In the paper collection, we collected traffic prediction papers published in the recent years (2016-now) on 11 top conferences and journals, namely, AAAI, IJCAI, KDD, CIKM, ICDM, WWW, NIPS, ICLR, 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.

• Traffic Prediction Paper Collection
  • Surveys
  • AAAI
  • IJCAI
  • KDD
  • CIKM
  • ICDM
  • WWW
  • NIPS
  • ICLR
  • SIGSPATIAL
  • IEEE TKDE
  • IEEE TITS
  • Others
• Contributors

2021

1. Graph Neural Network for Traffic Forecasting: A Survey. Weiwei Jiang, Jiayun Luo. arXiv 2021. link
2. Short-term Traffic Prediction with Deep Neural Networks: A Survey. Kyungeun Lee; Moonjung Eo; Euna Jung; Yoonjin Yoon; Wonjong Rhee. IEEE Access 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. 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
2. Big Data Analytics in Intelligent Transportation Systems: A Survey. Li Zhu, Fei Richard Yu, Yige Wang, Bin Ning, Tao Tang. IEEE TITS 2019. link
3. Deep Learning for Spatio-Temporal Data Mining: A Survey. Senzhang Wang, Jiannong Cao, and Philip S. Yu. IEEE TKDE 2019. link

2018

1. Spatio-temporal data mining: A survey of problems and methods. Atluri, Gowtham, Anuj Karpatne, and Vipin Kumar. ACM Computing Surveys 2018. link
2. Survey on traffic prediction in smart cities. Attila M Nagy, Vilmos Simon. Pervasive and Mobile Computing 2018. link
3. A Brief Overview of Machine Learning Methods for Short-term Traffic Forecasting and Future Directions. Yaguang Li, Cyrus Shahabi. SIGSPATIAL 2018. 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. 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
4. 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
5. Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting. Mengzhang Li, Zhanxing Zhu. AAAI 2021. link
6. FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting. Boris N. Oreshkin, Arezou Amini, Lucy Coyle, Mark Coates. AAAI 2021. link
7. 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
8. 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
9. Disentangled Multi-Relational Graph Convolutional Network for Pedestrian Trajectory Prediction. Inhwan Bae, Hae-Gon Jeon. AAAI 2021. link
10. Coupled Layer-Wise Graph Convolution for Transportation Demand Prediction. Junchen Ye, Leilei Sun, Bowen Du, Yanjie Fu, Hui Xiong. AAAI 2021. link
11. CARPe Posterum: A Convolutional Approach for Real-Time Pedestrian Path Prediction. Matias Mendieta, Hamed Tabkhi. AAAI 2021. link
12. 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
13. Community-Aware Multi-Task Transportation Demand Prediction. Hao Liu, Qiyu Wu, Fuzhen Zhuang, Xinjiang Lu, Dejing Dou, Hui Xiong. AAAI 2021. link
14. 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

2020

1. Enhancing Personalized Trip Recommendation with Attractive Routes. Jiqing Gu, Chao Song, Wenjun Jiang, Xiaomin Wang, Ming Liu. AAAI 2020. link
2. Real-Time Route Search by Locations. Lisi Chen, Shuo Shang, Tao Guo. AAAI 2020. link
3. 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
4. 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
5. RiskOracle: A Minute-Level Citywide Traffic Accident Forecasting Framework. Zhengyang Zhou, Yang Wang, Xike Xie, Lianliang Chen, Hengchang Liu. AAAI 2020. link
6. GMAN: A Graph Multi-Attention Network for Traffic Prediction. Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, Jianzhong Qi. AAAI 2020. link
7. Learning Geo-Contextual Embeddings for Commuting Flow Prediction. Zhicheng Liu, Fabio Miranda, Weiting Xiong, Junyan Yang, Qiao Wang, Cláudio T. Silva. AAAI 2020. link
8. 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
9. Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development. Yongshun Gong, Zhibin Li, Jian Zhang, Wei Liu, Jinfeng Yi. AAAI 2020. link
10. 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
11. Spatio-Temporal Graph Structure Learning for Traffic Forecasting. Qi Zhang, Jianlong Chang, Gaofeng Meng, Shiming Xiang, Chunhong Pan. AAAI 2020. link
12. Self-Attention ConvLSTM for Spatiotemporal Prediction. Zhihui Lin, Maomao Li, Zhuobin Zheng, Yangyang Cheng, Chun Yuan. AAAI 2020. link
13. 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
14. Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets. Ziqiang Cheng, Yang Yang, Wei Wang, Wenjie Hu, Yueting Zhuang, Guojie Song. AAAI 2020. link
15. 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
16. An Attentional Recurrent Neural Network for Personalized Next Location Recommendation. Qing Guo, Zhu Sun, Jie Zhang, Yin-Leng Theng. AAAI 2020. link
17. 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
18. OMuLeT: Online Multi-Lead Time Location Prediction for Hurricane Trajectory Forecasting. Ding Wang, Boyang Liu, Pang-Ning Tan, Lifeng Luo. AAAI 2020. link
19. Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series. Zhi-Xuan Tan, Harold Soh, Desmond Ong. AAAI 2020. link

