  Road traffic forecasting plays a critical role in smart city initiatives and
has experienced significant advancements thanks to the power of deep learning
in capturing non-linear patterns of traffic data. However, the promising
results achieved on current public datasets may not be applicable to practical
scenarios due to limitations within these datasets. First, the limited sizes of
them may not reflect the real-world scale of traffic networks. Second, the
temporal coverage of these datasets is typically short, posing hurdles in
studying long-term patterns and acquiring sufficient samples for training deep
models. Third, these datasets often lack adequate metadata for sensors, which
compromises the reliability and interpretability of the data. To mitigate these
limitations, we introduce the LargeST benchmark dataset. It encompasses a total
number of 8,600 sensors in California with a 5-year time coverage and includes
comprehensive metadata. Using LargeST, we perform in-depth data analysis to
extract data insights, benchmark well-known baselines in terms of their
performance and efficiency, and identify challenges as well as opportunities
for future research. We release the datasets and baseline implementations at:
https://github.com/liuxu77/LargeST.
