{"id":252291,"date":"2022-09-07T07:44:18","date_gmt":"2022-09-07T07:44:18","guid":{"rendered":"https:\/\/ingenieros.en-desarrollo.net\/?post_type=project&p=252291"},"modified":"2024-10-17T14:52:55","modified_gmt":"2024-10-17T14:52:55","slug":"ct-successfully-completes-the-digital-hbe-twin-project-a-smart-interaction-platform-for-buses-based-on-digital-twins-and-occupational-and-environmental-health-monitoring","status":"publish","type":"project","link":"https:\/\/www.ctengineeringgroup.com\/project\/ct-successfully-completes-the-digital-hbe-twin-project-a-smart-interaction-platform-for-buses-based-on-digital-twins-and-occupational-and-environmental-health-monitoring\/","title":{"rendered":"CT successfully completes the Digital HBE Twin project, a smart interaction platform for buses based on digital twins and occupational and environmental health monitoring"},"content":{"rendered":"

[et_pb_section fb_built=”1″ _builder_version=”4.25.1″ _module_preset=”default” 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=”4.25.1″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

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Measuring the way cities move is a key vector to achieve sustainable mobility that benefits both passengers and workers, and the environment in general. Along this line, over the course of 2022, CT developed the\u00a0<\/span>Digital Human<\/strong>\u2010<\/strong>Bus Environment Twin project,\u00a0<\/span><\/strong>a new\u00a0<\/span>technological solution to control bus fleet operations\u00a0<\/span><\/strong>that immediately gives a better understanding of passenger transport in the city.<\/p>\n

To do this, the CT team deployed digital twins of the fleet\u2019s buses in a geo-specific virtual environment. The system that was developed includes the collection of telemetry data from the bus, remote monitoring of driver workloads and environmental conditions (CO2, temperature and seat use, among others). This information collected in the project demonstrator gives a better understanding of passenger transport in the city immediately, thanks to the implementation of technologies such as Big Data.<\/p>\n

Some of the advantages of this system include:<\/p>\n

    \n
  • Use of synthetic data for AI training.\n
      \n
    • Predict the fuel consumption of buses.<\/li>\n
    • Predict tire wear.<\/li>\n<\/ul>\n<\/li>\n
    • Optimize routes based on conditions in the city.<\/li>\n
    • Quickly create Digital Twins of urban mobility.<\/li>\n<\/ul>\n

      The developed technology allows data and fleet operation to be analysed under different virtual scenarios. These developments will serve as a basis for integrating future research and development in the area of urban mobility.<\/p>\n

      About<\/strong>\u00a0<\/span>Digital HBE Twin<\/strong><\/p>\n

      The project with dossier number 2020\/0720\/00099966, was awarded by Red.es and was co-financed through the European Regional Development Fund (ERDF).<\/strong><\/p>\n<\/div>\n<\/div>\n<\/div>\n

      [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ fullwidth=”on” _builder_version=”4.25.1″ _module_preset=”default” 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_fullwidth_slider _builder_version=”4.25.1″ _module_preset=”default” min_height=”500px” global_colors_info=”{}”][et_pb_slide _builder_version=”4.25.1″ _module_preset=”default” background_enable_color=”off” background_image=”https:\/\/www.ctengineeringgroup.com\/wp-content\/uploads\/2024\/06\/Bus-twin1.jpeg” background_enable_image=”on” global_colors_info=”{}” sticky_transition=”on”][\/et_pb_slide][et_pb_slide _builder_version=”4.25.1″ _module_preset=”default” background_enable_color=”off” background_image=”https:\/\/www.ctengineeringgroup.com\/wp-content\/uploads\/2024\/06\/Bus-twin-2.jpeg” background_enable_image=”on” global_colors_info=”{}” sticky_transition=”on”][\/et_pb_slide][\/et_pb_fullwidth_slider][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.25.1″ _module_preset=”default” 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_button button_url=”https:\/\/www.ctengineeringgroup.com\/wp-content\/uploads\/2024\/06\/CT-acheve-avec-succes-le-projet-Digital-HBE-Twin-une-plateforme-dinteraction-intelligente-pour-les-autobus-basee-sur-des-jumea.pdf” button_text=”Download the project information” button_alignment=”center” _builder_version=”4.25.1″ _module_preset=”default” custom_button=”on” button_text_color=”#707372″ button_bg_color=”#f7f7f7″ button_border_width=”0px” button_icon=”||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=”4.25.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_3″ _builder_version=”4.25.1″ _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″ _builder_version=”4.25.1″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.25.1″ _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=”4.25.1″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.25.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/www.ctengineeringgroup.com\/wp-content\/uploads\/2024\/06\/LOGO-FEDER-en.png” title_text=”LOGO-FEDER-en” _builder_version=”4.25.1″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"

      Measuring the way cities move is a key vector to achieve sustainable mobility that benefits both passengers and workers, and the environment in general. Along this line, over the course of 2022, CT developed the\u00a0Digital Human\u2010Bus Environment Twin project,\u00a0a new\u00a0technological solution to control bus fleet operations\u00a0that immediately gives a better understanding of passenger transport in the city. To do this, the CT team deployed digital twins of the fleet\u2019s buses in a geo-specific virtual environment. The system that was developed includes the collection of telemetry data from the bus, remote monitoring of driver workloads and environmental conditions (CO2, temperature and seat use, among others). This information collected in the project demonstrator gives a better understanding of passenger transport in the city immediately, thanks to the implementation of technologies such as Big Data. Some of the advantages of this system include: Use of synthetic data for AI training. Predict the fuel consumption of buses. Predict tire wear. Optimize routes based on conditions in the city. Quickly create Digital Twins of urban mobility. The developed technology allows data and fleet operation to be analysed under different virtual scenarios. These developments will serve as a basis for integrating future research and development in […]<\/p>\n","protected":false},"author":1,"featured_media":251349,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"project_category":[97,106],"project_tag":[122],"class_list":["post-252291","project","type-project","status-publish","has-post-thumbnail","hentry","project_category-automotive","project_category-rd","project_tag-digital-transformation"],"yoast_head":"\nCT successfully completes the Digital HBE Twin project, a smart interaction platform for buses based on digital twins and occupational and environmental health monitoring - 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\/project\/ct-successfully-completes-the-digital-hbe-twin-project-a-smart-interaction-platform-for-buses-based-on-digital-twins-and-occupational-and-environmental-health-monitoring\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"CT successfully completes the Digital HBE Twin project, a smart interaction platform for buses based on digital twins and occupational and environmental health monitoring - The CT Engineering Group\" \/>\n<meta property=\"og:description\" content=\"Measuring the way cities move is a key vector to achieve sustainable mobility that benefits both passengers and workers, and the environment in general. Along this line, over the course of 2022, CT developed the\u00a0Digital Human\u2010Bus Environment Twin project,\u00a0a new\u00a0technological solution to control bus fleet operations\u00a0that immediately gives a better understanding of passenger transport in the city. To do this, the CT team deployed digital twins of the fleet\u2019s buses in a geo-specific virtual environment. The system that was developed includes the collection of telemetry data from the bus, remote monitoring of driver workloads and environmental conditions (CO2, temperature and seat use, among others). This information collected in the project demonstrator gives a better understanding of passenger transport in the city immediately, thanks to the implementation of technologies such as Big Data. Some of the advantages of this system include: Use of synthetic data for AI training. Predict the fuel consumption of buses. Predict tire wear. Optimize routes based on conditions in the city. 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