ITENE optimizes last mile transport with a new application that predicts traffic

ITENE optimizes last mile transport with a new application that predicts traffic
ITENE optimizes last mile transport with a new application that predicts traffic

ITENE has presented its new application to predict traffic in Valencia and that is the result of the advances of its EMOBCONNECT projectcarried out between June 2023 and the same month this year and financed by IVACE+i, to define and develop New Intelligent Transport Systems (ITS) based on data and predictive analysis for sustainable mobilityconnected and intelligent and digitalization in transport.

This new application will allow you to know the Expected hourly and zone-wise traffic intensity throughout the city up to 10 days in advance. These data are valuable for the Optimization of delivery routes to avoid waiting times due to traffic or parking difficultiesas well as for the organisation of daily traffic in the city.

He Increase in traffic and last mile freight transport in cities, along with the application of New regulations on restricting vehicle access to certain areasopen a new scenario for the urban mobility. In this context, the New technologies can contribute to more efficient and sustainable mobility.

In this sense, the project arises EMOBCONNECTwhich has been worked on Predictive and optimization models for urban transport and mobility. The center has directed its research toward the development of a data center that, replicating real scenarios virtually, would allow the design and validation of urban mobility and transport scenarios.

Use of artificial intelligence

As part of the project objective, a Traffic prediction application in Valencia that, with an accuracy of over 90%predicts the traffic intensity in a certain area of ​​the city on a specific date and at a specific time.

For this purpose, a team has been trained Artificial intelligence model with historical data on traffic intensity throughout the city that has been cross-referenced with data relating to factors that affect mobility on a daily basissuch as weather or work schedules, among others. This has involved the cleaning and pre-treatment of a large amount of data, which, together with a parameterization exercise of different learning models, has achieved this precision of over 90%.

This application It allows traffic data to be included in urban transport planning processes such as delivery routes, loading and unloading services and event routes. public. These data can contribute to Optimize the routes of last mile delivery companieswhich offers the possibility of reorganizing their routes to avoid the most congested areas at a specific time or date. To do this, the application has both a interactive web page where you can explore the traffic map, as well as with a REST API that facilitates integration with any other software.

The results are part of the research carried out by the technological centre for the Transport, mobility and digital transformationIn this area, the centre continues to work on defining logistics and mobility models and on making possible the implementation of sustainable and connected solutions (electric, connected, autonomous vehicles, etc.) in the application of these processes.

 
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