hao Wang, Kai Zheng, Quoc Viet Hung Nguyen. IEEE TKDE 2016. link
  2. A General Multi-Context Embedding Model for Mining Human Trajectory Data. Ningnan Zhou, Wayne Xin Zhao, Xiao Zhang, Ji-Rong Wen, Shan Wang. IEEE TKDE 2016. link
  3. Hierarchical Spatio-Temporal Pattern Discovery and Predictive Modeling. Chung-Hsien Yu, Wei Ding, Melissa Morabito, Ping Chen. IEEE TKDE 2016. link

IEEE TITS

2024

  1. A Variational Bayesian Inference-Based En-Decoder Framework for Traffic Flow Prediction. Jianlei Kong(Beijing Technology and Business University), Xiaomeng Fan, Xuebo Jin, Sen Lin, Min Zuo. IEEE TITS 2024. link
  2. Spatial–Temporal Traffic Modeling With a Fusion Graph Reconstructed by Tensor Decomposition. Qin Li(Guangxi University), Xuan Yang, Yong Wang, Yuankai Wu, Deqiang He. IEEE TITS 2024. link
  3. GMHANN: A Novel Traffic Flow Prediction Method for Transportation Management Based on Spatial-Temporal Graph Modeling. Qing Wang(Fujian Normal University), Weiping Liu, Xiumei Wang, Xinghong Chen, Guannan Chen, Qingxiang Wu. IEEE TITS 2024. link
  4. A Multi-Layer Model Based on Transformer and Deep Learning for Traffic Flow Prediction. He-Xuan Hu(Hohai University), Qiang Hu, Guoping Tan, Ye Zhang, Zhen-Zhou Lin. IEEE TITS 2024. link
  5. Digital Twin for Transportation Big Data: A Reinforcement Learning-Based Network Traffic Prediction Approach. Laisen Nie(Northwestern Polytechnical University), Xiaojie Wang, Qinglin Zhao, Zhigang Shang, Li Feng, Guojun Li. IEEE TITS 2024. link
  6. Observer-Informed Deep Learning for Traffic State Estimation With Boundary Sensing. Chenguang Zhao(HKUST), Huan Yu. IEEE TITS 2024. link

