{"id":36568,"date":"2020-02-03T02:38:40","date_gmt":"2020-02-03T02:38:40","guid":{"rendered":"https:\/\/www.irap.org\/?post_type=project&p=36568"},"modified":"2026-02-17T00:48:38","modified_gmt":"2026-02-17T00:48:38","slug":"ai-rap","status":"publish","type":"project","link":"https:\/\/irap.org\/es\/project\/ai-rap\/","title":{"rendered":"Ai-RAP"},"content":{"rendered":"

[et_pb_section fb_built=”1″ _builder_version=”4.27.5″ hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″][et_pb_row _builder_version=”4.16″ global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_image src=”https:\/\/mcusercontent.com\/5040fa0f746030d8e42f73d8e\/images\/8aa713eb-69a6-b545-01d6-c56ee4f82441.png” title_text=”aiRAP_Logo.4K_UHD.RGB” show_bottom_space=”off” _builder_version=”4.16″ custom_padding=”5px||5px||false|false” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.16″ custom_margin=”||0px||false|false” custom_padding=”||0px||false|false” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ global_colors_info=”{}”]<\/p>\n

What is AiRAP?<\/strong><\/h2>\n

Data is increasingly considered as critical to addressing the global road safety crisis. The UN Second Decade of Action aims to halve fatal and serious injury road crashes by 2030. For this to happen, the tools and methods used to assess and address high risk roads need to become integrated into every aspect of a road network\u2019s planning, design, investment, construction, management and maintenance.<\/p>\n

The AiRAP initiative was conceived by iRAP in 2019 to help improve access to, and application of, existing and emerging data sources globally, including advances in artificial intelligence, machine learning, vision systems, LIDAR, telematics and other data sources. AiRAP\u00a0stands for the \u2018accelerated and intelligent\u2019 capture of\u00a0road safety-related data\u00a0using automatic, repeatable and scalable methods to support road safety assessment, crash risk mapping, investment prioritisation for all road users.<\/p>\n

\u00a0iRAP’s new AiRAP accreditation ensures this data meets iRAP\u2019s common global standard, which is already being used globally for recording of road features that impact road safety to streamline and benchmark performance.<\/p>\n

[\/et_pb_text][et_pb_text _builder_version=”4.16″ global_colors_info=”{}”]<\/p>\n

How do I become an AiRAP data supplier?<\/strong><\/h2>\n

iRAP has established a new AiRAP accreditation for the conversion of source data into iRAP attribute data. The AiRAP accreditation process will ensure data suppliers produce data in accordance with iRAP\u2019s global standard format, regardless of the source of the data.<\/p>\n

The accreditation process removes the need for complex data processing and storage for the data consumer. It also provides an understanding of the\u00a0 reliability of the data for different geographic regions, area types and road types, as well as when and how the data should be used. The process is flexible and accommodates data derived from different types of source data, as well as different collection and processing methods.<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_4,3_4″ _builder_version=”4.16″ _module_preset=”default” custom_margin=”20px||5px||false|false” custom_padding=”0px||5px||false|false” global_colors_info=”{}”][et_pb_column type=”1_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/mcusercontent.com\/5040fa0f746030d8e42f73d8e\/images\/b09e39c1-4985-6183-7525-1d5c570066ef.png” url=”https:\/\/resources.irap.org\/Key-documents\/AiRAP_Attribute_Accred_Process.pdf” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][et_pb_button button_url=”https:\/\/resources.irap.org\/Key-documents\/AiRAP_Attribute_Accred_Process.pdf” button_text=”Download the Process for AiRAP Attribute Accreditation document here” _builder_version=”4.16″ _module_preset=”default” custom_button=”on” button_text_size=”12px” button_bg_color=”#35809f” global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/irap.org\/wp-content\/uploads\/2020\/02\/New-AiRAP-supplier-13.png” title_text=”New AiRAP supplier (13)” force_fullwidth=”on” _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.16″ custom_margin=”10px||0px||false|false” custom_padding=”||0px||false|false” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ _module_preset=”default” custom_margin=”||10px||false|false” global_colors_info=”{}”]<\/p>\n

AiRAP news:<\/strong><\/h2>\n

[\/et_pb_text][et_pb_post_slider posts_number=”20″ include_categories=”624″ more_text=”Read more” show_meta=”off” image_placement=”left” _builder_version=”4.27.4″ _module_preset=”default” header_font_size=”20px” background_color=”#35809f” custom_margin=”10px||10px||false|false” custom_padding=”10px||10px||false|false” auto=”on” auto_ignore_hover=”on” global_colors_info=”{}”][\/et_pb_post_slider][et_pb_text _builder_version=”4.16″ custom_margin=”10px||||false|false” custom_padding=”20px||||false|false” global_colors_info=”{}”]<\/p>\n

