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The ever more extensive data collection from Internet of Thing (IoT) devices stresses the need for efficient application development tools. State-of-the-art IoT cloud services are powerful, but the best solutions are proprietary, and there is a growing demand for interoperability and standardization. We have investigated how to develop a nonvendor-locked framework, which exploits state of the art data management technologies, and targets effective and efficient development in this article. Focusing on the concept of measurement, we abstracted an architecture that could be applied in a variety of domains and contexts. We tested the framework and its workflow in four use cases analyzing data and enabling new services in health, automotive, and instruction. Our experience showed the benefits of the development tool, which is not tied to a commercial platform, nor requires the huge set-up times needed to start a project from scratch. The tool is released open-source, particularly supporting collaborative research. [ABSTRACT FROM AUTHOR]

As dynamic inductive power transfer for electric vehicles is growing in relevance, it is important to analyze solutions towards its deployment and integration in the cloud-based services for electric mobility. In this paper we present an Internet-enabling platform for Electric Vehicle Supply Equipment, which features a high-level Charging Station Control Unit and a Power Electronics Controller. The platform is a middleware that controls the charging process taking into account outside world information. Tests were performed in a safe driving track, in Italy, to verify the effectiveness and robustness of the installation, for one year, for a total of 120 drive hours, under various weather conditions. Tests showed the suitability of the platform in terms of ability to authenticate and authorize a vehicle even through a remote service, sequentially control each coil in a lane, monitor the charging process, assist the driver in keeping the vehicle aligned so as to maximize the energy exchange and deliver charging session information to the cloud (e.g. for billing). [ABSTRACT FROM AUTHOR]

The tomorrow's elastic adaptive mobility industrial research project developed eleven collaborative mobility apps addressing various traffic issues and scenarios. The apps, involving different aspects and degrees of collaboration (e.g., direct user participation, shared objectives, coordination, etc.), aim at increasing the driver/traveler awareness and support a better behavior. This paper describes the apps and the underlying system architecture shared by the participating car manufacturers. Then, it provides a user acceptance analysis grouping the apps according to the three main types of users and stakeholders: drivers, travelers, and administrators/operators. Data, collected in five European countries, shows that acceptance and expected impact are positive. The actual road-test experience did not diminish the high expectations raised by an initial presentation on paper, showing a good maturity of the prototypes. The Administrator app cluster shows a slightly better assessment, highlighting the importance of considering collaborative mobility as a system, including road, infrastructure, and traffic management. [ABSTRACT FROM AUTHOR]

Present public transport services still suffer from issues such as time deviations from the static timetable, overcrowded buses, increased on board time, and long wait at bus stops. This work studies the experimental implementation of an on-demand public bus transportation service in Trikala, a medium-sized Greek city. With a view to optimize the service from both the operator’s and the citizens’ point of view, this paper presents an insertion heuristic solving the static multivehicle dial-a-ride problem with time windows and a fixed fleet of vehicles. Since viability of such a service depends on its ability of involving a significant number of users and getting reliable information, we tested a gamification layer, aimed at motivating public transport users to participate and behave correctly with the system. We present and discuss a novel pervasive computing architecture and various types of serious games designed to achieve these goals. We finally report early usability test results and some simulation-based indications on the design of city-scale deployable serious games to enhance public transport-based mobility. [ABSTRACT FROM PUBLISHER]

Traffic safety and efficiency are major objectives for Intelligent Transportation Systems (ITS) [item 1) in the Appendix], [item 2) in the Appendix]. A lot of research has been done on autonomous vehicles and Advanced Driver Assistance Systems, which exploit a variety of sensors in order to support the driver and/or autonomous driving (e.g., [item 3) in the Appendix]–[item 7) in the Appendix]). [ABSTRACT FROM PUBLISHER]

Traffic flow prediction is a fundamental functionality of intelligent transportation systems. After presenting the state of the art, we focus on nearest neighbor regression methods, which are data-driven algorithms that are effective yet simple to implement. We try to strengthen their efficacy in two ways that are little explored in literature, i.e., by adopting a multivariate approach and by adding awareness of the time of the day. The combination of these two refinements, which represents a novelty, leads to the definition of a new class of methods that we call time-aware multivariate nearest neighbor regression (TaM-NNR) algorithms. To assess this class, we have used publicly available traffic data from a California highway. Computational results show the effectiveness of such algorithms in comparison with state-of-the-art parametric and non-parametric methods. In particular, they consistently perform better than their corresponding standard univariate versions. These facts highlight the importance of context elements in traffic prediction. The ideas presented here may be further investigated considering more context elements (e.g., weather conditions), more complex road topologies (e.g., urban networks), and different types of prediction methods. [ABSTRACT FROM PUBLISHER]