2023

  1. Tensor Extended Kalman Filter and its Application to Traffic Prediction. Shih Yu Chang(San Jose State University), Hsiao-Chun Wu, Yi-Chih Kao. IEEE TITS 2023. link
  2. PFNet: Large-Scale Traffic Forecasting With Progressive Spatio-Temporal Fusion. Chen Wang(Anhui Normal University), Kaizhong Zuo, Shaokun Zhang, Hanwen Lei, Peng Hu, Zhangyi Shen, Rui Wang, Peize Zhao. IEEE TITS 2023. link
  3. 2F-TP: Learning Flexible Spatiotemporal Dependency for Flexible Traffic Prediction. Jie Zhao(Chongqing University), Chao Chen, Chengwu Liao, Hongyu Huang, Jie Ma, Huayan Pu, Jun Luo, Tao Zhu, Shilong Wang. IEEE TITS 2023. link
  4. Privacy-Preserving Cross-Area Traffic Forecasting in ITS: A Transferable Spatial-Temporal Graph Neural Network Approach. Yuxin Qi(SJTU), Jun Wu, Ali Kashif Bashir, Xi Lin, Wu Yang, Mohammad Dahman Alshehri. IEEE TITS 2023. link
  5. A Neural Network Based on Spatial Decoupling and Patterns Diverging for Urban Rail Transit Ridership Prediction. Yong Luo(Soochow University), Jianying Zheng, Xiang Wang, Yanyun Tao, Xingxing Jiang. IEEE TITS 2023. link
  6. An Embedding-Driven Multi-Hop Spatio-Temporal Attention Network for Traffic Prediction. Rui Xue(Tongji University), Shengjie Zhao, Fengxia Han. IEEE TITS 2023. link
  7. GraphSAGE-Based Dynamic Spatial–Temporal Graph Convolutional Network for Traffic Prediction. Tao Liu(Hohai University), Aimin Jiang, Jia Zhou, Min Li, Hon Keung Kwan. IEEE TITS 2023. link
  8. Traffic Prediction With Transfer Learning: A Mutual Information-Based Approach. Yunjie Huang(Southern University of Science and Technology), Xiaozhuang Song, Yuanshao Zhu, Shiyao Zhang, James J. Q. Yu. IEEE TITS 2023. link
  9. Transfer Learning With Spatial–Temporal Graph Convolutional Network for Traffic Prediction. Zhixiu Yao(Chongqing University of Posts and Telecommunications), Shichao Xia, Yun Li, Guangfu Wu, Linli Zuo. IEEE TITS 2023. link
  10. Fast Spatiotemporal Learning Framework for Traffic Flow Forecasting. Canyang Guo(Fuzhou University), Chi-Hua Chen, Feng-Jang Hwang, Ching-Chun Chang, Chin-Chen Chang. IEEE TITS 2023. link
  11. Spatial-Temporal Position-Aware Graph Convolution Networks for Traffic Flow Forecasting. Yiji Zhao(BJTU), Youfang Lin, Haomin Wen, Tonglong Wei, Xiyuan Jin, Huaiyu Wan. IEEE TITS 2023. link
  12. A Deep Learning Approach for Long-Term Traffic Flow Prediction With Multifactor Fusion Using Spatiotemporal Graph Convolutional Network. Xiaoyu Qi(China University of Geosciences), Gang Mei, Jingzhi Tu, Ning Xi, Francesco Piccialli. IEEE TITS 2023. link
  13. Graph Attention Network With Spatial-Temporal Clustering for Traffic Flow Forecasting in Intelligent Transportation System. Yan Chen(Hunan University of Technology and Business), Tian Shu, Xiaokang Zhou, Xuzhe Zheng, Akira Kawai, Kaoru Fueda, Zheng Yan, Wei Liang, Kevin I-Kai Wang. IEEE TITS 2023. link
  14. Traffic Speed Prediction Based on Time Classification in Combination With Spatial Graph Convolutional Network. Xiuqin Pan(Minzu University of China), Fei Hou, Sumin Li. IEEE TITS 2023. link
  15. Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction. Guangyin Jin(National University of Defense Technology), Fuxian Li, Jinlei Zhang, Mudan Wang, Jincai Huang. IEEE TITS 2023. link
  16. Knowledge Expansion and Consolidation for Continual Traffic Prediction With Expanding Graphs. Binwu Wang(USTC), Yudong Zhang, Jiahao Shi, Pengkun Wang, Xu Wang, Lei Bai, Yang Wang. IEEE TITS 2023. link
  17. Data-Driven Distance Metrics for Kriging-Short-Term Urban Traffic State Prediction. Balázs Varga(Budapest University of Technology and Economics), Mike Pereira, Balázs Kulcsár, Luigi Pariota, Tamás Péni. IEEE TITS 2023. link
  18. Approximate Inference of Traffic Flow State at Signalized Intersections Using a Bayesian Learning Framework. Nan Zhang(SenseTime Research), Xiaoguang Yang, Haifeng Guo, Hongzhao Dong, Wanjing Ma. IEEE TITS 2023. link
  19. Low-Rank Hankel Tensor Completion for Traffic Speed Estimation. Xudong Wang(McGill University), Yuankai Wu, Dingyi Zhuang, Lijun Sun. IEEE TITS 2023. link
  20. Spatio-Temporal AutoEncoder for Traffic Flow Prediction. Mingzhe Liu(BUAA), Tongyu Zhu, Junchen Ye, Qingxin Meng, Leilei Sun, Bowen Du. IEEE TITS 2023. link
  21. Hierarchical Spatio–Temporal Graph Convolutional Networks and Transformer Network for Traffic Flow Forecasting. Guangyu Huo(Beijing University of Technology), Yong Zhang, Boyue Wang, Junbin Gao, Yongli Hu, Baocai Yin. IEEE TITS 2023. link
  22. A Novel STFSA-CNN-GRU Hybrid Model for Short-Term Traffic Speed Prediction. Changxi Ma(Lanzhou Jiaotong University), Yongpeng Zhao, Guowen Dai, Xuecai Xu, Sze-Chun Wong. IEEE TITS 2023. link
  23. Adaptive Spatiotemporal InceptionNet for Traffic Flow Forecasting. Yi Wang(PKU), Changfeng Jing, Wei Huang, Shiyuan Jin, Xinxin Lv. IEEE TITS 2023. link
  24. Modeling Dynamic Traffic Flow as Visibility Graphs: A Network-Scale Prediction Framework for Lane-Level Traffic Flow Based on LPR Data. Jie Zeng(Central South University), Jinjun Tang. IEEE TITS 2023. link
  25. Traffic Prediction With Missing Data: A Multi-Task Learning Approach. Ao Wang(Southern University of Science and Technology), Yongchao Ye, Xiaozhuang Song, Shiyao Zhang, James J. Q. Yu. IEEE TITS 2023. link
  26. A Gaussian-Process-Based Data-Driven Traffic Flow Model and Its Application in Road Capacity Analysis. Zhiyuan Liu(Southeast University), Cheng Lyu, Zelin Wang, Shuaian Wang, Pan Liu, Qiang Meng. IEEE TITS 2023. link
  27. Bayesian Traffic State Estimation Using Extended Floating Car Data. Victor Kyriacou(University of Amsterdam), Yiolanda Englezou, Christos G. Panayiotou, Stelios Timotheou. IEEE TITS 2023. link
  28. Self-Supervised Spatiotemporal Graph Neural Networks With Self-Distillation for Traffic Prediction. Junzhong Ji(Beijing University of Technology), Fan Yu, Minglong Lei. IEEE TITS 2023. link
  29. Few-Sample Traffic Prediction With Graph Networks Using Locale as Relational Inductive Biases. Mingxi Li(The Hong Kong Polytechnic University), Yihong Tang, Wei Ma. IEEE TITS 2023. link
  30. Spatial–Temporal Tensor Graph Convolutional Network for Traffic Speed Prediction. Xuran Xu(Nanjing University of Science and Technology), Tong Zhang, Chunyan Xu, Zhen Cui, Jian Yang. IEEE TITS 2023. link
  31. Robust and Hierarchical Spatial Relation Analysis for Traffic Forecasting. Weifeng Zhang(PKU), Zhe Wu, Xinfeng Zhang, Guoli Song, Yaowei Wang, Jie Chen. IEEE TITS 2023. link
  32. M3AN: Multitask Multirange Multisubgraph Attention Network for Condition-Aware Traffic Prediction. Dan Luo(BUPT), Dong Zhao, Zijian Cao, Mingyao Wu, Liang Liu, Huadong Ma. IEEE TITS 2023. link
  33. ClusterST: Clustering Spatial–Temporal Network for Traffic Forecasting. Guiyang Luo(Xidian University), Hui Zhang, Quan Yuan, Jinglin Li, Fei-Yue Wang. IEEE TITS 2023. link
  34. A Data-Driven Spatio-Temporal Speed Prediction Framework for Energy Management of Connected Vehicles. Mohammad Reza Amini(University of Michigan), Qiuhao Hu, Ashley Wiese, Ilya Kolmanovsky, Julia Buckland Seeds, Jing Sun. IEEE TITS 2023. link
  35. AutoMSNet: Multi-Source Spatio-Temporal Network via Automatic Neural Architecture Search for Traffic Flow Prediction. Shen Fang(CAS), Chunxia Zhang, Shiming Xiang, Chunhong Pan. IEEE TITS 2023. link
  36. Spatial-Temporal Attention Graph Convolution Network on Edge Cloud for Traffic Flow Prediction. Qifeng Lai(Sun Yat-sen University), Jinyu Tian, Wei Wang, Xiping Hu. IEEE TITS 2023. link
  37. FedSTN: Graph Representation Driven Federated Learning for Edge Computing Enabled Urban Traffic Flow Prediction. Xiaoming Yuan(Northeastern University), Jiahui Chen, Jiayu Yang, Ning Zhang, Tingting Yang, Tao Han, Amir Taherkordi. IEEE TITS 2023. link
  38. Short-Term Traffic Flow Prediction Based on Graph Convolutional Networks and Federated Learning. Mengran Xia(Zhongnan University of Economics and Law), Dawei Jin, Jingyu Chen. IEEE TITS 2023. link
  39. Short-Term Traffic Flow Prediction of the Smart City Using 5G Internet of Vehicles Based on Edge Computing. Shenghan Zhou(BUAA), Chaofan Wei, Chaofei Song, Xing Pan, Wenbing Chang, Linchao Yang. IEEE TITS 2023. link
  40. MG-TAR: Multi-View Graph Convolutional Networks for Traffic Accident Risk Prediction. Patara Trirat(KAIST), Susik Yoon, Jae-Gil Lee. IEEE TITS 2023. link
  41. Urban Traffic Congestion Level Prediction Using a Fusion-Based Graph Convolutional Network. Rui Feng(Dalian University of Technology), Heqi Cui, Qiang Feng, Sixuan Chen, Xiaoning Gu, Baozhen Yao. IEEE TITS 2023. link

2022

  1. Spatial-Temporal Position-Aware Graph Convolution Networks for Traffic Flow Forecasting. Yiji Zhao(BJTU), Youfang Lin, Haomin Wen, Tonglong Wei, Xiyuan Jin, Huaiyu Wan. IEEE TITS 2022. link
  2. Traffic Inflow and Outflow Forecasting by Modeling Intra- and Inter-Relationship between Flows. Yiji Zhao(BJTU), Youfang Lin, Yongkai Zhang, Haomin Wen, Yunxiao Liu, Hao Wu, Zhihao Wu, Shuaichao Zhang, and Huaiyu Wan. IEEE TITS 2022. link
  3. Traffic-GGNN: Predicting Traffic Flow via Attentional Spatial-Temporal Gated Graph Neural Networks. Yang Wang(Southwest Petroleum University), Jin Zheng, Yuqi Du, Cheng Huang, Ping Li. IEEE TITS 2022. link
  4. Adaptive Spatiotemporal Dependence Learning for Multi-Mode Transportation Demand Prediction. Haihui Xu(Beijing Municipal Transportation Operations Coordination Center), Tao Zou, Mingzhe Liu, Yanan Qiao, Jingjing Wang, Xucheng Li. IEEE TITS 2022. link
  5. M3AN: Multitask Multirange Multisubgraph Attention Network for Condition-Aware Traffic Prediction. Dan Luo(BUPT), Dong Zhao, Zijian Cao, Mingyao Wu, Liang Liu, Huadong Ma. IEEE TITS 2022. link
  6. AutoMSNet: Multi-Source Spatio-Temporal Network via Automatic Neural Architecture Search for Traffic Flow Prediction. Shen Fang(CAS), Chunxia Zhang, Shiming Xiang, Chunhong Pan. IEEE TITS 2022. link
  7. ClusterST: Clustering Spatial–Temporal Network for Traffic Forecasting. Guiyang Luo(BUPT), Hui Zhang, Quan Yuan, Jinglin Li, Fei-Yue Wang. IEEE TITS 2022. link
  8. TGAE: Temporal Graph Autoencoder for Travel Forecasting. Qiang Wang(Wuhan University), Hao Jiang, Meikang Qiu, Yifeng Liu, Dongsheng Ye. IEEE TITS 2022. link

