SOLUTIONS

The Mobility Observation Box (MOB) makes it possible to measure the safety of transport infrastructures according to objective criteria and thus make them comparable. Once data collection is done, machine learning algorithms automatically recognize different groups of road users (pedestrians, cyclists, cars, trucks, e-scooters, etc.), detects them, evaluates their traffic behavior using surrogate safety measures and provides a basis for targeted mitigation measures.  The battery-operated system allows a quick and uncomplicated installation and deinstallation of the box, not requiring a supplemental power source. Due to its small size, data collection of all traffic participants can be realized without distraction or influence. 

ITAINNOVA is going to develop Digital Twin, virtual copy of a real-world item or procedure. It is a dynamic model , created with historical data and updated in real-time to represent the physical object or process. Digital twins track, examine, and forecast how physical things or processes will behave. They can boost performance, reduce downtime, and increase efficiency.  

Digital twins have the potential to revolutionize the mobility and transport industries. They can be used to improve efficiency, reduce congestion, and make transportation more sustainable.  Traffic management: Digital twins can predict traffic conditions  with behavioral hypothesis. This information can be used to optimize traffic lights, reroute traffic around accidents, and provide drivers with  updates on traffic conditions. The trafic scenarios can be updated with information about the  status of the infrastructure, such as roads, bridges, and tunnels. This information can be used to identify potential problems before they cause significant disruptions and to plan for maintenance and repairs. 

 Public transportation: Digital twins can be used to improve the efficiency of dynamic public transportation systems. They can be used to optimize routes, schedules, and fares.