2019

1. A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems. Ling Pan, Qingpeng Cai, Zhixuan Fang, Pingzhong Tang, Longbo Huang. AAAI 2019. link
2. 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
3. Preference-Aware Task Assignment in Spatial Crowdsourcing. Yan Zhao, Jinfu Xia, Guanfeng Liu, Han Su, Defu Lian, Shuo Shang, Kai Zheng. AAAI 2019. link
4. 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
5. 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
6. 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
7. Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, Huaiyu Wan. AAAI 2019. link
8. 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
9. 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
10. 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
11. Neural Collective Graphical Models for Estimating Spatio-Temporal Population Flow from Aggregated Data. Tomoharu Iwata, Hitoshi Shimizu. AAAI 2019. link
12. Predicting Hurricane Trajectories Using a Recurrent Neural Network. Sheila Alemany, Jonathan Beltran, Adrián Pérez, Sam Ganzfried. AAAI 2019. link
13. TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents. Yuexin Ma, Xinge Zhu, Sibo Zhang, Ruigang Yang, Wenping Wang, Dinesh Manocha. AAAI. AAAI 2019. link
14. Congestion Graphs for Automated Time Predictions. Arik Senderovich, J. Christopher Beck, Avigdor Gal, Matthias Weidlich. AAAI 2019. link
15. DeepETA: A Spatial-Temporal Sequential Neural Network Model for Estimating Time of Arrival in Package Delivery System. Fan Wu, Lixia Wu. AAAI 2019. link
16. Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction. Huaxiu Yao, Xianfeng Tang, Hua Wei, Guanjie Zheng, Zhenhui Li. AAAI 2019. link

2018

1. 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
2. Algorithms for Trip-Vehicle Assignment in Ride-Sharing. Xiaohui Bei, Shengyu Zhang. AAAI 2018. link
3. Privacy Preserving Point-of-Interest Recommendation Using Decentralized Matrix Factorization. Chaochao Chen, Ziqi Liu, Peilin Zhao, Jun Zhou, Xiaolong Li. AAAI 2018. link
4. 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
5. Predicting Vehicular Travel Times by Modeling Heterogeneous Influences Between Arterial Roads. Avinash Achar, Venkatesh Sarangan, Rohith Regikumar, Anand Sivasubramaniam. AAAI 2018. link
6. 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

2017

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

2016 and before

1. Learning Deep Representation from Big and Heterogeneous Data for Traffic Accident Inference. Quanjun Chen, Xuan Song, Harutoshi Yamada, Ryosuke Shibasaki. AAAI 2016. link
2. 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
3. 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
4. Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts. Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan. AAAI 2016. link

2020

1. Towards Alleviating Traffic Congestion: Optimal Route Planning for Massive-Scale Trips. Ke Li, Lisi Chen, Shuo Shang. IJCAI 2020. link
2. Cross-Interaction Hierarchical Attention Networks for Urban Anomaly Prediction. Chao Huang, Chuxu Zhang, Peng Dai, Liefeng Bo. IJCAI 2020. link
3. 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
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. 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

2019

1. STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting. Lei Bai, Lina Yao, Salil S. Kanhere. IJCAI 2019. link
2. Cross-City Transfer Learning for Deep Spatio-Temporal Prediction. Leye Wang, Xu Geng, Xiaojuan Ma, Feng Liu, Qiang Yang. 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. Graph WaveNet for Deep Spatial-Temporal Graph Modeling. Zonghan Wu, Guodong Long, Chengqi Zhang. IJCAI 2019. link
5. Travel Time Estimation without Road Networks: An Urban Morphological Layout Representation Approach. Wuwei Lan, Yanyan Xu, Bin Zhao. 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. Understanding People Lifestyles: Construction of Urban Movement Knowledge Graph from GPS Trajectory. Nicholas Jing Yuan, Ruihua Song, Xing Xie, Qiang Ma. 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. 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

