{"id":255321,"date":"2024-06-04T10:25:41","date_gmt":"2024-06-04T10:25:41","guid":{"rendered":"https:\/\/ingenieros.en-desarrollo.net\/?p=255321"},"modified":"2025-01-30T10:05:14","modified_gmt":"2025-01-30T10:05:14","slug":"ecomobility-ct-develops-predictive-digital-twin-for-road-traffic-in-smart-cities","status":"publish","type":"post","link":"https:\/\/www.ctengineeringgroup.com\/ecomobility-ct-develops-predictive-digital-twin-for-road-traffic-in-smart-cities\/","title":{"rendered":"EcoMobility: CT develops predictive digital twin for road traffic in smart cities."},"content":{"rendered":"<p>[et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;4.26.1&#8243; hover_enabled=&#8221;0&#8243; da_disable_devices=&#8221;off|off|off&#8221; global_colors_info=&#8221;{}&#8221; custom_margin=&#8221;0px||||false|false&#8221; custom_padding=&#8221;0px||||false|false&#8221; sticky_enabled=&#8221;0&#8243; da_is_popup=&#8221;off&#8221; da_exit_intent=&#8221;off&#8221; da_has_close=&#8221;on&#8221; da_alt_close=&#8221;off&#8221; da_dark_close=&#8221;off&#8221; da_not_modal=&#8221;on&#8221; da_is_singular=&#8221;off&#8221; da_with_loader=&#8221;off&#8221; da_has_shadow=&#8221;on&#8221;][et_pb_row _builder_version=&#8221;4.16&#8243; background_size=&#8221;initial&#8221; background_position=&#8221;top_left&#8221; background_repeat=&#8221;repeat&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.16&#8243; custom_padding=&#8221;|||&#8221; global_colors_info=&#8221;{}&#8221; custom_padding__hover=&#8221;|||&#8221;][et_pb_text _builder_version=&#8221;4.26.1&#8243; background_size=&#8221;initial&#8221; background_position=&#8221;top_left&#8221; background_repeat=&#8221;repeat&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><\/p>\n<p>The EcoMobility project aims at developing new ways to address challenges related to digital mobility, as well as validating the technologies developed through real-world use cases. In short, it aims to develop intelligent connectivity mechanisms and architectures for safer, greener, and more efficient road mobility.<\/p>\n<p>The consortium developing this project consists of 47 partners in 9 countries, covering a wide range of disciplines from hardware and electronics design to battery management systems for electric vehicles, including software applications for autonomous driving and the development of smart cities, where CT and the Spanish part of the consortium are located.<\/p>\n<p>The Spanish partners include IDNEO, in charge of infrastructure and hardware (5G-based IoT devices), SIGMA Cognition, which provides environmental perception software using computer vision technology, UC3M, which generates the digital map metadata, and CT, responsible for the development of the predictive digital traffic twin.<\/p>\n<p><strong>Key technologies in EcoMobility<\/strong><\/p>\n<p>A key technology is V2V and V2X communication, which is established between vehicles with other vehicles (V2V) or with infrastructures (V2X). The data obtained from this communication includes information on congestion, accidents, emergencies, road conditions and signaling, providing a complete view of the urban environment and current traffic. Even the avenue of communicating ground platforms with aerial platforms is being explored.<\/p>\n<p>These communications seek to use vehicles more efficiently, optimizing routes and ensuring that all journeys are useful, especially in freight transport. Of course, these communications are protected by encryption and cybersecurity, especially when they involve emergency service vehicles (such as ambulance and firefighters, for example) and strategic vehicles, such as police and military vehicles.<\/p>\n<p><strong>Predictive digital twins<\/strong><\/p>\n<p>CT is leading the creation of the smart city\u2019s digital twin and traffic prediction capabilities. Specifically, it is responsible for developing a traffic management system for smart cities, based on information provided by its project partners, such as sensor information and digital maps. Using graphic tools such as Unity and Unreal, the CT team is creating a virtual environment in which traffic can be simulated and predicted, adjusting variables at will and providing the virtual elements, whether cars, motorcyclists or pedestrians, with artificial intelligence and decision-making capabilities.