AiRAP R&D projects<\/strong><\/h2>\n

AiRAP is being piloted as part of a number of research and innovation projects globally.<\/p>\n

[\/et_pb_text][et_pb_text admin_label=”Leveraging Artificial Intelligence and Big Data to Enhance Safety Analysis” _builder_version=”4.16″ _module_preset=”default” custom_margin=”||0px||false|false” custom_padding=”||0px||false|false” global_colors_info=”{}”]<\/p>\n

The\u00a0Leveraging Artificial Intelligence and Big Data to Enhance Safety Analysis<\/em><\/strong> research project will\u00a0investigate the use of artificial intelligence (AI), machine learning (ML) and Big Data (BD) to\u00a0improve the visibility of existing network conditions with a focus on road and exposure features influencing the safety of all road users across the entire road network.<\/p>\n

[\/et_pb_text][et_pb_button button_url=”https:\/\/apps.trb.org\/cmsfeed\/TRBNetProjectDisplay.asp?ProjectID=5087″ button_text=”Click here to find out more” _builder_version=”4.16″ _module_preset=”default” custom_button=”on” button_text_size=”16px” button_bg_color=”#35809f” custom_margin=”20px||0px||false|false” global_colors_info=”{}”][\/et_pb_button][et_pb_image src=”https:\/\/irap.org\/wp-content\/uploads\/2020\/02\/AiRAP-logos-for-website-6.png” title_text=”AiRAP logos for website (6)” show_bottom_space=”off” _builder_version=”4.16″ _module_preset=”default” custom_margin=”5px||20px||false|false” custom_padding=”0px||0px||false|false” global_colors_info=”{}”][\/et_pb_image][et_pb_divider color=”#35809f” divider_weight=”2px” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_divider][et_pb_text admin_label=”AiRAP Automation for Australian Road Safety” _builder_version=”4.16″ _module_preset=”default” custom_margin=”||0px||false|false” custom_padding=”||0px||false|false” global_colors_info=”{}”]<\/p>\n

The AiRAP Automation for Australian Road Safety<\/em><\/strong> project is funded by iMOVE CRC and supported by the Cooperative Research Centres program, an Australian Government initiative. The project will deliver 20,000 kilometres of road attribute data for the state road network in New South Wales using TomTom\u2019s MN-R data, as well as prove feature extraction techniques and machine learning for LiDAR data.<\/p>\n

[\/et_pb_text][et_pb_button button_url=”https:\/\/imoveaustralia.com\/project\/accelerated-and-intelligent-rap-data-collection\/” button_text=”Click here to find out more” _builder_version=”4.16″ _module_preset=”default” custom_button=”on” button_text_size=”16px” button_bg_color=”#35809f” custom_margin=”20px||0px||false|false” global_colors_info=”{}”][\/et_pb_button][et_pb_image src=”https:\/\/irap.org\/wp-content\/uploads\/2020\/02\/AiRAP-logos-for-website-9.png” title_text=”AiRAP logos for website (9)” show_bottom_space=”off” _builder_version=”4.19.4″ _module_preset=”default” custom_margin=”5px||20px||false|false” custom_padding=”0px||0px||false|false” global_colors_info=”{}”][\/et_pb_image][et_pb_divider color=”#35809f” divider_weight=”2px” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_divider][et_pb_text admin_label=”AI&Me: Leveraging AI Tools for Road Safety Impact Project” _builder_version=”4.27.5″ _module_preset=”default” custom_margin=”0px||0px||false|false” custom_padding=”0px||0px||false|false” global_colors_info=”{}”]<\/p>\n

The AI&Me: Leveraging AI Tools for Road Safety Impact Project,<\/em><\/strong> funded by Google.org, uses big data and artificial intelligence to map pedestrian risk across Vietnam and enhance road safety. By applying advanced AI models, the project identifies high-risk areas for pedestrians and optimizes Star Rating for Schools (SR4S)\u00a0analyses, enabling faster, more accurate assessments and scalable interventions to improve safety for the schools’ communities.<\/p>\n

