TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts
Keskeiset käsitteet
The author proposes TESTAM, a Mixture-of-Experts model, to enhance in-situ traffic forecasting by incorporating diverse graph architectures. By transforming the routing problem into a classification task, TESTAM effectively contextualizes various traffic conditions and selects appropriate spatial modeling methods.
Tiivistelmä
The paper introduces TESTAM, a novel model for traffic forecasting that focuses on in-situ modeling with diverse graph structures. It outperforms existing methods by better capturing recurring and non-recurring traffic patterns. The study highlights the importance of spatial modeling diversity and effective routing mechanisms for accurate traffic forecasting.
Accurate traffic forecasting is challenging due to complex interdependencies in road networks and abrupt speed changes caused by unexpected events. Recent work has focused on spatial modeling but paid less attention to temporal characteristics and in-situ modeling.
The proposed TESTAM model incorporates three experts for temporal modeling, spatio-temporal modeling with static and dynamic graphs to capture different traffic patterns effectively.
Experimental results show that TESTAM outperforms existing methods on three public datasets due to its improved modeling of recurring and non-recurring traffic patterns.
Key metrics:
- METR-LA dataset: MAE 2.93, RMSE 5.95, MAPE 7.99%
- PEMS-BAY dataset: MAE 1.53, RMSE 3.47, MAPE 3.41%
- EXPY-TKY dataset: MAE 6.40, RMSE 10.40, MAPE 28.67%
Käännä lähde
toiselle kielelle
Luo miellekartta
lähdeaineistosta
Siirry lähteeseen
arxiv.org
TESTAM
Tilastot
Experimental results on METR-LA dataset: MAE 2.93, RMSE 5.95, MAPE 7.99%
Experimental results on PEMS-BAY dataset: MAE 1.53, RMSE 3.47, MAPE 3.41%
Experimental results on EXPY-TKY dataset: MAE 6.40, RMSE 10.40, MAPE 28.67%
Lainaukset
"In this paper, we propose a time-enhanced spatio-temporal attention model (TESTAM), a novel Mixture-of-Experts (MoE) model that enables in-situ traffic forecasting."
"Experimental results over state-of-the-art models using three real-world datasets indicate that TESTAM outperforms existing methods quantitatively and qualitatively."
Syvällisempiä Kysymyksiä
How can the concept of in-situ modeling be applied to other domains beyond traffic forecasting
In-situ modeling, as demonstrated in TESTAM for traffic forecasting, can be applied to various other domains beyond transportation. For instance, in healthcare, it could help predict patient outcomes by considering real-time data from medical sensors and historical records. In finance, in-situ modeling could enhance stock market predictions by incorporating dynamic market conditions and trends. Additionally, in environmental monitoring, this approach could improve weather forecasting accuracy by analyzing current atmospheric data alongside past patterns.
What potential limitations or biases could arise from the diverse graph structures used in TESTAM
While diverse graph structures used in TESTAM offer advantages like improved generalization and context-aware spatial modeling, they may also introduce potential limitations or biases. One limitation could be the complexity of managing multiple experts with different spatial modeling methods efficiently during training and inference. Biases might arise if certain graph structures are favored over others based on the dataset characteristics or model design choices. Additionally, the performance of TESTAM could be sensitive to hyperparameters selection for each expert's architecture.
How might the findings of this study impact the development of future transportation technologies
The findings of this study have significant implications for the development of future transportation technologies. By showcasing the effectiveness of TESTAM in improving traffic forecasting accuracy through diverse graph structures and fine-grained routing mechanisms, it sets a precedent for enhancing intelligent transportation systems (ITS). This research can inspire advancements such as real-time adaptive traffic management systems that utilize similar techniques to optimize traffic flow dynamically based on changing conditions. Furthermore, it may lead to innovations in autonomous vehicle navigation systems that rely on precise spatio-temporal attention models for safe and efficient travel routes planning.
