Hyunwook Lee, Sungahn Ko
We lifted 4 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| HyunWookL/TESTAM | canonical | 3 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| asym_adj | Ran | HyunWookL/TESTAM/util.py code served (permissive licence) · get_code("901bedf723a6f00d") |
| calculate_normalized_laplacian | Ran | HyunWookL/TESTAM/util.py code served (permissive licence) · get_code("e7b2e6a60daa42ca") |
| sym_adj | Ran | HyunWookL/TESTAM/util.py code served (permissive licence) · get_code("9a7928795f74526c") |
| generate_graph_seq2seq_io_data | Not yet run | HyunWookL/TESTAM/generate_training_data.py code served (permissive licence) · get_code("dd669f99f4fe7e0f") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Accurate traffic forecasting is challenging due to the complex interdependencies of large road networks and abrupt speed changes caused by unexpected events. Recent work has focused on spatial modeling with adaptive graph embedding or graph attention but has paid less attention to the temporal characteristics and effectiveness of in-situ modeling. In this paper, we propose the time-enhanced spatio-temporal attention model (TESTAM) to better capture recurring and nonrecurring traffic patterns with mixture-of-experts model with three experts for temporal modeling, spatio-temporal modeling with a static graph, and spatio-temporal dependency modeling with a dynamic graph. By introducing different experts and properly routing them, TESTAM better captures traffic patterns under various circumstances, including cases of spatially isolated roads, highly interconnected roads, and recurring and non-recurring events. For proper routing, we reformulate a gating problem as a classification task with pseudo labels. Experimental results on three public traffic network datasets, METR-LA, PEMS-BAY, and EXPY-TKY, demonstrate that TESTAM outperforms 13 existing methods in terms of accuracy due to its better modeling of recurring and non-recurring traffic patterns. You can find the official code from https://github.com/HyunWookL/TESTAM
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2403.02600")
get_code_for_paper("2403.02600")
have("2403.02600")
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