2021

  1. Joint Optimization of Running Route and Scheduling for the Mixed Demand Responsive Feeder Transit With Time-Dependent Travel Times. Zhengwu Wang, Jie Yu, Wei Hao, Jian Xiang. IEEE TITS 2021.
  2. Pedestrian Trajectory Prediction Based on Deep Convolutional LSTM Network. Xiao Song, Kai Chen, Xu Li, Jinghan Sun, Baocun Hou, Yong Cui, Baochang Zhang, Gang Xiong, Zilie Wang. IEEE TITS 2021.
  3. A Particle Filter-Based Approach for Vehicle Trajectory Reconstruction Using Sparse Probe Data. Lei Wei, Yunpeng Wang, Peng Chen. IEEE TITS 2021.
  4. Vehicle Trajectory Clustering Based on Dynamic Representation Learning of Internet of Vehicles. Wei Wang, Feng Xia, Hansong Nie, Zhikui Chen, Zhiguo Gong, Xiangjie Kong, Wei Wei. IEEE TITS 2021.
  5. Multi-Task Travel Route Planning With a Flexible Deep Learning Framework. Feiran Huang, Jie Xu, Jian Weng. IEEE TITS 2021.
  6. Iterative Local-Search Heuristic for Weighted Vehicle Routing Problem. Xinyu Wang, Shuai Shao, Jiafu Tang. IEEE TITS 2021.
  7. Urban Traffic Route Guidance Method With High Adaptive Learning Ability Under Diverse Traffic Scenarios. Chuanhui Tang, Wenbin Hu, Simon Hu, Marc E. J. Stettler. IEEE TITS 2021.
  8. Pareto-Optimal Transit Route Planning With Multi-Objective Monte-Carlo Tree Search. Di Weng, Ran Chen, Jianhui Zhang, Jie Bao, Yu Zheng, Yingcai Wu. IEEE TITS 2021.
  9. Multiobjective Mission Route Planning Problem: A Neural Network-Based Forecasting Model for Mission Planning. Sumana Biswas, Sreenatha G. Anavatti, Matthew A. Garratt. IEEE TITS 2021.
  10. Modeling Time-Varying Variability and Reliability of Freeway Travel Time Using Functional Principal Component Analysis. Jeng-Min Chiou, Han-Tsung Liou, Wan-Hui Chen. IEEE TITS 2021.
  11. Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction. Kan Guo, Yongli Hu, Sean Qian, Hao Liu, Ke Zhang, Yanfeng Sun, Junbin Gao, Baocai Yin. IEEE TITS 2021. link
  12. Traffic Demand Prediction Based on Dynamic Transition Convolutional Neural Network. Bowen Du, Xiao Hu, Leilei Sun, Junming Liu, Yanan Qiao, Weifeng Lv. IEEE TITS 2021. link
  13. Predicting Short-Term Traffic Speed Using a Deep Neural Network to Accommodate Citywide Spatio-Temporal Correlations. Yongjin Lee, Hyunjeong Jeon, Keemin Sohn. IEEE TITS 2021. link
  14. Predicting Citywide Road Traffic Flow Using Deep Spatiotemporal Neural Networks. Tao Jia, Penggao Yan. IEEE TITS 2021. link
  15. Daily Traffic Flow Forecasting Through a Contextual Convolutional Recurrent Neural Network Modeling Inter- and Intra-Day Traffic Patterns. Dongfang Ma, Xiang Song, Pu Li. IEEE TITS 2021. link
  16. Predicting Bus Passenger Flow and Prioritizing Influential Factors Using Multi-Source Data: Scaled Stacking Gradient Boosting Decision Trees. Weitiao Wu, Yisong Xia, Wenzhou Jin. IEEE TITS 2021. link
  17. Short-Term Traffic Flow Prediction With Wavelet and Multi-Dimensional Taylor Network Model. Shanliang Zhu, Yu Zhao, Yanjie Zhang, Qingling Li, Wenwu Wang, Shuguo Yang. IEEE TITS 2021. link
  18. Speed Prediction Based on a Traffic Factor State Network Model. Weibin Zhang, Yaoyao Feng, Kai Lu, Yuhang Song, Yinhai Wang. IEEE TITS 2021. link
  19. A Proactive Real-Time Control Strategy Based on Data-Driven Transit Demand Prediction. Wensi Wang, Fang Zong, Baozhen Yao. IEEE TITS 2021. link
  20. Automatic Feature Engineering for Bus Passenger Flow Prediction Based on Modular Convolutional Neural Network. Yang Liu, Cheng Lyu, Xin Liu, Zhiyuan Liu. IEEE TITS 2021. link
  21. Recommendation for Ridesharing Groups Through Destination Prediction on Trajectory Data. Lei Tang, Zongtao Duan, Yishui Zhu, Junchi Ma, Zihang Liu. IEEE TITS 2021. link
  22. TrafficGAN: Network-Scale Deep Traffic Prediction With Generative Adversarial Nets. Yuxuan Zhang, Senzhang Wang, Bing Chen, Jiannong Cao, Zhiqiu Huang. IEEE TITS 2021. link
  23. Spatio-Temporal Knowledge Transfer for Urban Crowd Flow Prediction via Deep Attentive Adaptation Networks. Senzhang Wang(Central South University), Hao Miao, Jiyue Li, Jiannong Cao. IEEE TITS 2021. link
  24. Learning Dynamic and Hierarchical Traffic Spatiotemporal Features With Transformer. Haoyang Yan(BUAA), Xiaolei Ma, Ziyuan Pu. IEEE TITS 2021. link
  25. A Graph Convolutional Stacked Bidirectional Unidirectional-LSTM Neural Network for Metro Ridership Prediction. Pengfei Chen(Sun Yat-sen University), Xuandi Fu, Xue Wang. IEEE TITS 2021. link
  26. FTPG: A Fine-Grained Traffic Prediction Method With Graph Attention Network Using Big Trace Data. Mengyuan Fang(Wuhan University), Luliang Tang, Xue Yang, Yang Chen, Chaokui Li, Qingquan Li. IEEE TITS 2021. link
  27. KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting. Jiawei Zhu(Central South University), Xing Han, Hanhan Deng, Chao Tao, Ling Zhao, Pu Wang, Tao Lin, Haifeng Li. IEEE TITS 2021. link