<\/p>\n<p>Using machine learning technology, the CT team trains decision-making models for vehicles and people, allowing the digital twin to autonomously make decisions in real time, as if it were a human mind. With continuous improvement of the algorithms, the results will become increasingly reliable, leading to a virtual city capable of predicting its own evolution and growing in a sustainable manner.<\/p>\n<p><strong>Benefits of predictive management<\/strong><\/p>\n<p>The creation of digital twins of roads and cities is crucial to anticipate future events and enable key actors, such as the Directorate-General for Traffic, municipalities and emergency managers to respond appropriately.<\/p>\n<p>V2X communication is also vital for urban planning. It makes it possible to prioritize traffic according to circumstances, synchronize traffic lights, promote carpooling and optimize routes, among other things. These benefits also extend to the digitization of services such as parking search and, finally, to the interaction of V2X capability with physical vehicle systems such as braking assistants.<\/p>\n<p><\/p>\n<p><\/p>\n<p><\/p>\n<p><\/p>\n<p><\/p>\n<p>[\/et_pb_text][et_pb_button button_url=&#8221;https:\/\/www.ctengineeringgroup.com\/wp-content\/uploads\/2024\/08\/EcoMobility_CT-desarrolla-el-gemelo-digital-predictivo-de-la-ciudad-inteligente_EN.pdf&#8221; button_text=&#8221;Download the press release&#8221; button_alignment=&#8221;center&#8221; _builder_version=&#8221;4.26.1&#8243; _module_preset=&#8221;default&#8221; custom_button=&#8221;on&#8221; button_text_color=&#8221;#707372&#8243; button_bg_color=&#8221;#f7f7f7&#8243; button_border_width=&#8221;0px&#8221; button_icon=&#8221;&#xe092;||divi||400&#8243; button_icon_color=&#8221;#707372&#8243; button_icon_placement=&#8221;left&#8221; button_on_hover=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221;][\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Using machine learning technology, the CT team trains decision-making models for vehicles and people, allowing the digital twin to autonomously make decisions in real time, as if it were a human mind.<\/p>\n","protected":false},"author":1,"featured_media":255323,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"<p>[et_pb_section fb_built=\"1\" _builder_version=\"4.25.1\" _module_preset=\"default\" background_color=\"#f7f7f7\" background_image=\"https:\/\/www.ctengineeringgroup.com\/wp-content\/uploads\/2024\/06\/blanco_pequeno.png\" background_size=\"custom\" background_image_width=\"0%\" background_position=\"top_left\" background_horizontal_offset=\"-10%\" background_vertical_offset=\"-100%\" da_disable_devices=\"off|off|off\" global_colors_info=\"{}\" da_is_popup=\"off\" da_exit_intent=\"off\" da_has_close=\"on\" da_alt_close=\"off\" da_dark_close=\"off\" da_not_modal=\"on\" da_is_singular=\"off\" da_with_loader=\"off\" da_has_shadow=\"on\"][et_pb_row _builder_version=\"4.25.1\" _module_preset=\"default\" global_colors_info=\"{}\"][et_pb_column type=\"4_4\" _builder_version=\"4.25.1\" _module_preset=\"default\" global_colors_info=\"{}\"][et_pb_text _builder_version=\"4.26.1\" _module_preset=\"default\" text_font=\"--et_global_body_font||||||||\" text_text_color=\"#01426a\" custom_padding=\"|10%||10%|false|true\" global_colors_info=\"{}\"]<\/p><h1 style=\"color: #01426a;\">CT has completed the IADGENOL project on automating AWE System trajectories using deep learning.<\/h1><p>[\/et_pb_text][et_pb_text _builder_version=\"4.26.1\" _module_preset=\"default\" global_colors_info=\"{}\"]<\/p><section class=\"main-banner__general main-content__large view-project \"><div class=\"main-content__medium\"><div class=\"main-content__center\"><div class=\"main-banner__general-text\"><p style=\"text-align: center;\">After two years of research, CT has successfully developed a Deep Learning-based control model that addresses the dynamic challenges of autonomous AWE system operations. In collaboration with Carlos III University of Madrid, CT showcased the results of the latest validation tests in a simulation environment for the AWES control system at the European AWES Congress, AWEC2024, in Madrid, demonstrating precise wind alignment and optimal energy generation trajectories.