{"id":3764,"date":"2019-11-26T11:51:03","date_gmt":"2019-11-26T10:51:03","guid":{"rendered":"https:\/\/www.revistaitransporte.com\/?p=3764"},"modified":"2019-12-02T07:50:26","modified_gmt":"2019-12-02T06:50:26","slug":"predicting-to-prevent-accidents","status":"publish","type":"post","link":"https:\/\/www.revistaitransporte.com\/predicting-to-prevent-accidents\/","title":{"rendered":"Predicting to prevent accidents"},"content":{"rendered":"

Ineco\u2019s RONIN innovation project was one of the three nominees for the 2019 Ponle Freno Awards, selected from among 105 proposals entered in the AXA Road Safety Innovation and Development category.<\/i> This category recognises innovative products or designs that represent new scientific\/technological developments in the improvement of road safety and that show a high potential for industrial transfer, enabling them to be put to use for the benefit of road users and society in general. The Ponle Freno Awards are an initiative of Atresmedia and AXA and aim to recognise people, institutions and initiatives that contribute to promoting road safety and, consequently, help reduce the number of accidents on Spain\u2019s roads.<\/p>\n

Prior to this award, RONIN won first place for Sustainable Development Goal 9 Industry, Innovation and Infrastructure,<\/i> for helping to reduce accidents and optimise the predictive maintenance of the road network. The prize was awarded by the Spanish Global Compact Network, the most important initiative for the sustainability of the private sector promoted by the United Nations and Rafael del Pino Foundation. This award is part of the go!SDG Awards, which highlight the role of business and social entities in Spain when it comes to using innovation to achieve the UN\u2019s Sustainable Development Goals (SDGs). These are awards that highlight initiatives that are already underway and have proven results, and serve as an inspiration for the Spanish private sector as a whole, encouraging intersector collaboration and dialogue.<\/p>\n

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TEAM PHOTO. Josu\u00e9 Garc\u00eda, Iv\u00e1n Hern\u00e1ndez, Rafael Ib\u00e1\u00f1ez and Alejandro Rodr\u00edguez, members of the Ineco team that developed RONIN, with M\u00aa Sonia \u00c1lvarez and Manuel Francisco on the screen in the background.<\/p><\/div>\n

Previously, in its prototype stage, the tool was a nominee at the 3rd edition of the Innovation in Road Infrastructure Awards organised by the Spanish Road Technology Platform at Innovacarretera 2017,<\/i> in the category of best R&D project, among more than 50 proposals. Innovacarretera 2017<\/i> was the 4th edition of the biannual Road Infrastructure Sector Technology Demonstration Fair, <\/i>an event that brings together public authorities and companies in the same field in an updated format of live demonstrations of innovative products and services with road infrastructure applications.<\/p>\n

A powerful tool for road safety<\/h4>\n

RONIN, innovative software developed by Ineco, facilitates the making of strategic decisions in the field of infrastructure safety, serving as a powerful tool for public authorities and road operators, with a notable impact on users of one of the principal means of transport. The software \u2013designed to manage the road network\u2013 includes accident prediction models based on the weather, date and traffic flow.<\/p>\n

RONIN\u00a0includes\u00a0accident\u00a0prediction\u00a0models based\u00a0on the\u00a0weather,\u00a0date and\u00a0traffic flow<\/p><\/blockquote>\n

One of the main capabilities of the RONIN tool is the automatic calculation of accident black spots using formula management and black spot calculation parameterisation functionalities based on the Guidelines of the procedure\u00a0 for the management of accident black spots and State Road Network\u2019s safety classification, which makes it possible for the different available calculation methods to be applied to comply with Royal Decree 345\/2011 of 11 March, providing a major benefit to drivers, who can easily consult critical information about sections of road with high risk of accidents in real time. The system incorporates accident prediction models (random decision forests) based on the weather (connection with the AEMET API), date and traffic flow or any variable that can be provided through online services, which are used to reduce the accident rate while optimising road network maintenance, with the ability to integrate future road maintenance variables considered to be of great interest. As a result, the degree of accident risk is obtained, making it possible to rank the probability of an accident occurring on a road section in the short term (1, 2 or 3 days).<\/p>\n

In terms of road maintenance and operation, the RONIN tool enables public authorities to optimise expenditure based on greater efficiency of use of the limited resources available to them, investing them in locations where a greater risk of accident has been identified. In addition, the programme is scalable, allowing the incorporation of other modules, such as assistance in the management of improvable elements and identification of sections with high potential for improvement, which allow for longer-term planning of road improvement programmes and expected rate of return (ERR), adapting them to new regulations and, ultimately, improving their safety.<\/p>\n

Lastly, it is possible to manage the road inventory and generate reports in editable format.<\/p>\n

What is RONIN based on?<\/span><\/h4>\n\t\t\t
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RONIN DASHBOARD. To supervise and monitor the road network, RONIN has an analysis system consisting of dashboards with key performance indicators (KPIs).<\/p><\/div><\/p>\n

RONIN is a comprehensive road management web application that enables the efficient management of any road network over the course of its life cycle. The solution is based on a configurable and expandable inventory, in which all road elements are registered independently with their corresponding attributes, providing a fully scalable solution. The tool focuses mainly on road safety, specifically accident reduction, by facilitating early decision making through predictive models for infrastructure improvement, and is able to record, represent and analyse the different attributes that determine the safety of the network. Its main features are the following:<\/p>\n