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In the transportation sector, VRLabs has developed a tool for the identification of audio events, source localization, and urban traffic flow forecasting.
VRLabs is developing innovative sensors for monitoring the welding process of large metal slabs in shipbuilding.
AI & Transportation
Artificial intelligence (AI) plays a crucial role in improving transportation systems by analyzing various factors like human errors, accidents, and economic situations. It helps forecast different situations, enabling informed decision-making. AI monitors intersections and pedestrian and cyclist paths to detect accidents and enhance safety. It also researches traffic patterns to identify the causes of delays and congestion.
Overall, AI in transportation significantly enhances our daily lives.
Short-term urban traffic forecasting
Given observations of relevant traffic indicators on an urban road network, forecast the observation in the near future.
Urban area of the city of Genoa (north-west of Italy)
Monitoring of sheet metal welding procedure in shipyards
Application of several types of sensors to guide the operator in the welding procedure
Leather Optimal Nesting
Optimal positioning of complex shapes on leather for the production of objects such as shoes or bags
Combinatorial Optimization problem with constraints, such as minimization of leather waste and color nuance.
Solution exploiting Evolutionary Calculation methods
Success Stories
VRLabs provides unique Skills in Artificial Intelligence technology and matches them with Market and Trends Knowledge in the various industries to help companies with new Models to Boost Profitability.
- Neural networks for tide forecasts in the Venice lagoon
- Fuzzy logic for electric hybrid vehicles operation control
- Neural networks for nonlinear processes modelling
- Traffic monitoring and prediction
- Intelligent Lamp & audio sensor installation
Tracking Time Evolving Data Streams for Short-Term Traffic Forecasting
Francesco Masulli, Stefano Rovetta, Amr Abdullatif.
Tracking Time Evolving Data Streams for Short-Term Traffic
Forecasting
Received: 12 June 2017 / Revised: 1 October 2017 / Accepted: 2 October 2017 / Published online: 24 October 2017
The Author(s) 2017. This article is an open access publication