The idea within PLAYMOBeL is to exploit the fast and effective information flow, offered by the big data collected through web and the sensors of portable technologies (nomadic and wearable devices like smartphones and smartwatches). The objective is to make individual travel options efficient and adaptive to possible changes in travellers’ activity-travel agendas, and to the valorisation of collaborative mobility sharing solutions. We therefore develop a new concept of personalised travel assistants that builds upon social networks and make use of gamification paradigms as a way to influence mobility patterns.
In this project we will develop an information and activity-travel planning framework where the decision support system will link the mobility patterns of different users within a real social environment. This will give the opportunity to offer advice looking at a specific individual state and social context, in order to exploit commonalities between activity-travel patterns to link and optimise collective services (e.g. carpooling, carsharing, parking sharing).
We aim to develop a general methodology for providing travel information and advice as a two-way feedback system. The core activity is the design of an intelligent transportation system mechanism that seeks mutual adaptation between traveller’s activity-patterns, preferences and the sharing services available.
The architecture of this new concept is later translated into software specifications, therefore implemented in a system composed by a machine learning and recommendation system platform. Using the smartphone app, travellers are supported in the activity-travel choices, and bilaterally give and obtain feedback from the recommendation system, in order to make use of the collaborative mobility services available. The system allows for quick alternative scanning and computations, information exchange and synchronisation with the devices of the users in real time.