<\/p><\/div><\/div><\/div><\/section><div class=\"main-banner__placeholder bg-right \"><div class=\"main-single__card\">\u00a0<\/div><\/div><p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=\"1\" _builder_version=\"4.26.1\" _module_preset=\"default\" background_image=\"https:\/\/www.ctengineeringgroup.com\/wp-content\/uploads\/2024\/08\/Imagen_ensayo_marzo2024.jpeg\" min_height=\"500px\" da_disable_devices=\"off|off|off\" global_colors_info=\"{}\" da_is_popup=\"off\" da_exit_intent=\"off\" da_has_close=\"on\" da_alt_close=\"off\" da_dark_close=\"off\" da_not_modal=\"on\" da_is_singular=\"off\" da_with_loader=\"off\" da_has_shadow=\"on\"][\/et_pb_section][et_pb_section fb_built=\"1\" _builder_version=\"4.16\" da_disable_devices=\"off|off|off\" global_colors_info=\"{}\" da_is_popup=\"off\" da_exit_intent=\"off\" da_has_close=\"on\" da_alt_close=\"off\" da_dark_close=\"off\" da_not_modal=\"on\" da_is_singular=\"off\" da_with_loader=\"off\" da_has_shadow=\"on\"][et_pb_row _builder_version=\"4.16\" background_size=\"initial\" background_position=\"top_left\" background_repeat=\"repeat\" global_colors_info=\"{}\"][et_pb_column type=\"4_4\" _builder_version=\"4.16\" custom_padding=\"|||\" global_colors_info=\"{}\" custom_padding__hover=\"|||\"][et_pb_text _builder_version=\"4.26.1\" background_size=\"initial\" background_position=\"top_left\" background_repeat=\"repeat\" global_colors_info=\"{}\"]<\/p><p><\/p><p>deeper understanding of wind energy generation systems known as AWES.<\/p><p>\u201cAWES represents an innovative approach to harnessing wind at high atmospheric layers, but they face significant challenges, particularly in operational autonomy. Currently, achieving robust autonomous control is one of the main hurdles; the systems must operate completely autonomously and withstand variable weather conditions over long periods. To enhance the adaptability of traditional control systems, we studied the use of machine learning and reinforcement learning,\u201d explains Pablo Egea Herv\u00e1s, project manager at CT.<\/p><p>The primary objective of the IADGENOL initiative was the creation of a Deep Learning-based control model for the automatic trajectory control of AWE systems, as well as employing these models to understand and characterize the dynamic challenges faced by such systems. This project has been entirely carried out by CT using its own resources and the shared AWES test machine available through a collaborative agreement with Carlos III University of Madrid for the development of AWES technologies. The tasks performed range from preliminary research on the state of the art, data mining and its processing for later use in the development of models, and the development itself of data-based models and the controller, split into\u00a0<a href=\"https:\/\/www.ctengineeringgroup.com\/projects\/ct-studies-automatic-control-of-awe-system-trajectories-using-advanced-deep-learning-technologies\/\">four phases<\/a>.<\/p><p>During last month, the AWES control system developed by CT and Carlos III University of Madrid underwent real-operation validation testing after being successfully trained in a simulation environment using reinforcement learning algorithms and an incremental learning methodology. This system, which teaches the aircraft to maintain its position in the air under various conditions and to maximize the tension of the cables, demonstrated its ability to align the kite with the wind and perform figure-eight trajectories, thus optimizing energy generation.<\/p><p>The preliminary results have been presented at the European AWES congress, AWEC2024, in Madrid, highlighting the potential of these technologies to develop robust and adaptive controllers, although further research in this field is acknowledged as necessary.