Artificial intelligence focused on
prediction
- to identify movement patterns, levels of vehicle/people flows, etc., being able to identify bottlenecks and traffic peaks in advance. (Project reference: TIMON - H2020 project)
- design, development and validation of new methodologies for the extraction and comparison of mobility patterns based on the fusion of heterogeneous data sources (transport supply and demand, maps and cartography, socio-demographic and travel time information). Collection of data sources, harmonisation and integration according to suitable standard formats by developing scalable processing flows. (Project reference: MOMENTUM - H2020 project)
Artificial intelligence focused on
optimisation
- for the design and implementation of sustainable routes, combining different means of transport and being able to prioritise different parameters, such as: distance, time, eco-efficiency, safety, cost, etc. Modal routes for a) Vulnerable Road Users (VRU), for cyclists, motorcyclist; b) car drivers. (Project reference: TIMON - H2020 project)
- in the logistic domain: o dynamic and multi-objective optimisation applied to dynamic deliveries planning. (Project reference: SENATOR - H2020 project)
o a system for planning and optimising routes for the pick-up and delivery of goods by fleets of trucks, considering horizontal collaboration between Fast Consumer Goods Companies (FMCG), in order that a vehicle from a company can deliver goods from others, establishing a win-win collaboration. (Project reference: LOGISTAR - H2020 project)
Techniques of artificial intelligence: metaheuristics and machine learning (deep learning,
fuzzy systems, etc.)
Continuous positioning (outdoor and indoor environments) of people and goods, using IoT and smart devices. Obtaining the position in real time from door to door. The techniques that have been researched on, are Kalman filters and Bayesian techniques, which help to increase the accuracy on seamless positioning and on determining the
activity type. In the case of positioning people, this information can be exploited to identify areas of high density of people, for example, in commercial areas of airports, supporting on the space distribution planning,
pricing strategies, etc. Further information available in: http://cloud.mobility.deustotech.eu/blueteam/
