Traffic24: A 24-Year Traffic Dataset and Benchmark for Long-Term Forecasting

Understanding how traffic forecasting models benefit from extended data histories and generalize across changing conditions requires reusable longitudinal datasets. We introduce Traffic24, a curated traffic dataset covering 24 years of observations in San Diego, from September 2001 to September 2025. Traffic24 contains 505.6 million station–time records at five-minute resolution, including traffic flow, speed, occupancy, truck-related measurements, and lane-level observations. Six nested historical subsets, spanning one to 24 years, support the construction of forecasting tasks with different amounts of historical data. A weather-augmented subset adds regional meteorological context aligned with traffic observations through a shared UTC time axis. Standardized schemas, explicit field definitions, and retained quality and processing indicators support consistent data access and interpretation. To establish an initial forecasting benchmark, we evaluate multiple models on selected subsets using the open-source TimeCopilot and report their predictive performance. These evaluations provide reference results for subsequent research using Traffic24. By bringing together long observational histories, structured temporal subsets, and regional weather context, Traffic24 supports research into how much historical data forecasting models can effectively use, how they generalize over time, and how environmental information can inform traffic prediction.