DeepMove: predicting human mobility with attentional recurrent networks J Feng, Y Li, C Zhang, F Sun, F Meng, A Guo, D Jin Proceedings of the 2018 World Wide Web Conference on World Wide Web, 1459-1468, 2018 | 342 | 2018 |
DeepSTN+: Context-aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis Z Lin*, J Feng*, Z Lu, Y Li, D Jin The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), 2019 | 123 | 2019 |
DeepTP: An end-to-end neural network for mobile cellular traffic prediction J Feng, X Chen, R Gao, M Zeng, Y Li IEEE Network 32 (6), 108-115, 2018 | 79 | 2018 |
Learning phase competition for traffic signal control G Zheng, Y Xiong, X Zang, J Feng, H Wei, H Zhang, Y Li, K Xu, Z Li Proceedings of the 28th ACM international conference on information and …, 2019 | 74 | 2019 |
PMF: A privacy-preserving human mobility prediction framework via federated learning J Feng, C Rong, F Sun, D Guo, Y Li Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous …, 2020 | 61 | 2020 |
Context-aware real-time population estimation for metropolis F Xu, J Feng, P Zhang, Y Li Proceedings of the 2016 ACM International Joint Conference on Pervasive and …, 2016 | 58 | 2016 |
DPLink: User identity linkage via deep neural network from heterogeneous mobility data J Feng, M Zhang, H Wang, Z Yang, C Zhang, Y Li, D Jin The World Wide Web Conference, 459-469, 2019 | 56 | 2019 |
Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution F Li, J Feng, H Yan, G Jin, F Yang, F Sun, D Jin, Y Li ACM Transactions on Knowledge Discovery from Data (TKDD), 2021 | 25 | 2021 |
Learning to simulate human mobility J Feng, Z Yang, F Xu, H Yu, M Wang, Y Li Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 24 | 2020 |
DeepDPM: Dynamic Population Mapping via Deep Neural Network Z Zong*, J Feng*, K Liu, H Shi, Y Li The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), 2018 | 17 | 2018 |
Uniqueness in the city: Urban morphology and location privacy H Cao, J Feng, Y Li, V Kostakos Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous …, 2018 | 17 | 2018 |
DeepMM: Deep learning based map matching with data augmentation K Zhao, J Feng, Z Xu, T Xia, L Chen, F Sun, D Guo, D Jin, Y Li Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances …, 2019 | 16 | 2019 |
A Sequential Convolution Network for Population Flow Prediction with Explicitly Correlation Modelling J Feng, Z Lin, T Xia, F Sun, D Guo, Y Li Proceedings of the Twenty-Ninth International Joint Conference on Artificial …, 2020 | 14 | 2020 |
A bimodal model to estimate dynamic metropolitan population by mobile phone data J Feng, Y Li, F Xu, D Jin Sensors 18 (10), 3431, 2018 | 12 | 2018 |
Understanding metropolitan crowd mobility via mobile cellular accessing data H Cao, J Sankaranarayanan, J Feng, Y Li, H Samet ACM Transactions on Spatial Algorithms and Systems (TSAS) 5 (2), 1-18, 2019 | 11 | 2019 |
Predicting human mobility with semantic motivation via multi-task attentional recurrent networks J Feng, Y Li, Z Yang, Q Qiu, D Jin IEEE Transactions on Knowledge and Data Engineering, 2020 | 10 | 2020 |
Semantic-aware spatio-temporal app usage representation via graph convolutional network Y Yu, T Xia, H Wang, J Feng, Y Li Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous …, 2020 | 8 | 2020 |
Deep learning models for population flow generation from aggregated mobility data C Rong, J Feng, Y Li Adjunct Proceedings of the 2019 ACM International Joint Conference on …, 2019 | 8 | 2019 |
3dgcn: 3-dimensional dynamic graph convolutional network for citywide crowd flow prediction T Xia, J Lin, Y Li, J Feng, P Hui, F Sun, D Guo, D Jin ACM Transactions on Knowledge Discovery from Data (TKDD) 15 (6), 1-21, 2021 | 7 | 2021 |
Attnmove: History enhanced trajectory recovery via attentional network T Xia, Y Qi, J Feng, F Xu, F Sun, D Guo, Y Li Proceedings of the AAAI Conference on Artificial Intelligence 35 (5), 4494-4502, 2021 | 6 | 2021 |