LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting

TL;DR

LargeST is a comprehensive, large-scale traffic forecasting dataset covering 8,600 sensors over five years, designed to address limitations of existing datasets and facilitate advanced deep learning research in traffic prediction.

Contribution

The paper introduces LargeST, a new extensive traffic dataset with long-term coverage and detailed metadata, enabling more realistic and scalable traffic forecasting research.

Findings

Benchmarking of baseline models on LargeST

Insights into data patterns and challenges

Identification of future research opportunities

Abstract

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.