2016 and before

1. 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
2. Exploring the Context of Locations for Personalized Location Recommendations. Xin Liu, Yong Liu, Xiaoli Li. IJCAI 2016. link

2020

1. 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
2. City Metro Network Expansion with Reinforcement Learning. Yu Wei, Minjia Mao, Xi Zhao, Jianhua Zou, Ping An. KDD 2020. link
3. 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
4. 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
5. 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
6. 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
7. Competitive Analysis for Points of Interest. Shuangli Li, Jingbo Zhou, Tong Xu, Hao Liu, Xinjiang Lu, Hui Xiong. KDD 2020. link
8. 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
9. 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
10. Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data. Garrett Wilson, Janardhan Rao Doppa, Diane J. Cook. KDD 2020. link
11. 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
12. USAD: UnSupervised Anomaly Detection on Multivariate Time Series. Julien Audibert, Pietro Michiardi, Frédéric Guyard, Sébastien Marti, Maria A. Zuluaga. KDD 2020. link
13. 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
14. Geography-Aware Sequential Location Recommendation. Defu Lian, Yongji Wu, Yong Ge, Xing Xie, Enhong Chen. KDD 2020. link
15. 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
16. AutoST: Efficient Neural Architecture Search for Spatio-Temporal Prediction. Ting Li, Junbo Zhang, Kainan Bao, Yuxuan Liang, Yexin Li, Yu Zheng. KDD 2020. link
17. Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction. Haoxing Lin, Rufan Bai, Weijia Jia, Xinyu Yang, Yongjian You. KDD 2020. link
18. BusTr: Predicting Bus Travel Times from Real-Time Traffic. Richard Barnes, Senaka Buthpitiya, James Cook, Alex Fabrikant, Andrew Tomkins, Fangzhou Xu. KDD 2020. link
19. 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
20. Learning Effective Road Network Representation with Hierarchical Graph Neural Networks. Ning Wu, Wayne Xin Zhao, Jingyuan Wang, Dayan Pan. KDD 2020. link
21. ST-SiameseNet: Spatio-Temporal Siamese Networks for Human Mobility Signature Identification. Huimin Ren, Menghai Pan, Yanhua Li, Xun Zhou, Jun Luo. KDD 2020. link
22. 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
23. CompactETA: A Fast Inference System for Travel Time Prediction. Kun Fu, Fanlin Meng, Jieping Ye, Zheng Wang. KDD 2020. link
24. 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
25. 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
26. 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

2019

1. 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
2. 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
3. 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
4. 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
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. 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
7. Predicting dynamic embedding trajectory in temporal interaction networks. Srijan Kumar, Xikun Zhang, Jure Leskovec. KDD 2019. link
8. 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. 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
2. Intellilight: A reinforcement learning approach for intelligent traffic light control. Hua Wei, Guanjie Zheng, Huaxiu Yao, Zhenhui Li. KDD 2018. link
3. 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
4. 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

2017

1. 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
2. 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
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. Point-of-interest demand modeling with human mobility patterns. Yanchi Liu, Chuanren Liu, Xinjiang Lu, Mingfei Teng, Hengshu Zhu, Hui Xiong. KDD 2017. link
5. 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
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

