Nowcasting GDP Using Real-Time Highway Traffic Volume by Vehicle Type: Evidence from the Republic of Korea

Timely assessment of macroeconomic conditions is essential because official gross domestic product (GDP) statistics are released with substantial delays and are often revised. This study examines whether high-frequency highway traffic volumes, disaggregated by vehicle type, improve short-term GDP nowcasting in the Republic of Korea. Using nationwide expressway traffic data from 328 toll plazas over the period from September 2008 to September 2025, we integrate traffic series with conventional macroeconomic indicators into a mixed-frequency dynamic factor model and evaluate pseudo-real-time nowcasting performance against official quarterly GDP releases. Time-series diagnostics indicate that traffic volumes contain short-horizon predictive information for GDP and satisfy stationarity requirements after appropriate transformation. In the full evaluation sample, the macro-only benchmark records an RMSE of 1.0258 and an MAE of 0.8716. Adding aggregated traffic changes these metrics only marginally (RMSE = 1.0269, MAE = 0.8696), whereas the model augmented with the heaviest freight class (Vehicle Type 6) performs best, lowering RMSE to 1.0179 and MAE to 0.8652. During the COVID-19 period, forecast accuracy deteriorates across specifications: aggregated traffic increases RMSE and MAE to 1.3456 and 1.2096 relative to the macro-only benchmark (RMSE = 1.3082, MAE = 1.2020), while Vehicle Type 6 lowers MAE to 1.1683 but still records a higher RMSE of 1.3198. These findings show that aggregate mobility measures add limited value, whereas freight-oriented vehicle-type disaggregation provides the most informative highway traffic signal for real-time GDP nowcasting.