Accurate spatial-temporal traffic flow forecasting is crucial in aiding
traffic managers in implementing control measures and assisting drivers in
selecting optimal travel routes. Traditional deep-learning based methods for
traffic flow forecasting typically rely on historical data to train their
models, which are then used to make predictions on future data. However, the
performance of the trained model usually degrades due to the temporal drift
between the historical and future data. To make the model trained on historical
data better adapt to future data in a fully online manner, this paper conducts
the first study of the online test-time adaptation techniques for
spatial-temporal traffic flow forecasting problems. To this end, we propose an
Adaptive Double Correction by Series Decomposition (ADCSD) method, which first
decomposes the output of the trained model into seasonal and trend-cyclical
parts and then corrects them by two separate modules during the testing phase
using the latest observed data entry by entry. In the proposed ADCSD method,
instead of fine-tuning the whole trained model during the testing phase, a lite
network is attached after the trained model, and only the lite network is
fine-tuned in the testing process each time a data entry is observed. Moreover,
to satisfy that different time series variables may have different levels of
temporal drift, two adaptive vectors are adopted to provide different weights
for different time series variables. Extensive experiments on four real-world
traffic flow forecasting datasets demonstrate the effectiveness of the proposed
ADCSD method. The code is available at this https URL

本研究介绍了一种用于空间 - 时间交通流预测问题的在线测试时适应技术，提出了自适应双重修正的系列分解（ADCSD）方法，通过两个独立的模块对已训练模型的输出进行季节和趋势周期分解，并在每次观测到数据条目时使用最新观测数据进行修正。该方法在四个真实世界交通流预测数据集上进行了广泛实验，证明了其有效性。