2020

1. 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
2. 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
3. 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
4. DATSING: Data Augmented Time Series Forecasting with Adversarial Domain Adaptation. Hailin Hu, MingJian Tang, Chengcheng Bai. CIKM 2020. link
5. Learning Graph-Based Geographical Latent Representation for Point-of-Interest Recommendation. Buru Chang, Gwanghoon Jang, Seoyoon Kim, Jaewoo Kang. CIKM 2020. link
6. Magellan: A Personalized Travel Recommendation System Using Transaction Data. Konik Kothari, Dhruv Gelda, Wei Zhang, Hao Yang. CIKM 2020. link
7. Generating Full Spatiotemporal Vehicular Paths: A Data Fusion Approach. Nan Xiao, Nan Hu, Liang Yu, Cheng Long. CIKM 2020. link
8. Multi-task Adversarial Spatial-Temporal Networks for Crowd Flow Prediction. Senzhang Wang, Hao Miao, Hao Chen, Zhiqiu Huang. CIKM 2020. link
9. Spatiotemporal Adaptive Gated Graph Convolution Network for Urban Traffic Flow Forecasting. Bin Lu, Xiaoying Gan, Haiming Jin, Luoyi Fu, Haisong Zhang. CIKM 2020. link
10. 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
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. 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
3. 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
4. Modeling temporal-spatial correlations for crime prediction. Xiangyu Zhao, Jiliang Tang. CIKM 2019. link
5. Query processing techniques for big spatial-keyword data. Ahmed R. Mahmood, Walid G. Aref. CIKM 2019. link
6. Towards explainable representation of time-evolving graphs via spatial-temporal graph attention networks. Zhining Liu, Dawei Zhou, Jingrui He. CIKM 2019. link
7. Temporal network embedding with micro-and macro-dynamics. Yuanfu Lu, Xiao Wang, Chuan Shi, Philip S. Yu, Yanfang Ye. CIKM 2019. link
8. Learning to Effectively Estimate the Travel Time for Fastest Route Recommendation. Ning Wu, Jingyuan Wang, Wayne Xin Zhao, Yang Jin. CIKM 2019. link
9. 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
10. 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
11. Exploring The Interaction Effects for Temporal Spatial Behavior Prediction. Huan Yang, Tianyuan Liu, Yuqing Sun, Elisa Bertino. CIKM 2019. link
12. Personalized Route Description Based On Historical Trajectories. Han Su, Guanglin Cong, Wei Chen, Bolong Zheng, Kai Zheng. CIKM 2019. link
13. DeepIST: Deep Image-based Spatio-Temporal Network for Travel Time Estimation. Tao-Yang Fu, Wang-Chien Lee. CIKM 2019. link
14. Unsupervised Representation Learning of Spatial Data via Multimodal Embedding. Porter Jenkins, Ahmad Farag, Suhang Wang, Zhenhui Li. CIKM 2019. link
15. 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
16. 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
17. Learning Traffic Signal Control from Demonstrations. Yuanhao Xiong, Guanjie Zheng, Kai Xu, Zhenhui Li. CIKM 2019. link
18. Deep Dynamic Fusion Network for Traffic Accident Forecasting. Chao Huang, Chuxu Zhang, Peng Dai, Liefeng Bo. CIKM 2019. link
19. Long- and Short-term Preference Learning for Next POI Recommendation. Yuxia Wu, Ke Li, Guoshuai Zhao, Xueming Qian. 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. Destination-aware task assignment in spatial crowdsourcing. Yan Zhao, Yang Li, Yu Wang, Han Su, Kai Zheng. CIKM 2017. link
2. 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
3. SERM: A Recurrent Model for Next Location Prediction in Semantic Trajectories. Di Yao, Chao Zhang, Jian-Hui Huang, Jingping Bi. CIKM 2017. link

2016 and before

1. Learning Points and Routes to Recommend Trajectories. Dawei Chen, Cheng Soon Ong, Lexing Xie. CIKM 2016. link
2. Learning Graph-based POI Embedding for Location-based Recommendation. Min Xie, Hongzhi Yin, Hao Wang, Fanjiang Xu, Weitong Chen, Sen Wang. CIKM 2016. link
3. Improving Personalized Trip Recommendation by Avoiding Crowds. Xiaoting Wang, Christopher Leckie, Jeffrey Chan, Kwan Hui Lim, Tharshan Vaithianathan. 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. 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
6. Collective Traffic Prediction with Partially Observed Traffic History using Location-Based Social Media. Xinyue Liu, Xiangnan Kong, Yanhua Li. CIKM 2016. link
7. Inferring Traffic Incident Start Time with Loop Sensor Data. Mingxuan Yue, Liyue Fan, Cyrus Shahabi. CIKM 2016. link

2020

1. 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
2. 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
3. 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
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. FreqST: Exploiting Frequency Information in Spatiotemporal Modeling for Traffic Prediction. Xian Zhou, Yanyan Shen, Linpeng Huang. ICDM 2020. link
7. 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
8. Modeling Personalized Out-of-Town Distances in Location Recommendation. Daizong Ding, Mi Zhang, Xudong Pan, Min Yang, Xiangnan He. ICDM 2020. link

