{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T13:29:16Z","timestamp":1774445356527,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T00:00:00Z","timestamp":1774310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/systems14040343","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T10:07:13Z","timestamp":1774433233000},"page":"343","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Nowcasting GDP Using Real-Time Highway Traffic Volume by Vehicle Type: Evidence from the Republic of Korea"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5497-0412","authenticated-orcid":false,"given":"Sung Jae","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Industrial Management and Big Data Engineering, Dong-Eui University, 176, Eomgwang-ro, Busanjin-gu, Busan 47340, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8193-5611","authenticated-orcid":false,"given":"Soongoo","family":"Hong","sequence":"additional","affiliation":[{"name":"International School, Duy Tan University, Danang 550000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5110-7097","authenticated-orcid":false,"given":"Kyungtae","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Management Information Systems, College of Business Administration, Dong-A University, Busan 49236, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9496-5898","authenticated-orcid":false,"given":"Yongbok","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Management Information Systems, College of Business Administration, Dong-A University, Busan 49236, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,24]]},"reference":[{"key":"ref_1","unstructured":"Callen, T. (2012). Gross Domestic Product: An Economy\u2019s All, International Monetary Fund."},{"key":"ref_2","first-page":"38","article-title":"Factors affecting GDP (manufacturing, services, industry): An Indian perspective","volume":"3","author":"Jain","year":"2015","journal-title":"Annu. Res. J. SCMS Pune"},{"key":"ref_3","first-page":"148","article-title":"The factors affecting Gross Domestic Product (GDP) in developing countries: The case of Tanzania","volume":"5","author":"Kira","year":"2013","journal-title":"Eur. J. Bus. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"109","DOI":"10.29252\/jemr.8.30.109","article-title":"Investigating factors affecting on per capita gdp growth in different groups of countries with emphasis on governance indicators","volume":"8","author":"Mohammad","year":"2018","journal-title":"J. Econ. Model. Res."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ilter, C. (2017). What Economic and Social Factors Affect GDP per Capita? A Study on 40 Countries, SSRN.","DOI":"10.2139\/ssrn.2914765"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/S0304-405X(02)00248-9","article-title":"News related to future GDP growth as a risk factor in equity returns","volume":"68","author":"Vassalou","year":"2003","journal-title":"J. Financ. Econ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1016\/j.jmoneco.2008.05.010","article-title":"Nowcasting: The real-time informational content of macroeconomic data","volume":"55","author":"Giannone","year":"2008","journal-title":"J. Monet. Econ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1146\/annurev-economics-080217-053214","article-title":"Macroeconomic nowcasting and forecasting with big data","volume":"10","author":"Bok","year":"2018","journal-title":"Annu. Rev. Econ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1111\/obes.12047","article-title":"Real-time nowcasting of GDP: A factor model vs. professional forecasters","volume":"76","author":"Liebermann","year":"2014","journal-title":"Oxf. Bull. Econ. Stat."},{"key":"ref_10","unstructured":"Stock, J.H., and Watson, M.W. (2010). Dynamic Factor Models. Oxford Handbook of Economic Forecasting, Oxford University Press."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s00181-017-1254-1","article-title":"A dynamic factor model for nowcasting Canadian GDP growth","volume":"53","author":"Chernis","year":"2017","journal-title":"Empir. Econ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1002\/jae.2306","article-title":"Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data","volume":"29","author":"Modugno","year":"2014","journal-title":"J. Appl. Econom."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1016\/j.tra.2006.02.006","article-title":"Economic indicators for the US transportation sector","volume":"40","author":"Lahiri","year":"2006","journal-title":"Transp. Res. Part A Policy Pract."},{"key":"ref_14","first-page":"44","article-title":"A Leading Indicator of Austrian Exports Based on Truck Mileage","volume":"Q1\/09","author":"Fenz","year":"2009","journal-title":"Monet. Policy Econ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1002\/for.1262","article-title":"Nowcasting business cycles using toll data","volume":"32","author":"Askitas","year":"2013","journal-title":"J. Forecast."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"101327","DOI":"10.1016\/j.jjie.2024.101327","article-title":"Nowcasting economic activity with mobility data","volume":"73","author":"Matsumura","year":"2024","journal-title":"J. Jpn. Int. Econ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1111\/joes.12579","article-title":"Future directions in nowcasting economic activity: A systematic literature review","volume":"38","author":"Stundziene","year":"2024","journal-title":"J. Econ. Surv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ashwin, J., Kalamara, E., and Saiz, L. (2021). Nowcasting Euro Area GDP with News Sentiment: A Tale of Two Crises, European Central Bank. Technical Report 2616.","DOI":"10.2139\/ssrn.3971974"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Linzenich, J., and Meunier, B. (2024). Nowcasting Made Easier: A Toolbox for Economists, European Central Bank. Technical Report 3004.","DOI":"10.2139\/ssrn.5060436"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Huber, F., Koop, G., Onorante, L., Pfarrhofer, M., and Schreiner, J. (2021). Nowcasting in a Pandemic Using Non-Parametric Mixed Frequency VARs, European Central Bank. Technical Report 2510.","DOI":"10.2139\/ssrn.3797129"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1016\/j.ijforecast.2022.10.005","article-title":"Testing big data in a big crisis: Nowcasting under COVID-19","volume":"39","author":"Barbaglia","year":"2023","journal-title":"Int. J. Forecast."