Event-Aware Multimodal Mobility Nowcasting
Authors: Zhaonan Wang, Renhe Jiang, Hao Xue, Flora D. Salim, Xuan Song, Ryosuke Shibasaki4228-4236
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The enhanced event-aware spatio-temporal network, namely EAST-Net, is evaluated on several real-world datasets with a wide variety and coverage of societal events. Both quantitative and qualitative experimental results verify the superiority of our approach compared with the state-of-the-art baselines. |
| Researcher Affiliation | Academia | Zhaonan Wang1,3 , Renhe Jiang1,2 , Hao Xue3, Flora D. Salim3, Xuan Song1, Ryosuke Shibasaki1 1 Center for Spatial Information Science, University of Tokyo; 2 Information Technology Center, University of Tokyo 3 School of Computing Technologies, RMIT University |
| Pseudocode | No | No pseudocode or algorithm blocks were explicitly labeled or formatted as such. |
| Open Source Code | Yes | Code and data are published on https://github.com/underdoc-wang/EAST-Net. |
| Open Datasets | Yes | We chronologically split each dataset for training, validation, testing with a ratio of 7 : 1 : 2, such that the lengths of test sets are roughly last 20 days for JONAS-{NYC, DC}, 110 days for COVID-CHI, and 40 days for COVID-US. |
| Dataset Splits | Yes | We chronologically split each dataset for training, validation, testing with a ratio of 7 : 1 : 2, such that the lengths of test sets are roughly last 20 days for JONAS-{NYC, DC}, 110 days for COVID-CHI, and 40 days for COVID-US. |
| Hardware Specification | Yes | We implement EAST-Net with Py Torch and carry out experiments on a GPU server with NVIDIA Ge Force GTX 1080 Ti graphic cards. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify a version number or other software dependencies with their versions. |
| Experiment Setup | Yes | Lengths of observational and nowcasting sequences are set to α = 8 and β = 8, respectively; number of GCRU layers L = 2 with approximation order K = 3 and hidden dimension q = 32; embedding dimensions for Tcov v = 2, µ(sp) = 20 and µ(mo) = 3; mobility prototype memory m = 8 and D = 16. For model training, batch size = 32; learning rate = 5 10 4; maximum epoch = 100 with an early stopper with a patience of 10; MAE is chosen to be optimized using Adam. |