2020

  1. The Viscosity Solution for Hamilton Jacobi Travel Time Dynamics. Sergio Contreras, Pushkin Kachroo. IEEE TITS 2020.
  2. Profitable Taxi Travel Route Recommendation Based on Big Taxi Trajectory Data. Boting Qu, Wenxin Yang, Ge Cui, Xin Wang. IEEE TITS 2020.
  3. Exploring Individual Travel Patterns Across Private Car Trajectory Data. Yourong Huang, Zhu Xiao, Dong Wang, Hongbo Jiang, Di Wu. IEEE TITS 2020.
  4. Trajectory Planning for Autonomous High-Speed Overtaking in Structured Environments Using Robust MPC. Shilp Dixit, Umberto Montanaro, Mehrdad Dianati, David Oxtoby, Tom Mizutani, Alexandros Mouzakitis, Saber Fallah. IEEE TITS 2020.
  5. TrajCompressor: An Online Map-matching-based Trajectory Compression Framework Leveraging Vehicle Heading Direction and Change. Chao Chen, Yan Ding, Xuefeng Xie, Shu Zhang, Zhu Wang, Liang Feng. IEEE TITS 2020.
  6. Graph-Search and Differential Equations for Time-Optimal Vessel Route Planning in Dynamic Ocean Waves. Gianandrea Mannarini, Deepak N. Subramani, Pierre F. J. Lermusiaux, Nadia Pinardi. IEEE TITS 2020.
  7. Evidential-Based Approach for Trajectory Planning With Tentacles, for Autonomous Vehicles. Hafida Mouhagir, Reine Talj, Véronique Cherfaoui, François Aioun, Franck Guillemard. IEEE TITS 2020.
  8. Estimating Travel Time Distributions by Bayesian Network Inference. Anatolii Prokhorchuk, Justin Dauwels, Patrick Jaillet. IEEE TITS 2020.
  9. Real-Time Speed Trajectory Planning for Minimum Fuel Consumption of a Ground Vehicle. Junyoung Kim, Changsun Ahn. IEEE TITS 2020.
  10. Human-Like Trajectory Planning on Curved Road: Learning From Human Drivers. Aoxue Li, Haobin Jiang, Zhaojian Li, Jie Zhou, Xinchen Zhou. IEEE TITS 2020.
  11. Network-Wide Link Travel Time Inference Using Trip-Based Data From Automatic Vehicle Identification Detectors. Yiting Zhu, Zhaocheng He, Weiwei Sun. IEEE TITS 2020.
  12. Estimation of Link Travel Time Distribution With Limited Traffic Detectors. Peibo Duan, Guoqiang Mao, Jun Kang, Baoqi Huang. IEEE TITS 2020.
  13. Short-Term Prediction of Passenger Demand in Multi-Zone Level: Temporal Convolutional Neural Network With Multi-Task Learning. Kunpeng Zhang, Zijian Liu, Liang Zheng. IEEE TITS 2020. link
  14. Spatio-Temporal Ensemble Method for Car-Hailing Demand Prediction. Yang Liu, Cheng Lyu, Anish Khadka, Wenbo Zhang, Zhiyuan Liu. IEEE TITS 2020. link
  15. Attention-Based Deep Ensemble Net for Large-Scale Online Taxi-Hailing Demand Prediction. Yang Liu;Zhiyuan Liu;Cheng Lyu;Jieping Ye. IEEE TITS 2020. link
  16. Deep Multi-Scale Convolutional LSTM Network for Travel Demand and Origin-Destination Predictions. Kai-Fung Chu, Albert Y. S. Lam, Victor O. K. Li. IEEE TITS 2020. link
  17. Short-Term Traffic Flow Forecasting: A Component-Wise Gradient Boosting Approach With Hierarchical Reconciliation. Zili Li, Zuduo Zheng, Simon Washington. IEEE TITS 2020. link
  18. DeepSTD: Mining Spatio-Temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction. Chuanpan Zheng, Xiaoliang Fan, Chenglu Wen, Longbiao Chen, Cheng Wang, Jonathan Li. IEEE TITS 2020. link
  19. Data-Driven Metro Train Crowding Prediction Based on Real-Time Load Data. Erik Jenelius. IEEE TITS 2020. link
  20. Deep Irregular Convolutional Residual LSTM for Urban Traffic Passenger Flows Prediction. Bowen Du, Hao Peng, Senzhang Wang, Md Zakirul Alam Bhuiyan, Lihong Wang, Qiran Gong, Lin Liu, Jing Li. IEEE TITS 2020. link
  21. Subway Passenger Flow Prediction for Special Events Using Smart Card Data. Enhui Chen, Zhirui Ye, Chao Wang, Mingtao Xu. IEEE TITS 2020. link
  22. An Improved Bayesian Combination Model for Short-Term Traffic Prediction With Deep Learning. Yuanli Gu, Wenqi Lu, Xinyue Xu, Lingqiao Qin, Zhuangzhuang Shao, Hanyu Zhang. IEEE TITS 2020. link
  23. Spatial–Temporal Deep Tensor Neural Networks for Large-Scale Urban Network Speed Prediction. Lingxiao Zhou, Shuaichao Zhang, Jingru Yu, Xiqun Chen. IEEE TITS 2020. link
  24. Trajectory Forecasting With Neural Networks: An Empirical Evaluation and A New Hybrid Model. Yuan Wang, Dongxiang Zhang, Ying Liu, Kian-Lee Tan. IEEE TITS 2020. link
  25. T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction. Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng;Haifeng Li. IEEE TITS 2020. link
  26. Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting. Zhiyong Cui, Kristian Henrickson, Ruimin Ke, Yinhai Wang. IEEE TITS 2020. link
  27. Multi-Scale and Multi-Scope Convolutional Neural Networks for Destination Prediction of Trajectories. Jianming Lv, Qinghui Sun, Qing Li Luis Moreira-Matias. IEEE TITS 2020. link
  28. Topological Graph Convolutional Network-Based Urban Traffic Flow and Density Prediction. Han Qiu; Qinkai Zheng; Mounira Msahli; Gerard Memmi; Meikang Qiu; Jialiang Lu. IEEE TITS 2020. link
  29. Deep learning architecture for short-term passenger flow forecasting in urban rail transit. Jinlei Zhang; Feng Chen; Zhiyong Cui; Yinan Guo; Yadi Zhu. IEEE TITS 2020. link
  30. Dynamic Graph Convolution Network for Traffic Forecasting Based on Latent Network of Laplace Matrix Estimation. Kan Guo; Yongli Hu; Zhen Qian; Yanfeng Sun; Junbin Gao; Baocai Yin. IEEE TITS 2020. link
  31. Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction. Lingbo Liu(Sun Yat-sen University), Jiajie Zhen, Guanbin Li, Geng Zhan, Zhaocheng He, Bowen Du, Liang Lin. IEEE TITS 2020. link
  32. Global-Local Temporal Convolutional Network for Traffic Flow Prediction. Yajie Ren(BUPT), Dong Zhao, Dan Luo, Huadong Ma, Pengrui Duan. IEEE TITS 2020. link
  33. A Multi-Stream Feature Fusion Approach for Traffic Prediction. Zhishuai Li(CAS), Gang Xiong, Yonglin Tian, Yisheng Lv, Yuanyuan Chen, Pan Hui, Xiang Su. IEEE TITS 2020. link
  34. Short-Term Traffic Flow Forecasting Using Ensemble Approach Based on Deep Belief Networks. Jin Liu(Macau University of Science and Technology), Naiqi Wu, Yan Qiao, Zhiwu Li. IEEE TITS 2020. link
  35. Grids versus graphs: Partitioning space for improved taxi demand-supply forecasts. Neema Davis, Gaurav Raina, Krishna Jagannathan. IEEE TITS 2020. link
  36. Short-Term Traffic Flow Forecasting Method With M-B-LSTM Hybrid Network. Zhaowei Qu(Jilin University), Haitao Li, Zhihui Li, Zhong Tao. IEEE TITS 2020. link
  37. Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction. Mingqi Lv(Zhejiang University of Technology), Zhaoxiong Hong, Ling Chen, Tieming Chen, Tiantian Zhu, Shouling Ji. IEEE TITS 2020. link
  38. Embedding Traffic Network Characteristics Using Tensor for Improved Traffic Prediction. Manish Bhanu(ndian Institute of Technology), João Mendes-Moreira, Joydeep Chandra. IEEE TITS 2020. link
  39. Forecasting Transportation Network Speed Using Deep Capsule Networks With Nested LSTM Models. Xiaolei Ma(BUAA), Houyue Zhong, Yi Li, Junyan Ma, Zhiyong Cui, Yinhai Wang. IEEE TITS 2020. link
  40. A Spatial–Temporal Attention Approach for Traffic Prediction. Xiaoming Shi(Dalian University of Technology), Heng Qi, Yanming Shen, Genze Wu, Baocai Yin. IEEE TITS 2020. link
  41. Long-Term Traffic Prediction Based on LSTM Encoder-Decoder Architecture. Zhumei Wang(Beijing University of Technology), Xing Su, Zhiming Ding. IEEE TITS 2020. link
  42. A Hybrid Deep Learning Model With Attention-Based Conv-LSTM Networks for Short-Term Traffic Flow Prediction. Haifeng Zheng(Fuzhou University), Feng Lin, Xinxin Feng, Youjia Chen. IEEE TITS 2020. link
  43. Spatial Origin-Destination Flow Imputation Using Graph Convolutional Networks. Xin Yao(PKU), Yong Gao, Di Zhu, Ed Manley, Jiaoe Wang, Yu Liu. IEEE TITS 2020. link
  44. STNN: A Spatio-Temporal Neural Network for Traffic Predictions. Zhixiang He(CUHK), Chi-Yin Chow, Jia-Dong Zhang. IEEE TITS 2020. link
  45. Taxi Demand Prediction Using Parallel Multi-Task Learning Model. Chizhan Zhang(CAS), Fenghua Zhu, Xiao Wang, Leilei Sun, Haina Tang, Yisheng Lv. IEEE TITS 2020. link