[\/et_pb_text][et_pb_button button_url=”https:\/\/irap.org\/2023\/09\/irap-receives-google-support-to-advance-the-un-sdgs-and-road-safety\/” button_text=”Click here to find out more” _builder_version=”4.27.5″ _module_preset=”default” custom_button=”on” button_text_size=”16px” button_bg_color=”#35809f” custom_margin=”20px||0px||false|false” global_colors_info=”{}”][\/et_pb_button][et_pb_image src=”https:\/\/irap.org\/wp-content\/uploads\/2020\/02\/Leveraging-AI-Tools-for-Road-Safety-Impact-Project-1.png” title_text=”Leveraging AI Tools for Road Safety Impact Project” show_bottom_space=”off” _builder_version=”4.27.5″ _module_preset=”default” custom_margin=”0px||20px||false|false” custom_padding=”0px||0px||false|false” global_colors_info=”{}”][\/et_pb_image][et_pb_divider color=”#35809f” divider_weight=”2px” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_divider][et_pb_text admin_label=”AI&Me Empowering Youth for Safer Roads” _builder_version=”4.16″ _module_preset=”default” custom_margin=”0px||0px||false|false” custom_padding=”0px||0px||false|false” global_colors_info=”{}”]<\/p>\n

The AI&Me Empowering Youth for Safer Roads<\/em><\/strong> project is funded by Fondation Botnar and supported by the FIA Foundation. The project is using big data and AI to map pedestrian risk around 1,000 schools in Vietnam and develop an app to connect young voices with decision-makers for safer roads.<\/p>\n

[\/et_pb_text][et_pb_button button_url=”https:\/\/starratingforschools.org\/2021\/07\/big-data-and-youth-voices-unite-for-safer-schools-in-vietnam\/” button_text=”Click here to find out more” _builder_version=”4.27.5″ _module_preset=”default” custom_button=”on” button_text_size=”16px” button_bg_color=”#35809f” custom_margin=”20px||0px||false|false” global_colors_info=”{}”][\/et_pb_button][et_pb_button button_url=”https:\/\/resources.irap.org\/CRM\/Asia_Pacific\/Vietnam\/2022_Ai_and_Me_Big_Data_Analysis_Report.pdf” url_new_window=”on” button_text=”Read the Project Report – Big Data Analysis: Methodology for Assessing High-risk Schools” _builder_version=”4.16″ _module_preset=”default” custom_button=”on” button_text_size=”16px” button_bg_color=”#35809f” custom_margin=”20px||0px||false|false” global_colors_info=”{}”][\/et_pb_button][et_pb_image src=”https:\/\/irap.org\/wp-content\/uploads\/2020\/02\/AiRAP-logos-for-website-1.png” title_text=”AiRAP logos for website (1)” show_bottom_space=”off” _builder_version=”4.16″ _module_preset=”default” custom_margin=”0px||20px||false|false” custom_padding=”0px||0px||false|false” global_colors_info=”{}”][\/et_pb_image][et_pb_divider color=”#35809f” divider_weight=”2px” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_divider][et_pb_text admin_label=”Regional Road Safety Observatories (RRSOs) Road Safety data collection in LMICs” _builder_version=”4.20.2″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

The Regional Road Safety Observatories (RRSOs) Road Safety data collection in LMICs<\/em><\/strong> project is funded by the World Bank. The project piloted the use of satellite data, amongst others, to map where 75% of travel occurs across two countries, Kenya and Ethiopia, and trialed the detection of speed, flow and other physical road features for the measuring and monitoring of road safety key performance indicators (KPIs).<\/p>\n

[\/et_pb_text][et_pb_button button_url=”https:\/\/irap.org\/2022\/12\/pioneering-use-of-ai-scoops-prince-michael-royal-road-safety-award\/” button_text=”Click here to find out more” disabled_on=”off|off|off” _builder_version=”4.20.2″ _module_preset=”default” custom_button=”on” button_text_size=”16px” button_bg_color=”#35809f” custom_margin=”20px||0px||false|false” global_colors_info=”{}”][\/et_pb_button][et_pb_image src=”https:\/\/irap.org\/wp-content\/uploads\/2020\/02\/AiRAP-logos-for-website-5.png” title_text=”AiRAP logos for website (5)” show_bottom_space=”off” disabled_on=”off|off|off” _builder_version=”4.19.4″ _module_preset=”default” custom_margin=”0px||20px||false|false” custom_padding=”0px||0px||false|false” global_colors_info=”{}”][\/et_pb_image][et_pb_divider color=”#35809f” divider_weight=”2px” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_divider][et_pb_text admin_label=”V\u00edaSegura” _builder_version=”4.16″ _module_preset=”default” custom_margin=”0px||0px||false|false” custom_padding=”0px||0px||false|false” global_colors_info=”{}”]<\/p>\n

