{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T16:32:45Z","timestamp":1779294765278,"version":"3.51.4"},"reference-count":40,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T00:00:00Z","timestamp":1742256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Vehicle-to-Everything (V2X) communication promises to revolutionize road safety and efficiency. However, challenges in data sharing and network reliability impede its full realization. This paper addresses these challenges by proposing a novel Deep Learning (DL) approach for traffic prediction in V2X environments. We employ Bidirectional Long Short-Term Memory (BiLSTM) networks and compare their performance against other prominent DL architectures, including unidirectional LSTM and Gated Recurrent Unit (GRU). Our findings demonstrate that the BiLSTM model exhibits superior accuracy in predicting traffic patterns. This enhanced prediction capability enables more efficient resource allocation, improved network performance, and enhanced safety for all road users, reducing fuel consumption, decreased emissions, and a more sustainable transportation system.<\/jats:p>","DOI":"10.3389\/frai.2025.1565287","type":"journal-article","created":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T07:03:22Z","timestamp":1742281402000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Accurate V2X traffic prediction with deep learning architectures"],"prefix":"10.3389","volume":"8","author":[{"given":"Ali R.","family":"Abdellah","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed","family":"Abdelmoaty","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdelhamied A.","family":"Ateya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed A.","family":"Abd El-Latif","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ammar","family":"Muthanna","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrey","family":"Koucheryavy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,3,18]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"2207","DOI":"10.3390\/sym13112207","article-title":"Performance estimation in V2X networks using deep learning-based M-estimator loss functions in the presence of outliers","volume":"13","author":"Abdellah","year":"2021","journal-title":"Symmetry"},{"key":"ref2","author":"Abdellah","year":"2022"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.31854\/2307-1303-2022-10-2-1-13","article-title":"Artificial intelligence driven 5G and beyond networks","volume":"10","author":"Abdellah","year":"2022","journal-title":"Telecom IT"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"304","DOI":"10.3390\/fi13120304","article-title":"Machine learning algorithm for delay prediction in IoT and tactile internet","volume":"13","author":"Abdellah","year":"2021","journal-title":"Future Internet"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"700","DOI":"10.3390\/math11030700","article-title":"Smart traffic shaping based on distributed reinforcement learning for multimedia streaming over 5G-VANET communication technology","volume":"11","author":"Ahmed","year":"2023","journal-title":"Mathematics"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2024\/8845070","article-title":"Artificial intelligence in 6G wireless networks: opportunities, applications, and challenges","volume":"2024","author":"Alhammadi","year":"2024","journal-title":"Int. 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