In the last few years, with the exponential diffusion of smartphones, services for turn-by-turn navigation have seen a surge in popularity. Current solutions available in the market allow the user to select via an interface the desired transportation mode, for which an optimal route is then computed. Automatically recognizing the transportation system that the user is travelling by allows to dynamically control, and consequently update, the route proposed to the user. Such a dynamic approach is an enabling technology for multi-modal transportation planners, in which the optimal path and its associated transportation solutions are updated in real-time based on data coming from (i) distributed sensors (e.g., smart traffic lights, road congestion sensors, etc.); (ii) service providers (e.g., car-sharing availability, bus waiting time, etc.); and (iii) the user’s own device, in compliance with the development of smart cities envisaged by the 5G architecture. In this paper, we present a series of Machine Learning approaches for real-time Transportation Mode Recognition and we report their performance difference in our field tests. Several Machine Learning-based classifiers, including Deep Neural Networks, built on both statistical feature extraction and raw data analysis are presented and compared in this paper; the result analysis also highlights which features are proven to be the most informative ones for the classification.
Intelligent Transportation Systems (ITS) are changing the way people plan a journey and travel around the world. Advanced mobility information systems, as well as intelligent multimodal mobility services, may take considerable advantage of consolidated technologies from emerging ICT frameworks. In this paper we propose an Extended Intelligent Transportation System (ExITS) consisting of a basic ITS equipped with a User Centric Control System (UCCS). The proposed ExITS relies on service personalization methodologies and is conceived as a Future Internet (FI) oriented, closed-loop, user centric architecture integrating and controlling ITS services. The proposed UCCS considers the trip planning service and takes into account both explicit and implicit user preferences in selecting travel solutions satisfying a given user request. The aim of the UCCS is to drive the trip planning service in proposing to the user travel typologies tailored to personal preferences. Implicit preferences are automatically inducted by similarity based unsupervised machine learning techniques and verified by a closed-loop control mechanism triggered by explicit user feedback
In the framework of the Future Internet, the aim of the Quality of Experience (QoE) Control functionalities is to track the personalized desired QoE level of the applications. The paper proposes to perform such a task by dynamically selecting the most appropriate Classes of Service (among the ones supported by the network), this selection being driven by a novel heuristic Multi-Agent Reinforcement Learning (MARL) algorithm. The paper shows that such an approach offers the opportunity to cope with some practical implementation problems: in particular, it allows to face the so-called "curse of dimensionality" of MARL algorithms, thus achieving satisfactory performance results even in the presence of several hundreds of Agents.
Multimodal travel planning services allow travelers to plan their journey by combining different transport modes: air, rail, waterborne, coach, public transport, demand responsive transport, walking, cycling, etc. The European Union is fostering the development of cross-border multimodal planning services by establishing a regulation framework for their coordinated and coherent deployment across Member States (under the Directive 2010/40/EU). This EU regulation gives precise requirements on travel data formats (DATEX II, SIRI, NeTEx, etc.) and on fundamental and recommended system-level services, such as discovery and linking services. However, it does not (yet) pose constraints on how to implement them. In this paper, we devise and test a system architecture, named Bonvoyage, which proposes an innovative solution implementing such services. For discovery purposes, it federates nation-wide NoSQL databases that contain travel information by exploiting a novel telecommunication paradigm, Information Centric Networking. As regards linking purposes, it orchestrates the use of autonomous monomodal or multimodal routing services provided by small/big stakeholders to compose the best door-to-door journey.
A cylindrical hydrogel tube, completely submerged in water, hydrates by swelling and filling its internal cavity. When it comes back into contact with air, it dehydrates: the tube thus expels the solvent through the walls, shrinking. This dehydration process causes a depression in the tube cavity, which can lead to circumferential buckling. Here we study the occurrence of such buckling using a continuous model that combines nonlinear elasticity with Flory–Rehner theory, to take into account both the large deformations and the active behaviour of the hydrogel. In quasi-static approximation, we use the incremental deformation formalism, extended to the chemo-mechanical equations, to determine the threshold value of the enclosed volume at which buckling is triggered. This critical value is found to depend on the shell thickness, chemical potential and constitutive features. The results obtained are in good agreement with the results of the finite element simulations of the complete dynamic problem.
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