%0 Dataset
%T Los Angeles County Highway Traffic Speed Benchmark Dataset (METR-LA)
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/2af8c989-9763-49fd-a824-a5401fab9100
%W NCDC
%A zhang xin min
%K Traffic speed prediction;Traffic flow prediction;Spatiotemporal forecasting;Graph neural networks;Deep learning;Benchmark dataset
%X This dataset covers the highway network of Los Angeles County, collected from loop detectors in the Caltrans Performance Measurement System (PeMS). Curated by Li et al. (2018) for the DCRNN project, METR-LA contains traffic speed readings from 207 sensors over 4 months (March 1, 2012 to June 30, 2012), aggregated at 5-minute intervals, yielding 34,272 timestamps and approximately 6,519,002 observations. Traffic speed (mph) is the sole variable, normalized via Z-score standardization. The dataset is split into training (70%), validation (10%), and testing (20%). A weighted adjacency matrix based on road network distances is constructed using a thresholded Gaussian kernel. METR-LA is widely adopted as a standard benchmark for evaluating spatiotemporal traffic forecasting models including DCRNN, Graph WaveNet, and STGCN.