Cross-city Few-Shot Traffic Forecasting via Traffic Pattern Bank
Zhanyu Liu Guanjie Zheng Yanwei Yu
Traffic forecasting is a critical service in Intelligent Transportation
Systems (ITS). Utilizing deep models to tackle this task relies heavily on data
from traffic sensors or vehicle devices, while some cities might lack device
support and thus have few available data. So, it is necessary to learn from
data-rich cities and transfer the knowledge to data-scarce cities in order to
improve the performance of traffic forecasting. To address this problem, we
propose a cross-city few-shot traffic forecasting framework via Traffic Pattern
Bank (TPB) due to that the traffic patterns are similar across cities. TPB
utilizes a pre-trained traffic patch encoder to project raw traffic data from
data-rich cities into high-dimensional space, from which a traffic pattern bank
is generated through clustering. Then, the traffic data of the data-scarce city
could query the traffic pattern bank and explicit relations between them are
constructed. The metaknowledge is aggregated based on these relations and an
adjacency matrix is constructed to guide a downstream spatial-temporal model in
forecasting future traffic. The frequently used meta-training framework Reptile
is adapted to find a better initial parameter for the learnable modules.
Experiments on real-world traffic datasets show that TPB outperforms existing
methods and demonstrates the effectiveness of our approach in cross-city
few-shot traffic forecasting.