2019

1. Boosted trajectory calibration for traffic state estimation. Xitong Zhang, Liyang Xie, Zheng Wang, Jiayu Zhou. ICDM 2019. link
2. TrafficGAN: Off-Deployment Traffic Estimation with Traffic Generative Adversarial Networks. Yingxue Zhang, Yanhua Li, Xun Zhou, Xiangnan Kong, Jun Luo. 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. Situation Aware Multi-task Learning for Traffic Prediction. Dingxiong Deng, Cyrus Shahabi, Ugur Demiryurek, Linhong Zhu. ICDM 2017. link
2. Exploiting Hierarchical Structures for POI Recommendation. Pengpeng Zhao, Xiefeng Xu, Yanchi Liu, Ziting Zhou, Kai Zheng, Victor S. Sheng, Hui Xiong. 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. Traffic Speed Prediction and Congestion Source Exploration: A Deep Learning Method. Jingyuan Wang, Qian Gu, Junjie Wu, Guannan Liu, Zhang 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. POI Recommendation: A Temporal Matching between POI Popularity and User Regularity. Zijun Yao, Yanjie Fu, Bin Liu, Yanchi Liu, Hui Xiong. ICDM 2016. link

2021

1. STAN: Spatio-Temporal Attention Network for Next Location Recommendation. Yingtao Luo, Qiang Liu, and Zhaocheng Liu. WWW 2021. link

2020

1. 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
2. Hierarchically Structured Transformer Networks for Fine-Grained Spatial Event Forecasting. Xian Wu, Chao Huang, Chuxu Zhang, Nitesh V. Chawla. WWW 2020. link
3. Towards Fine-grained Flow Forecasting: A Graph Attention Approach for Bike Sharing Systems. Suining He, Kang G. Shin. WWW 2020. link
4. Dynamic Flow Distribution Prediction for Urban Dockless E-Scooter Sharing Reconfiguration. Suining He, Kang G. Shin. WWW 2020. link
5. 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
6. Mining Points-of-Interest for Explaining Urban Phenomena: A Scalable Variational Inference Approach. Christof Naumzik, Patrick Zoechbauer, Stefan Feuerriegel. WWW 2020. link
7. 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
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

2019

1. Predicting Human Mobility via Variational Attention. Qiang Gao, Fan Zhou, Goce Trajcevski, Kunpeng Zhang, Ting Zhong, Fengli Zhang. WWW 2019. link
2. Context-aware Variational Trajectory Encoding and Human Mobility Inference. Fan Zhou, Xiaoli Yue, Goce Trajcevski, Ting Zhong, Kunpeng Zhang. WWW 2019. link
3. 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
4. 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
5. 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
6. Learning Travel Time Distributions with Deep Generative Model. Xiucheng Li, Gao Cong, Aixin Sun, Yun Cheng. 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. TribeFlow: Mining & Predicting User Trajectories. Flavio Figueiredo, Bruno Ribeiro, Jussara M. Almeida, Christos Faloutsos. WWW 2016. link
2. Exploiting Dining Preference for Restaurant Recommendation. Fuzheng Zhang, Nicholas Jing Yuan, Kai Zheng, Defu Lian, Xing Xie, Yong Rui. WWW 2016. link
3. Factorizing Personalized Markov Chains for Next-Basket Recommendation. Rendle, Steffen and Freudenthaler, Christoph and Schmidt-Thieme, Lars. WWW 2010. link

2020

1. AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control. Afshin Oroojlooy, MohammadReza Nazari, Davood Hajinezhad, Jorge Silva. NIPS 2020. link
2. Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. Lei Bai, Lina Yao, Can Li, Xianzhi Wang, Can Wang. NIPS 2020. link
3. EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning. Jiachen Li, Fan Yang, Masayoshi Tomizuka, Chiho Choi. NIPS 2020. link
4. Multi-agent Trajectory Prediction with Fuzzy Query Attention. Nitin Kamra, Hao Zhu, Dweep Kumarbhai Trivedi, Ming Zhang, Yan Liu. 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

2021

1. Discrete Graph Structure Learning for Forecasting Multiple Time Series. Chao Shang, Jie Chen, Jinbo Bi. ICLR 2021. link