2019

  1. Probabilistic Data Fusion for Short-Term Traffic Prediction With Semiparametric Density Ratio Model. Zheng Zhu, Xiqun Chen, Xuechi Zhang, Lei Zhang. IEEE TITS 2019. link
  2. Real-Time Traffic Speed Estimation With Graph Convolutional Generative Autoencoder. James Jian Qiao Yu, Jiatao Gu. IEEE TITS 2019. link
  3. Long-Term Traffic Speed Prediction Based on Multiscale Spatio-Temporal Feature Learning Network. Di Zang, Jiawei Ling, Zhihua Wei, Keshuang Tang, Jiujun Cheng. IEEE TITS 2019. link
  4. Traffic Volume Prediction With Segment-Based Regression Kriging and its Implementation in Assessing the Impact of Heavy Vehicles. Yongze Song, Xiangyu Wang, Graeme Wright, Dominique Thatcher, Peng Wu, Pascal Felix. IEEE TITS 2019. link
  5. A Hybrid Model for Short-Term Traffic Volume Prediction in Massive Transportation Systems. Zulong Diao, Dafang Zhang, Xin Wang, Kun Xie, Shaoyao He, Xin Lu, Yanbiao Li. IEEE TITS 2019. link
  6. Adaptive Rolling Smoothing With Heterogeneous Data for Traffic State Estimation and Prediction. Xiqun Chen, Shuaichao Zhang, Li Li, Liang Li. IEEE TITS 2019. link
  7. An Evaluation of HTM and LSTM for Short-Term Arterial Traffic Flow Prediction. Jonathan Mackenzie, John F. Roddick, Rocco Zito. IEEE TITS 2019. link
  8. Adaptive Multi-Kernel SVM With Spatial–Temporal Correlation for Short-Term Traffic Flow Prediction. Xinxin Feng, Xianyao Ling, Haifeng Zheng, Zhonghui Chen, Yiwen Xu. IEEE TITS 2019. link
  9. A Unified Spatio-Temporal Model for Short-Term Traffic Flow Prediction. Peibo Duan, Guoqiang Mao, Weifa Liang, Degan Zhang. IEEE TITS 2019. link
  10. Prediction-Based Eco-Approach and Departure at Signalized Intersections With Speed Forecasting on Preceding Vehicles. Fei Ye, Peng Hao, Xuewei Qi, Guoyuan Wu, Kanok Boriboonsomsin, Matthew J. Barth. IEEE TITS 2019. link
  11. Deep Spatial–Temporal 3D Convolutional Neural Networks for Traffic Data Forecasting. Shengnan Guo, Youfang Lin, Shijie Li, Zhaoming Chen, Huaiyu Wan. IEEE TITS 2019. link
  12. Forecasting Short-Term Passenger Flow: An Empirical Study on Shenzhen Metro. Liyang Tang, Yang Zhao, Javier Cabrera, Jian Ma, Kwok Leung Tsui. IEEE TITS 2019. link
  13. Deep and Embedded Learning Approach for Traffic Flow Prediction in Urban Informatics. Zibin Zheng, Yatao Yang, Jiahao Liu, Hong-Ning Dai, Yan Zhang. IEEE TITS 2019. link
  14. Tunable and Transferable RBF Model for Short-Term Traffic Forecasting. Pinlong Cai, Yunpeng Wang, Guangquan Lu. IEEE TITS 2019. link
  15. A General Framework for Unmet Demand Prediction in On-Demand Transport Services. Wengen Li, Jiannong Cao, Jihong Guan, Shuigeng Zhou, Guanqing Liang, Winnie K. Y. So, Michal Szczecinski. IEEE TITS 2019. link
  16. Contextualized Spatial–Temporal Network for Taxi Origin-Destination Demand Prediction. Lingbo Liu, Zhilin Qiu, Guanbin Li, Qing Wang, Wanli Ouyang, Liang Lin. IEEE TITS 2019. link
  17. Vehicle Speed Prediction Using a Markov Chain With Speed Constraints. Jaewook Shin, Myoungho Sunwoo. IEEE TITS 2019. link
  18. A Scalable Framework for Trajectory Prediction. Punit Rathore, Dheeraj Kumar, Sutharshan Rajasegarar, Marimuthu Palaniswami, James C. Bezdek. IEEE TITS 2019. link
  19. Travel-Time Prediction of Bus Journey With Multiple Bus Trips. Peilan He, Guiyuan Jiang, Siew-Kei Lam, Dehua Tang. IEEE TITS 2019. link
  20. Hexagon-Based Convolutional Neural Network for Supply-Demand Forecasting of Ride-Sourcing Services. Jintao Ke, Hai Yang, Hongyu Zheng, Xiqun Chen, Yitian Jia, Pinghua Gong, Jieping Ye. IEEE TITS 2019. link
  21. Estimated Time of Arrival Using Historical Vessel Tracking Data. Alfredo Alessandrini, Fabio Mazzarella, Michele Vespe. IEEE TITS 2019. link

