{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T14:14:24Z","timestamp":1772979264384,"version":"3.50.1"},"reference-count":23,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:00:00Z","timestamp":1772755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100019920","name":"Naif Arab University for Security Sciences","doi-asserted-by":"crossref","award":["NAUSS-25-R01"],"award-info":[{"award-number":["NAUSS-25-R01"]}],"id":[{"id":"10.13039\/100019920","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Network"],"abstract":"<jats:p>Digital twin systems are becoming an important tool in intelligent transportation management, as they provide simulation-based environments for monitoring, analyzing, and predicting traffic behavior. However, the predictive performance of traffic digital twins is often limited by the quality and temporal consistency of sensor-level data generated from microscopic simulations. Most current calibration methods focus mainly on matching macroscopic traffic indicators, such as vehicle count and speed, without explicitly addressing the requirements of multi-horizon forecasting. This creates a gap between achieving realistic simulations and building reliable predictive models. This research proposes a forecasting-aware digital traffic twin framework that integrates microscopic SUMO simulation, controlled sensor-level observation modeling through geometric misalignment and noise injection, behavioral calibration, and deep temporal forecasting within a unified end-to-end structure. Unlike traditional calibration approaches, the proposed Genetic Algorithm (GA) reformulates calibration as a multi-step predictive optimization task. Simulation parameters are optimized by minimizing forecasting error produced by a lightweight proxy sequence model embedded within the calibration loop. In this way, calibration moves beyond simple statistical matching and instead emphasizes temporal learnability and forecasting stability, enabling the digital twin to generate traffic patterns more suitable for long-term prediction. Based on the calibrated traffic time series, both convolutional and recurrent deep learning models are evaluated under single-step and multi-step forecasting scenarios. To further examine generalizability, external validation is performed using the real-world PEMS-BAY dataset. The experimental findings demonstrate that forecasting-aware calibration reduces macroscopic traffic signal errors by around 50% for vehicle count and around 40% for average speed, improves temporal stability, and significantly enhances forecasting accuracy across both short-term and long-term horizons.<\/jats:p>","DOI":"10.3390\/network6010013","type":"journal-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T09:22:14Z","timestamp":1772788934000},"page":"13","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Forecasting-Aware Digital Twin Calibration for Reliable Multi-Horizon Traffic Prediction"],"prefix":"10.3390","volume":"6","author":[{"given":"Zeyad","family":"AlJundi","sequence":"first","affiliation":[{"name":"Centre of Excellence in Cybercrime and Digital Forensics, Naif Arab University for Security Sciences, Riyadh 11452, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taqwa A.","family":"Alhaj","sequence":"additional","affiliation":[{"name":"Centre of Excellence in Cybercrime and Digital Forensics, Naif Arab University for Security Sciences, Riyadh 11452, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fatin A.","family":"Elhaj","sequence":"additional","affiliation":[{"name":"College of Computing & Intelligent Systems, University of Khorfakkan, Sharjah 18119, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Inshirah","family":"Idris","sequence":"additional","affiliation":[{"name":"Computer Science and Information Technology College, Wadmedani Ahlia University, Wad Medani P.O. Box 402, Sudan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tasneem","family":"Darwish","sequence":"additional","affiliation":[{"name":"Department of Computer Science, St. Francis Xavier University, Antigonish, NS B2G 2W5, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,6]]},"reference":[{"key":"ref_1","unstructured":"SaudiPedia (2026, February 21). Transport in Saudi Arabia. SaudiPedia 2024. Available online: https:\/\/saudipedia.com\/en\/transport-in-saudi-arabia."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wu, D., Zheng, A., Yu, W., Cao, H., Ling, Q., Liu, J., and Zhou, D. (2025). Digital twin technology in transportation infrastructure: A comprehensive survey of current applications, challenges, and future directions. Appl. 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