<\/p><p><\/p><p><\/p><p><\/p><p><\/p><p><\/p><p>[\/et_pb_text][et_pb_button button_url=\"https:\/\/www.ctengineeringgroup.com\/wp-content\/uploads\/2024\/08\/CT-has-completed-the-IADGENOL-Project-on-automating-AWE-System-trajectories-using-Deep-Learning.pdf\" button_text=\"Download the press release\" button_alignment=\"center\" _builder_version=\"4.26.1\" _module_preset=\"default\" custom_button=\"on\" button_text_color=\"#707372\" button_bg_color=\"#f7f7f7\" button_border_width=\"0px\" button_icon=\"\ue092||divi||400\" button_icon_color=\"#707372\" button_icon_placement=\"left\" button_on_hover=\"off\" global_colors_info=\"{}\"][\/et_pb_button][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=\"1_3,1_3,1_3\" _builder_version=\"4.26.1\" _module_preset=\"default\" global_colors_info=\"{}\"][et_pb_column type=\"1_3\" _builder_version=\"4.26.1\" _module_preset=\"default\" global_colors_info=\"{}\"][et_pb_image src=\"https:\/\/www.ctengineeringgroup.com\/wp-content\/uploads\/2024\/06\/CONBANDERA_REDES_0.jpeg\" title_text=\"CONBANDERA_REDES_0\" align=\"center\" _builder_version=\"4.26.1\" _module_preset=\"default\" global_colors_info=\"{}\"][\/et_pb_image][\/et_pb_column][et_pb_column type=\"1_3\" _builder_version=\"4.26.1\" _module_preset=\"default\" global_colors_info=\"{}\"][et_pb_image src=\"https:\/\/www.ctengineeringgroup.com\/wp-content\/uploads\/2024\/06\/redes_footer_0.png\" title_text=\"redes_footer_0\" align=\"center\" _builder_version=\"4.26.1\" _module_preset=\"default\" global_colors_info=\"{}\"][\/et_pb_image][\/et_pb_column][et_pb_column type=\"1_3\" _builder_version=\"4.26.1\" _module_preset=\"default\" global_colors_info=\"{}\"][et_pb_image src=\"https:\/\/www.ctengineeringgroup.com\/wp-content\/uploads\/2024\/06\/EN_Funded_by_the_European_Union_RGB_POS.png\" title_text=\"EN_Funded_by_the_European_Union_RGB_POS\" _builder_version=\"4.26.1\" _module_preset=\"default\" global_colors_info=\"{}\"][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=\"1_3,1_3,1_3\" _builder_version=\"4.26.1\" _module_preset=\"default\" global_colors_info=\"{}\"][et_pb_column type=\"1_3\" _builder_version=\"4.26.1\" _module_preset=\"default\" global_colors_info=\"{}\"][et_pb_image src=\"https:\/\/www.ctengineeringgroup.com\/wp-content\/uploads\/2024\/06\/Logo-PRTR-tres-lineas_COLOR.png\" title_text=\"Logo-PRTR-tres-li\u0301neas_COLOR\" align=\"center\" _builder_version=\"4.26.1\" _module_preset=\"default\" global_colors_info=\"{}\"][\/et_pb_image][\/et_pb_column][et_pb_column type=\"1_3\" _builder_version=\"4.26.1\" _module_preset=\"default\" global_colors_info=\"{}\"][\/et_pb_column][et_pb_column type=\"1_3\" _builder_version=\"4.26.1\" _module_preset=\"default\" global_colors_info=\"{}\"][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=\"4.25.1\" _module_preset=\"default\" global_colors_info=\"{}\"][et_pb_column type=\"4_4\" _builder_version=\"4.25.1\" _module_preset=\"default\" global_colors_info=\"{}\"][et_pb_text _builder_version=\"4.25.1\" _module_preset=\"default\" text_font=\"CTgroup Bold||||||||\" text_text_color=\"#f2a900\" text_font_size=\"24px\" custom_margin=\"||-1px||false|false\" custom_padding=\"||50px||false|false\" global_colors_info=\"{}\"]<\/p><div class=\"blog-heading main-content__medium\"><h2 class=\"main-title__general title-left\">Related Insights<\/h2><\/div><div class=\"projects-list__content projects-list__content-border main-content__medium\">\u00a0<\/div><p>[\/et_pb_text][et_pb_blog fullwidth=\"off\" posts_number=\"3\" include_categories=\"current\" _builder_version=\"4.25.1\" _module_preset=\"default\" global_colors_info=\"{}\"][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>","_et_gb_content_width":"","footnotes":""},"categories":[36],"tags":[95],"class_list":["post-255321","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","tag-ecomobility"],"yoast_head":"\n<title>EcoMobility: CT develops predictive digital twin for road traffic in smart cities. - The CT Engineering Group<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ctengineeringgroup.com\/ecomobility-ct-develops-predictive-digital-twin-for-road-traffic-in-smart-cities\/\" \/>\n<meta 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