2018

  1. Citywide Spatial-Temporal Travel Time Estimation Using Big and Sparse Trajectories. Kun Tang, Shuyan Chen, Zhiyuan Liu. IEEE TITS 2018. link
  2. Urban Network Travel Time Prediction Based on a Probabilistic Principal Component Analysis Model of Probe Data. Erik Jenelius, Haris N. Koutsopoulos. IEEE TITS 2018. link
  3. A Functional Data Analysis Approach to Traffic Volume Forecasting. Isaac Michael Wagner-Muns, Ivan G. Guardiola, V. A. Samaranayke, Wasim Irshad Kayani. IEEE TITS 2018. link
  4. Using an ARIMA-GARCH Modeling Approach to Improve Subway Short-Term Ridership Forecasting Accounting for Dynamic Volatility. Chuan Ding, Jinxiao Duan, Yanru Zhang, Xinkai Wu, Guizhen Yu. IEEE TITS 2018. link
  5. Taxi Demand Forecasting: A HEDGE-Based Tessellation Strategy for Improved Accuracy. Neema Davis, Gaurav Raina, Krishna Jagannathan. IEEE TITS 2018. link

2017

  1. Forecasting the Subway Passenger Flow Under Event Occurrences With Social Media. Ming Ni, Qing He, Jing Gao. IEEE TITS 2017. link
  2. Real-Time Traffic State Estimation With Connected Vehicles. Sakib Mahmud Khan, Kakan C. Dey, Mashrur Chowdhury. IEEE TITS 2017. link
  3. A Real-Time Passenger Flow Estimation and Prediction Method for Urban Bus Transit Systems. Jun Zhang, Dayong Shen, Lai Tu, Fan Zhang, Chengzhong Xu, Yi Wang, Chen Tian, Xiangyang Li, Benxiong Huang, Zhengxi Li. IEEE TITS 2017. link
  4. Long-Term Ship Speed Prediction for Intelligent Traffic Signaling. Shaojun Gan, Shan Liang; Kang Li; Jing Deng, Tingli Cheng. IEEE TITS 2017. link
  5. An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic. Jinjun Tang, Fang Liu, Yajie Zou, Weibin Zhang, Yinhai Wang. IEEE TITS 2017. link
  6. Traffic Velocity Estimation From Vehicle Count Sequences. Takayuki Katsuki, Tetsuro Morimura, Masato Inoue. IEEE TITS 2017. link
  7. Vehicle Speed Prediction by Two-Level Data Driven Models in Vehicular Networks. Bingnan Jiang, Yunsi Fei. IEEE TITS 2017. link

2016 and before

  1. Repeatability and Similarity of Freeway Traffic Flow and Long-Term Prediction Under Big Data. Zhongsheng Hou, Xingyi Li. IEEE TITS 2016. link
  2. Improvement of Search Strategy With K-Nearest Neighbors Approach for Traffic State Prediction. Simon Oh, Young-Ji Byon, Hwasoo Yeo. IEEE TITS 2016. link
  3. Short-Term Traffic Prediction Based on Dynamic Tensor Completion. Huachun Tan, Yuankai Wu, Bin Shen, Peter J. Jin, Bin Ran. IEEE TITS 2016. link
  4. Fusing Loop and GPS Probe Measurements to Estimate Freeway Density. Matthew Wright, Roberto Horowitz. IEEE TITS 2016. link
  5. T-DesP: Destination Prediction Based on Big Trajectory Data. Xiang Li, Mengting Li, Yue-Jiao Gong, Xing-Lin Zhang, Jian Yin. IEEE TITS 2016. link
  6. Uncertainty in Bus Arrival Time Predictions: Treating Heteroscedasticity With a Metamodel Approach. Aidan O'Sullivan, Francisco C. Pereira, Jinhua Zhao, Harilaos N. Koutsopoulos. IEEE TITS 2016. link
  7. Managing Spatial Graph Dependencies in Large Volumes of Traffic Data for Travel-Time Prediction. Athanasios Salamanis, Dionysios D. Kehagias, Christos K. Filelis-Papadopoulos, Dimitrios Tzovaras, George A. Gravvanis. IEEE TITS 2016. link
  8. High-Order Gaussian Process Dynamical Models for Traffic Flow Prediction. Jing Zhao, Shiliang Sun. IEEE TITS 2016. link
  9. Traffic Flow Prediction With Big Data: A Deep Learning Approach. Yisheng Lv(CAS), Yanjie Duan, Wenwen Kang, Zhengxi Li, Fei-Yue Wang. IEEE TITS 2015. link
  10. Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning. Marco Lippi(University of Florence), Matteo Bertini, Paolo Frasconi. IEEE TITS 2013. link

Others

2024

  1. CityCAN: Causal Attention Network for Citywide Spatio-Temporal Forecasting. Chengxin Wang(NUS), Yuxuan Liang, Gary Tan. WSDM 2024. link
  2. CreST: A Credible Spatiotemporal Learning Framework for Uncertainty-aware Traffic Forecasting. Zhengyang Zhou(USTC), Jiahao Shi, Hongbo Zhang, Qiongyu Chen, Xu Wang, Hongyang Chen, Yang Wang. WSDM 2024. link
  3. MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy Optimization. Dongcheng Zou(BUAA), Senzhang Wang, Xuefeng Li, Hao Peng, Yuandong Wang, Chunyang Liu, Kehua Sheng, Bo Zhang. WSDM 2024. link

2023

  1. A Multi-graph Fusion Based Spatiotemporal Dynamic Learning Framework. Xu Wang(USTC), Lianliang Chen, Hongbo Zhang, Pengkun Wang, Zhengyang Zhou, Yang Wang. WSDM 2023. link
  2. A Knowledge-Driven Memory System for Traffic Flow Prediction. Binwu Wang(USTC), Yudong Zhang, Pengkun Wang, Xu Wang, Lei Bai, Yang Wang. DASFAA 2023. link
  3. Region-Aware Graph Convolutional Network for Traffic Flow Forecasting. Haitao Liang(Soochow University), An Liu, Jianfeng Qu, Wei Chen, Xiaofang Zhang, Lei Zhao. DASFAA 2023. link
  4. Dual-Stage Graph Convolution Network With Graph Learning For Traffic Prediction. Zilong Li(Heilongjiang University), Qianqian Ren, Long Chen, Jianguo Sun. ICASSP 2023. link
  5. Sparse Graph Learning from Spatiotemporal Time Series. Andrea Cini(Università della Svizzera italiana), Daniele Zambon, Cesare Alippi. JMLR 2023. link
  6. Dynamic Multi-View Graph Neural Networks for Citywide Traffic Inference. Shaojie Dai(Ocean University of China), Jinshuai Wang, Chao Huang, Yanwei Yu, Junyu Dong. TKDD 2023. link
  7. Graph Neural Rough Differential Equations for Traffic Forecasting. Jeongwhan Choi(Yonsei University), Noseong Park. TIST 2023. link

