  To tackle ever-increasing city traffic congestion problems, researchers have
proposed deep learning models to aid decision-makers in the traffic control
domain. Although the proposed models have been remarkably improved in recent
years, there are still questions that need to be answered before deploying
models. For example, it is difficult to figure out which models provide
state-of-the-art performance, as recently proposed models have often been
evaluated with different datasets and experiment environments. It is also
difficult to determine which models would work when traffic conditions change
abruptly (e.g., rush hour). In this work, we conduct two experiments to answer
the two questions. In the first experiment, we conduct an experiment with the
state-of-the-art models and the identical public datasets to compare model
performance under a consistent experiment environment. We then extract a set of
temporal regions in the datasets, whose speeds change abruptly and use these
regions to explore model performance with difficult intervals. The experiment
results indicate that Graph-WaveNet and GMAN show better performance in
general. We also find that prediction models tend to have varying performances
with data and intervals, which calls for in-depth analysis of models on
difficult intervals for real-world deployment.