2020

1. 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

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

2019

1. FairST: Equitable Spatial and Temporal Demand Prediction for New Mobility Systems. An Yan, Bill Howe. SIGSPATIAL 2019. link
2. Predicting traffic accidents with event recorder data. oshiaki Takimoto, Yusuke Tanaka, Takeshi Kurashima, Shuhei Yamamoto, Maya Okawa, Hiroyuki Toda. SIGSPATIAL 2019. link
3. 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
4. Context-based Markov Model toward Spatio-Temporal Prediction with Realistic Dataset. Kota Tsubouchi, Tomoki Saito, Masamichi Shimosaka. 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. DNN-based prediction model for spatio-temporal data. Junbo Zhang, Yu Zheng, Dekang Qi, Ruiyuan Li, Xiuwen Yi. SIGSPATIAL 2016. link
2. FCCF: forecasting citywide crowd flows based on big data. Minh X. Hoang, Yu Zheng, Ambuj K. Singh. SIGSPATIAL 2016. link

2021

1. Predicting Taxi and Uber Demand in Cities: Approaching the Limit of Predictability. Kai Zhao, Denis Khryashchev, Huy T. Vo. IEEE TKDE 2021. link
2. 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
3. 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
4. TIPS: Mining Top-K Locations to Minimize User-Inconvenience for Trajectory-Aware Services. Shubhadip Mitra, Priya Saraf, Arnab Bhattacharya. 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. 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
5. Citywide Bike Usage Prediction in a Bike-Sharing System. Yexin Li, Yu Zheng. IEEE TKDE 2020. link

2019

1. 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
2. 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

2018

1. 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
2. Capturing the Spatiotemporal Evolution in Road Traffic Networks. Tarique Anwar, Chengfei Liu, Hai L. Vu, Md. Saiful Islam, Timos Sellis. 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. Hierarchical Spatio-Temporal Pattern Discovery and Predictive Modeling. Chung-Hsien Yu, Wei Ding, Melissa Morabito, Ping Chen. 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. 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

2021

1. TrafficGAN: Network-Scale Deep Traffic Prediction With Generative Adversarial Nets. Yuxuan Zhang, Senzhang Wang, Bing Chen, Jiannong Cao, Zhiqiu Huang. IEEE TITS 2021. link
2. 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
3. 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
4. Recommendation for Ridesharing Groups Through Destination Prediction on Trajectory Data. Lei Tang, Zongtao Duan, Yishui Zhu, Junchi Ma, Zihang Liu. IEEE TITS 2021. link
5. 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
6. A Proactive Real-Time Control Strategy Based on Data-Driven Transit Demand Prediction. Wensi Wang, Fang Zong, Baozhen Yao. IEEE TITS 2021. link
7. Speed Prediction Based on a Traffic Factor State Network Model.. Weibin Zhang, Yaoyao Feng, Kai Lu, Yuhang Song, Yinhai Wang. IEEE TITS 2021. link
8. 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
9. 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
10. 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
11. Predicting Citywide Road Traffic Flow Using Deep Spatiotemporal Neural Networks. Tao Jia, Penggao Yan. IEEE TITS 2021. link
12. 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

2020

1. Spatio-Temporal Ensemble Method for Car-Hailing Demand Prediction. Yang Liu, Cheng Lyu, Anish Khadka, Wenbo Zhang, Zhiyuan Liu. IEEE TITS 2020. link
2. 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
3. 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
4. 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
5. Short-Term Traffic Flow Forecasting: A Component-Wise Gradient Boosting Approach With Hierarchical Reconciliation. Zili Li, Zuduo Zheng, Simon Washington. IEEE TITS 2020. link
6. 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
7. Data-Driven Metro Train Crowding Prediction Based on Real-Time Load Data. Erik Jenelius. IEEE TITS 2020. link
8. 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
9. Subway Passenger Flow Prediction for Special Events Using Smart Card Data. Enhui Chen, Zhirui Ye, Chao Wang, Mingtao Xu. IEEE TITS 2020. link
10. 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
11. 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
12. 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
13. 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
14. 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
15. 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
16. 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
17. 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
18. 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