2022

  1. Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting. Zezhi Shao(CAS), Zhao Zhang, Wei Wei, Fei Wang, Yongjun Xu, Xin Cao, Christian S. Jensen. VLDB 2022. link
  2. ST-GSP: Spatial-Temporal Global Semantic Representation Learning for Urban Flow Prediction. Liang Zhao(Chongqing University), Min Gao, Zongwei Wang. WSDM 2022. link
  3. CMT-Net: A Mutual Transition Aware Framework for Taxicab Pick-ups and Drop-offs Co-Prediction. Yudong Zhang(USTC), Binwu Wang, Ziyang Shan, Zhengyang Zhou, Yang Wang. WSDM 2022. link
  4. Graph-Based Traffic Forecasting via Communication-Efficient Federated Learning. Chenhan Zhang(Southern University of Science and Technology), Shiyao Zhang, Shui Yu, James J.Q. Yu. WCNC 2022. link
  5. Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting. Jiabin Tang(Southwest Jiaotong University), Tang Qian, Shijing Liu, Shengdong Du, Jie Hu, Tianrui Li. IJCNN 2022. link
  6. Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution. Fuxian Li(THU), Jie Feng, Huan Yan, Guangyin Jin, Depeng Jin, Yong Li. TKDD 2022. link
  7. Adaptive Spatio-temporal Graph Neural Network for traffic forecasting. Xuxiang Ta(BUAA), Zihan Liu, Xiao Hu, Le Yu, Leilei Sun, Bowen Du. KBS 2022. link

2021

  1. MDTP: A Multi-source Deep Traffic Prediction Framework over Spatio-Temporal Trajectory Data. Ziquan Fang(ZJU), Lu Pan, Lu Chen, Yuntao Du, Yunjun Gao. VLDB 2021. link
  2. METRO: A Generic Graph Neural Network Framework for Multivariate Time Series Forecasting. Yue Cui(HKUST), Kai Zheng, Dingshan Cui, Jiandong Xie, Liwei Deng, Feiteng Huang, Xiaofang Zhou. VLDB 2021. link
  3. Predicting Crowd Flows via Pyramid Dilated Deeper Spatial-temporal Network. Congcong Miao(THU), Jiajun Fu, Jilong Wang, Heng Yu, Botao Yao, Anqi Zhong, Jie Chen, Zekun He. WSDM 2021. link
  4. MT-STNets: Multi-Task Spatial-Temporal Networks for Multi-Scale Traffic Prediction. Senzhang Wang(Central South University), Meiyue Zhang, Hao Miao, Philip S. Yu. SDM 2021. link
  5. STG-Meta: Spatial-Temporal Graph Meta-Learning for Traffic Forecasting. Jiadong Li(THU), Wang Pan, Qipu Deng, Zhi Wang, Wenwu Zhu. IJCNN 2021. link
  6. Transfer Learning in Traffic Prediction with Graph Neural Networks. Yunjie Huang(Southern University of Science and Technology), Xiaozhuang Song, Shiyao Zhang, James J.Q. Yu. ITSC 2021. link
  7. Fine-Grained Spatial-Temporal Representation Learning with Missing Data Completion for Traffic Flow Prediction. Shiqi Wang( Chongqing Universit), Min Gao, Zongwei Wang, Jia Wang, Fan Wu, Junhao Wen. CollaborateCom 2021. link
  8. AreaTransfer: A Cross-City Crowd Flow Prediction Framework Based on Transfer Learning. Xiaohui Wei(Jilin University), Tao Guo, Hongmei Yu, Zijian Li, Hao Guo, Xiang Li. SmartCom 2021. link
  9. TAGCN: Station-level demand prediction for bike-sharing system via a temporal attention graph convolution network. Wenjie Zi(National University of Defense Technology), Wei Xiong, Hao Chen, Luo Chen. Information Sciences 2021. link
  10. A transfer approach with attention reptile method and long-term generation mechanism for few-shot traffic prediction. Chujie Tian(BUPT), Xinning Zhu, Zheng Hu, JianMa. Neurocomputing 2021. link
  11. Long-term Origin-Destination Demand Prediction with Graph Deep Learning. Xiexin Zou(Southern University of Science and Technology), Shiyao Zhang, Chenhan Zhang, James J.Q. Yu, Edward Chung. TBD 2021. link
  12. Spatial-temporal attention wavenet: A deep learning framework for traffic prediction considering spatial-temporal dependencies. Chenyu Tian(THU), Wai Kin (Victor) Chan. IET Intelligent Transport Systems 2021. link
  13. Graph Sequence Neural Network with an Attention Mechanism for Traffic Speed Prediction. Zhilong Lu(BUAA), Weifeng Lv, Zhipu Xie, Bowen Du, Guixi Xiong, Leilei Sun, Haiquan Wang. TIST 2021. link

2020

  1. On the inclusion of spatial information for spatio-temporal neural networks. R de Medrano, JL Aznarte. Springer 2020. link
  2. A spatio-temporal attention-based spot-forecasting framework for urban traffic prediction. Rodrigo de Medrano, José L. Aznarte. Applied Soft Computing 2020. link
  3. ST-MGAT: Spatial-Temporal Multi-Head Graph Attention Networks for Traffic Forecasting. Kelang Tian; Jingjie Guo; Kejiang Ye; Cheng-Zhong Xu. IEEE ICTAI 2020. link
  4. Multi-STGCnet: A Graph Convolution Based Spatial-Temporal Framework for Subway Passenger Flow Forecasting. Jiexia Ye; Juanjuan Zhao; Kejiang Ye; Chengzhong Xu. IEEE IJCNN 2020. link
  5. Multi-graph convolutional network for short-term passenger flow forecasting in urban rail transit. Jinlei Zhang; Feng Chen; Yinan Guo; Xiaohong Li. IET ITS 2020. link
  6. Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting. Tanwi Mallick(Argonne National Laboratory), Prasanna Balaprakash, Eric Rask, Jane Macfarlane. ICPR 2020. link
  7. Dynamic Graph Filters Networks: A Gray-box Model for Multistep Traffic Forecasting. Guopeng LI(University of Technology Delft), Victor L. Knoop, Hans van Lint. ITSC 2020. link
  8. Short-term Traffic Prediction with Deep Neural Networks and Adaptive Transfer Learning. Junyi Li(ICL), Fangce Guo, Yibing Wang, Lihui Zhang, Xiaoxiang Na, Simon Hu. ITSC 2020. link
  9. Transfer Learning and Online Learning for Traffic Forecasting under Different Data Availability Conditions: Alternatives and Pitfalls. Eric L. Manibardo(Basque Research and Technology Alliance), Ibai Laña, Javier Del Ser. ITSC 2020. link
  10. Learning Mobility Flows from Urban Features with Spatial Interaction Models and Neural Networks. Gevorg Yeghikyan, Felix L. Opolka, Mirco Nanni, Bruno Lepri, Pietro Lio'. SMARTCOMP 2020. link
  11. A Meta-Learning Scheme for Adaptive Short-Term Network Traffic Prediction. Qing He(KTH Royal Institute of Technology), Arash Moayyedi, György Dán, Georgios P. Koudouridis, Per Tengkvist. JSAC 2020. link
  12. Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting. Hao Peng(BUAA), Hongfei Wang, Bowen Du, Md Zakirul Alam Bhuiyan, Hongyuan Ma, Jianwei Liu, Lihong Wang, Zeyu Yang, Linfeng Du, Senzhang Wang, Philip S.Yu. Information Sciences 2020. link
  13. A temporal-aware LSTM enhanced by loss-switch mechanism for traffic flow forecasting. Huakang Lu, Zuhao Ge, Youyi Song, Dazhi Jiang, Teng Zhou, Jing Qin. Neurocomputing 2020. link
  14. Meta-MSNet: Meta-Learning based Multi-Source Data Fusion for Traffic Flow Prediction. Shen Fang(CAS), Xianbing Pan, Shiming Xiang, Chunhong Pan. SPL 2020. link
  15. A Spatio-Temporal Spot-Forecasting Framework for Urban Traffic Prediction. Rodrigo de Medrano, José L. Aznarte. Applied Soft Computing 2020. link
  16. Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network. Jintao Ke(HKUST), Xiaoran Qin, Hai Yang, Zhengfei Zheng, Zheng Zhu, Jieping Ye. Transportation Research Part C: Emerging Technologies 2020. link
  17. Multi-community passenger demand prediction at region level based on spatio-temporal graph convolutional network. Jinjun Tang(Central South University), Jian Liang, Fang Liu, Jingjing Hao, Yinhai Wang. Transportation Research Part C: Emerging Technologies 2020. link