In partnership iRAP, the IDB has developed V\u00edaSegura<\/em><\/strong>,\u00a0a digital initiative to assess road infrastructure safety. The tool focuses on evaluating a set of variables used in the Star Rating methodology.<\/p>\n

[\/et_pb_text][et_pb_button button_url=”https:\/\/irap.org\/2022\/02\/viasegura-digital-technology-for-improving-road-safety\/” button_text=”Click here to find out more” _builder_version=”4.16″ _module_preset=”default” custom_button=”on” button_text_size=”16px” button_bg_color=”#35809f” custom_margin=”20px||0px||false|false” global_colors_info=”{}”][\/et_pb_button][et_pb_image src=”https:\/\/irap.org\/wp-content\/uploads\/2020\/02\/AiRAP-logos-for-website-7.png” title_text=”AiRAP logos for website (7)” show_bottom_space=”off” _builder_version=”4.16″ _module_preset=”default” custom_margin=”0px||20px||false|false” custom_padding=”0px||0px||false|false” global_colors_info=”{}”][\/et_pb_image][et_pb_divider color=”#35809f” divider_weight=”2px” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_divider][et_pb_text admin_label=”Using Convolutional Neural Networks To Improve Road Safety And Save Lives” _builder_version=”4.16″ _module_preset=”default” custom_margin=”0px||0px||false|false” custom_padding=”0px||0px||false|false” global_colors_info=”{}”]<\/p>\n

In early 2021, more than 30 machine learning engineers, subject matter experts, and mentors collaborated as part of an\u00a0Omdena challenge<\/a>\u00a0to work towards iRAP\u2019s vision of \u201ca world free of high-risk roads. The project, titled\u00a0Using Convolutional Neural Networks To Improve Road Safety And Save Lives<\/em><\/strong>,\u00a0built AI based solutions to increase road safety by mapping the crash risk on roads.<\/p>\n

[\/et_pb_text][et_pb_button button_url=”https:\/\/omdena.com\/projects\/ai-road-safety\/” button_text=”Click here to find out more” _builder_version=”4.16″ _module_preset=”default” custom_button=”on” button_text_size=”16px” button_bg_color=”#35809f” custom_margin=”20px||0px||false|false” global_colors_info=”{}”][\/et_pb_button][et_pb_image src=”https:\/\/irap.org\/wp-content\/uploads\/2020\/02\/AiRAP-logos-for-website-4.png” title_text=”AiRAP logos for website (4)” show_bottom_space=”off” _builder_version=”4.16″ _module_preset=”default” custom_margin=”0px||20px||false|false” custom_padding=”0px||0px||false|false” global_colors_info=”{}”][\/et_pb_image][et_pb_divider color=”#35809f” divider_weight=”2px” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_divider][et_pb_text admin_label=”SLAIN (Saving Lives Assessing and Improving TEN-T road Network safety)” _builder_version=”4.16″ _module_preset=”default” custom_margin=”0px||0px||false|false” custom_padding=”0px||0px||false|false” global_colors_info=”{}”]<\/p>\n

The European Commission CEF funded project SLAIN<\/em><\/strong> (Saving Lives Assessing and Improving TEN-T road Network safety)<\/em><\/strong> aims to extend the skills and knowledge base of partners in performing network-wide road assessment. Deliverables 7.3 and 7.4 relate to the methodology and improvement of automatic road-attribute coding for network-wide assessments.<\/p>\n

[\/et_pb_text][et_pb_button button_url=”https:\/\/eurorap.org\/slain-project\/%20″ button_text=”Click here to find out more” _builder_version=”4.16″ _module_preset=”default” custom_button=”on” button_text_size=”16px” button_bg_color=”#35809f” custom_margin=”20px||0px||false|false” global_colors_info=”{}”][\/et_pb_button][et_pb_image src=”https:\/\/irap.org\/wp-content\/uploads\/2020\/02\/AiRAP-logos-for-website-3.png” title_text=”AiRAP logos for website (3)” show_bottom_space=”off” _builder_version=”4.16″ _module_preset=”default” custom_margin=”0px||20px||false|false” custom_padding=”0px||0px||false|false” global_colors_info=”{}”][\/et_pb_image][et_pb_divider color=”#35809f” divider_weight=”2px” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_divider][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

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For more information<\/strong><\/h2>\n

Please contact:<\/strong><\/span>
Monica Olyslagers<\/span>
Global Innovation Manager and Cities Specialist<\/em><\/span>
monica.olyslagers@irap.org<\/a><\/p>\n

Andrejus Laugman<\/span>
Technical Development Manager<\/em><\/span>
andrejus.laugman@irap.org<\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n

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\u00a0<\/div>\n<\/div>\n

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