2019

1. 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
2. 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
3. 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
4. Tunable and Transferable RBF Model for Short-Term Traffic Forecasting. Pinlong Cai, Yunpeng Wang, Guangquan Lu. IEEE TITS 2019. link
5. 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
6. 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
7. 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
8. A Unified Spatio-Temporal Model for Short-Term Traffic Flow Prediction. Peibo Duan, Guoqiang Mao, Weifa Liang, Degan Zhang. IEEE TITS 2019. link
9. 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
10. 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
11. An Evaluation of HTM and LSTM for Short-Term Arterial Traffic Flow Prediction. Jonathan Mackenzie, John F. Roddick, Rocco Zito. IEEE TITS 2019. link
12. Adaptive Rolling Smoothing With Heterogeneous Data for Traffic State Estimation and Prediction. Xiqun Chen, Shuaichao Zhang, Li Li, Liang Li. IEEE TITS 2019. link
13. 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
14. 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
15. 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
16. Real-Time Traffic Speed Estimation With Graph Convolutional Generative Autoencoder. James Jian Qiao Yu, Jiatao Gu. IEEE TITS 2019. link
17. Vehicle Speed Prediction Using a Markov Chain With Speed Constraints. Jaewook Shin, Myoungho Sunwoo. IEEE TITS 2019. link
18. 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
19. A Scalable Framework for Trajectory Prediction. Punit Rathore, Dheeraj Kumar, Sutharshan Rajasegarar, Marimuthu Palaniswami, James C. Bezdek. IEEE TITS 2019. link
20. Travel-Time Prediction of Bus Journey With Multiple Bus Trips. Peilan He, Guiyuan Jiang, Siew-Kei Lam, Dehua Tang. 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. Taxi Demand Forecasting: A HEDGE-Based Tessellation Strategy for Improved Accuracy. Neema Davis, Gaurav Raina, Krishna Jagannathan. IEEE TITS 2018. link
2. 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
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. Citywide Spatial-Temporal Travel Time Estimation Using Big and Sparse Trajectories. Kun Tang, Shuyan Chen, Zhiyuan Liu. IEEE TITS 2018. link
5. 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

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. Traffic Velocity Estimation From Vehicle Count Sequences. Takayuki Katsuki, Tetsuro Morimura, Masato Inoue. IEEE TITS 2017. link
6. Vehicle Speed Prediction by Two-Level Data Driven Models in Vehicular Networks. Bingnan Jiang, Yunsi Fei. IEEE TITS 2017. link
7. 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

2016 and before

1. Improvement of Search Strategy With K-Nearest Neighbors Approach for Traffic State Prediction. Simon Oh, Young-Ji Byon, Hwasoo Yeo. IEEE TITS 2016. link
2. Repeatability and Similarity of Freeway Traffic Flow and Long-Term Prediction Under Big Data. Zhongsheng Hou, Xingyi Li. IEEE TITS 2016. link
3. High-Order Gaussian Process Dynamical Models for Traffic Flow Prediction. Jing Zhao, Shiliang Sun. IEEE TITS 2016. link
4. Short-Term Traffic Prediction Based on Dynamic Tensor Completion. Huachun Tan, Yuankai Wu, Bin Shen, Peter J. Jin, Bin Ran. IEEE TITS 2016. link
5. Fusing Loop and GPS Probe Measurements to Estimate Freeway Density. Matthew Wright, Roberto Horowitz. IEEE TITS 2016. link
6. 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
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. 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

2020

1. A spatio-temporal attention-based spot-forecasting framework for urban traffic prediction. Rodrigo de Medrano, José L. Aznarte. Applied Soft Computing 2020. link
2. 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
3. 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
4. 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
5. On the inclusion of spatial information for spatio-temporal neural networks. R de Medrano, JL Aznarte. Springer 2020. link

2019

1. 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
2. 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

2018

1. Attentive Crowd Flow Machines. Lingbo Liu, Ruimao Zhang, Jiefeng Peng, Guanbin Li, Bowen Du, Liang Lin. ACM MM 2018. link
2. 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
3. A Contextual Attention Recurrent Architecture for Context-Aware Venue Recommendation. Jarana Manotumruksa, Craig Macdonald, and Iadh Ounis. SIGIR 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

2016 and before

1. 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
2. Location Prediction: A Temporal-Spatial Bayesian Model. Yantao Jia, Yuanzhuo Wang, Xiaolong Jin, Xueqi Cheng. ACM TIST 2016. link

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