2019

  1. An Attention-based Spatiotemporal LSTM Network for Next POI Recommendation. Liwei Huang, Yutao Ma, Shibo Wang, and Yanbo Liu. IEEE Transactions on Services Computing 2019. link
  2. A Simple Baseline for Travel Time Estimation using Large-scale Trip Data. Hongjian Wang, Xianfeng Tang, Yu-Hsuan Kuo, Daniel Kifer, Zhenhui Li. ACM TIST 2019. link
  3. Foreseeing Congestion using LSTM on Urban Traffic Flow Clusters. Ziyue Wang(University of Manitoba Winnipeg), Parimala Thulasiraman. ICSAI 2019. link

2018

  1. A Contextual Attention Recurrent Architecture for Context-Aware Venue Recommendation. Jarana Manotumruksa, Craig Macdonald, and Iadh Ounis. SIGIR 2018. link
  2. Attentive Crowd Flow Machines. Lingbo Liu, Ruimao Zhang, Jiefeng Peng, Guanbin Li, Bowen Du, Liang Lin. ACM MM 2018. link
  3. TPM: A Temporal Personalized Model for Spatial Item Recommendation. Weiqing Wang, Hongzhi Yin, Xingzhong Du, Quoc Viet Hung Nguyen, Xiaofang Zhou. ACM Trans. Intell. Syst. Technol. ACM TIST 2018. link
  4. GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs. Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, Dit-Yan Yeung. UAI 2018. link
  5. Detecting Taxi Speeding from Sparse and Low-Sampled Trajectory Data. Xibo Zhou(HKUST), Qiong Luo, Dian Zhang, Lionel M. Ni. APWeb-WAIM 2018. link
  6. Estimating multi-year 24/7 origin-destination demand using high-granular multi-source traffic data. Wei Ma, Zhen (Sean) Qian. Transportation Research Part C: Emerging Technologies 2018. link

2017

  1. ST-SAGE: A Spatial-Temporal Sparse Additive Generative Model for Spatial Item Recommendation. Weiqing Wang, Hongzhi Yin, Ling Chen, Yizhou Sun, Shazia Wasim Sadiq, Xiaofang Zhou. ACM TIST 2017. link
  2. Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction. Xiaolei Ma(BUAA), Zhuang Dai, Zhengbing He, Jihui Ma, Yong Wang, Yunpeng Wang. Sensors 2017. link

2016 and before

  1. Location Prediction: A Temporal-Spatial Bayesian Model. Yantao Jia, Yuanzhuo Wang, Xiaolong Jin, Xueqi Cheng. ACM TIST 2016. link
  2. A Unified Point-of-Interest Recommendation Framework in Location-Based Social Networks. Chen Cheng, Haiqin Yang, Irwin King, Michael R. Lyu. ACM TIST 2016. link
  3. Using LSTM and GRU Neural Network Methods for Traffic Flow Prediction. Rui Fu(THU), Zuo Zhang, Li Li. YAC 2016. link
  4. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Xiaolei Ma(BUAA), Zhimin Tao, Yinhai Wang, Haiyang Yu, Yunpeng Wang. Transportation Research Part C: Emerging Technologies 2015. link
  5. New Bayesian combination method for short-term traffic flow forecasting. Jian Wang(Purdue University), Wei Deng, Yuntao Guo. Transportation Research Part C: Emerging Technologies 2014. link
  6. Real-time road traffic prediction with spatio-temporal correlations. Wanli Min(IBM Singapore), Laura Wynter. Transportation Research Part C: Emerging Technologies 2011. link

Contributors

There are contributors to this paper collection, we will continue to update it.

<a href="https://github.com/WenMellors"><img src="https://avatars.githubusercontent.com/u/33283819?v=4" width=98px></img></a> <a href="https://github.com/aptx1231"><img src="https://avatars.githubusercontent.com/u/35984903?v=4" width=98px></img></a> <a href="https://github.com/NickHan-cs"><img src="https://avatars.githubusercontent.com/u/59010369?v=4" width=98px></img></a> <a href="https://github.com/XBR-1111"><img src="https://avatars.githubusercontent.com/u/59074275?v=4" width=98px></img></a> <a href="https://github.com/MarSeventh"><img src="https://avatars.githubusercontent.com/u/108160987" width="98px"></img></a>

Cite

Our paper is accepted by ACM SIGSPATIAL 2021. If you find LibCity useful for your research or development, please cite our paper.

@inproceedings{10.1145/3474717.3483923,
  author = {Wang, Jingyuan and Jiang, Jiawei and Jiang, Wenjun and Li, Chao and Zhao, Wayne Xin},
  title = {LibCity: An Open Library for Traffic Prediction},
  year = {2021},
  isbn = {9781450386647},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3474717.3483923},
  doi = {10.1145/3474717.3483923},
  booktitle = {Proceedings of the 29th International Conference on Advances in Geographic Information Systems},
  pages = {145–148},
  numpages = {4},
  keywords = {Spatial-temporal System, Reproducibility, Traffic Prediction},
  location = {Beijing, China},
  series = {SIGSPATIAL '21}
}

Jingyuan Wang, Jiawei Jiang, Wenjun Jiang, Chao Li, and Wayne Xin Zhao. 2021. LibCity: An Open Library for Traffic Prediction. In Proceedings of the 29th International Conference on Advances in Geographic Information Systems (SIGSPATIAL '21). Association for Computing Machinery, New York, NY, USA, 145–148. DOI:https://doi.org/10.1145/3474717.3483923