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1016\/j.ijforecast.2020.10.005","article-title":"Nowcasting GDP using machine-learning algorithms: A real-time assessment","volume":"37","author":"Richardson","year":"2021","journal-title":"Int. J. Forecast."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1460","DOI":"10.1016\/j.ijforecast.2022.07.009","article-title":"Nowcasting GDP with a pool of factor models and a fast estimation algorithm","volume":"39","author":"Eraslan","year":"2023","journal-title":"Int. J. Forecast."},{"key":"ref_24","unstructured":"Galbraith, J.W., and Tkacz, G. (2015). Nowcasting GDP with Electronic Payments Data, European Central Bank. Technical Report 10."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Stundziene, A., Pilinkiene, V., Bruneckiene, J., Grybauskas, A., and Lukauskas, M. (2023). Nowcasting Economic Activity Using Electricity Market Data: The Case of Lithuania. Economies, 11.","DOI":"10.3390\/economies11050134"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"100126","DOI":"10.1016\/j.latcb.2024.100126","article-title":"GDP nowcasting: A machine learning and remote sensing data-based approach for Bolivia","volume":"5","author":"Bolivar","year":"2024","journal-title":"Lat. Am. J. Cent. Bank."},{"key":"ref_27","first-page":"4","article-title":"Highway capacity and economic growth","volume":"14","author":"Aschauer","year":"1990","journal-title":"Econ. Perspect."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1177\/088541229701100402","article-title":"Highways and economic productivity: Interpreting recent evidence","volume":"11","author":"Boarnet","year":"1997","journal-title":"J. Plan. Lit."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s11116-018-9884-5","article-title":"Revisiting the relationship between traffic congestion and the economy: A longitudinal examination of US metropolitan areas","volume":"47","author":"Marshall","year":"2020","journal-title":"Transportation"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.1177\/0042098013505883","article-title":"Traffic congestion\u2019s economic impacts: Evidence from US metropolitan regions","volume":"51","author":"Sweet","year":"2014","journal-title":"Urban Stud."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"104577","DOI":"10.1016\/j.tra.2025.104577","article-title":"Investigating the factors influencing intercity travel mode choice in urban agglomerations: Insights from a three-phase framework","volume":"199","author":"Cheng","year":"2025","journal-title":"Transp. Res. Part A Policy Pract."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Shi, Z., Qian, Y., Zeng, J., Wei, X., and Yang, M. (2025). Coordinated Development of Urban Transportation Structure Optimization and Energy Conservation, and Emission Reduction Under the Low-Carbon Background in Lanzhou, China. Systems, 13.","DOI":"10.3390\/systems13010034"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1111\/1468-0262.00273","article-title":"Determining the number of factors in approximate factor models","volume":"70","author":"Bai","year":"2002","journal-title":"Econometrica"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.1162\/REST_a_00225","article-title":"A quasi-maximum likelihood approach for large, approximate dynamic factor models","volume":"94","author":"Doz","year":"2012","journal-title":"Rev. Econ. Stat."},{"key":"ref_35","first-page":"1","article-title":"Dynamic Factor Model and Deep Learning Algorithm for GDP Nowcasting","volume":"28","author":"Yi","year":"2022","journal-title":"Econ. Anal."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1002\/jae.695","article-title":"A new coincident index of business cycles based on monthly and quarterly series","volume":"18","author":"Mariano","year":"2003","journal-title":"J. Appl. Econom."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Singh, V.P., Singh, R., Paul, P.K., Bisht, D.S., and Gaur, S. (2024). Time series analysis. Hydrological Processes Modelling and Data Analysis: A Primer, Springer.","DOI":"10.1007\/978-981-97-1316-5"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Guo, S., Ladroue, C., and Feng, J. (2010). Granger causality: Theory and applications. Frontiers in Computational and Systems Biology, Springer.","DOI":"10.1007\/978-1-84996-196-7_5"},{"key":"ref_39","first-page":"5803","article-title":"Statistical tests for detecting granger causality","volume":"66","author":"Chopra","year":"2018","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1080\/07350015.1994.10524568","article-title":"Testing for a unit root in time series with pretest data-based model selection","volume":"12","author":"Hall","year":"1994","journal-title":"J. Bus. Econ. Stat."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e896","DOI":"10.1016\/S2468-2667(24)00222-6","article-title":"City mobility patterns during the COVID-19 pandemic: Analysis of a global natural experiment","volume":"9","author":"Hunter","year":"2024","journal-title":"Lancet Public Health"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"13069","DOI":"10.1038\/s41598-021-92134-x","article-title":"Mobility-based real-time economic monitoring amid the COVID-19 pandemic","volume":"11","author":"Spelta","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3354\/cr030079","article-title":"Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance","volume":"30","author":"Willmott","year":"2005","journal-title":"Clim. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5481","DOI":"10.5194\/gmd-15-5481-2022","article-title":"Root mean square error (RMSE) or mean absolute error (MAE): When to use them or not","volume":"15","author":"Hodson","year":"2022","journal-title":"Geosci. Model Dev."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/14\/4\/343\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T10:55:05Z","timestamp":1774436105000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/14\/4\/343"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,24]]},"references-count":44,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["systems14040343"],"URL":"https:\/\/doi.org\/10.3390\/systems14040343","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,24]]}}}