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Rionda A.,Adn mobile solution | Martinez D.,Adn mobile solution | Paneda X.G.,University of Oviedo | Arbesu D.,Adn mobile solution | And 2 more authors.
RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao | Year: 2012

In the last few years an important increase in private car journeys has created significant congestion in cities and on interurban roads. Furthermore, these shifts have generated significant parking problems. To be able to make predictions and define policies that can improve circulation, governments from time to time perform mobility studies that analyze the traffic situation and propose corrective measures. In this paper we present a technology that enables studies of mobility using the driver-citizen collaboration as the key element to obtain knowledge. The ease of use of technology, motivation and reward are elements that must be thoroughly considered if you want to keep in the driver's interest to cooperate. Cated Box technology has been tested successfully in the study of mobility in technological area of the city of Gijón known as "Milla del Conocimiento".

Rionda Rodriguez A.,Adn mobile solution | Martinez Alvarez D.,Adn mobile solution | Garcia Paneda X.,University of Oviedo | Alvarez A.,Adn mobile solution | And 4 more authors.
IEEE Latin America Transactions | Year: 2015

The ISO 50001 is an international standard oriented to the energy management. In the transport sector and in companies where the equipment is mainly combustion engine means, this energy management process is principally oriented to control the vehicle fuel consumption. In this paper, we present a service capable of monitoring how and when the fuel is consumed and offering tools to the fleet responsible to analyze and manage it. The service produces the required information and tools to support the ISO 50001 certification. To evaluate its performance, the service has been deployed in several vehicles of a garbage collector company making possible a reduction in the fuel consumption by approximately 20%. © 2015 IEEE.

Paneda A.G.,Adn mobile solution | Pozueco L.,University of Oviedo | Melendi D.,University of Oviedo | Paneda X.G.,University of Oviedo | And 5 more authors.
Revista Iberoamericana de Tecnologias del Aprendizaje | Year: 2016

Transport companies are probably one of the greatest sources of pollution nowadays. Perhaps because these companies would like to improve this situation, or perhaps because they simply would like to reduce the petrol they consume, they are more than ever deploying plans in order to increase the efficiency of their fleets. One of the easiest and cheapest ways to achieve this is to teach their drivers how to be more efficient. Nevertheless, traditional learning approaches were only successful in the short term, according to the previous work. In order to achieve long-term results, new learning paradigms must be taken into account. Furthermore, if we combine these paradigms with a learning analytics system, optimal results may be reached for both the company and the drivers. In this paper, we present a learning analytics system applied to the efficient driving context. This learning analytics system is used as a fundamental piece in the deployment of the blended learning methodology for efficient professional driving designed by our research group. We describe the design and the integration of this system with a real product currently being used in many transport fleets. With a technical approach, we also describe the main problems found during the deployment of this system and the solutions designed to cope with these problems. © 2016 IEEE.

Rionda A.,Adn mobile solution | Paneda X.G.,University of Oviedo | Garcia R.,University of Oviedo | Diaz G.,Spanish University for Distance Education (UNED) | And 4 more authors.
Computers and Education | Year: 2014

One of the most important expenses in bus and truck transport companies is the cost of fuel. A small increase in the price of petrol can have a very negative effect on the companies' balance sheet. Apart from that, road transport companies are targeted due to their influence on air pollution. All of this has made the reduction in fuel consumption the most important priority for this type of companies. One of the cheapest measures to reduce fuel burning is efficient driving. According to various studies, more efficient driving could reduce fuel consumption by more than 5%. This article presents a blended learning method which makes use of an on-board tutoring system, an e-learning platform and traditional courses to guide professional drivers to more efficient driving. Through visual and acoustic recommendations, the tutoring system helps drivers achieve more efficient driving in real-time. The on-board system is complemented with a Web portal where drivers can check their driving and receive recommendations for further improvement and a set of traditional seminars imparted by experts in the area. To evaluate the performance of the whole learning system, the driving of 34 professional drivers of the Urban Bus Company (EMTUSA) in the City of Gijón (Spain) has been monitored and analyzed over a period of 12 months. The results of the study showed an improvement in driving efficiency and a reduction in fuel consumption of almost 7% compared to the previous year. © 2014 Elsevier Ltd. All rights reserved.

Paneda X.G.,University of Oviedo | Garcia R.,University of Oviedo | Diaz G.,Spanish University for Distance Education (UNED) | Tuero A.G.,University of Oviedo | And 4 more authors.
Transportation Research Part A: Policy and Practice | Year: 2016

Public institutions and private companies all around the world agree that road transport is one of the main sectors responsible for global warming. With this in mind, all of them have designed actions to increase efficiency and reduce fuel consumption and emissions. A favorite for the companies is eco-driving because it can improve the fleet performance without a great investment. However, although these programs have achieved promising results in the majority of the experiences, the figures are not so encouraging in the long term. In many cases this decrease is produced by fuzzy reward programs or the total lack of them. Nevertheless, any coherent reward program, in order to be effective, must be associated with a complete and fair evaluation process which takes into account all the different aspects and complexities related with driving. In this paper, we propose a formal characterization of an efficient driving evaluation process which starts with a review of many different driving recommendation systems. These recommendations are used as seeds to build a set of formal competences that any eco driver must have, as well as the learning outcomes associated with each competence. A set of patterns of driving behaviors are defined, that allow confirming any of the learning outcomes. The definition also comprises a set of Key Performance Indicators (KPIs) for each learning outcome. These KPIs allow measuring the progress associated with each competence. Finally, we also propose some relevant differences that must be taken into account for the goals associated with each KPI, depending on the domain of application: type and road geometry, vehicle type (automatic or manual, passengers, cargo or not, public or private), amount of traffic, weather. Some examples of this driver characterization have been included to demonstrate the process. © 2016 Elsevier Ltd

Pozueco L.,University of Oviedo | Tuero A.G.,University of Oviedo | Paneda X.G.,University of Oviedo | Melendi D.,University of Oviedo | And 5 more authors.
IEEE CITS 2015 - 2015 International Conference on Computer, Information and Telecommunication Systems | Year: 2015

Concerns about global warming and energy costs have induced transport companies to take measures to reduce fuel consumption. Among the different options available, efficient driving is widely used, allowing a reduction in fuel consumption of around 10%. However, changing the driver's behavior is not exempt of problems. The success of efficient driving techniques in the long term is related to the motivation of the driver and, for that reason, an adaptive training system according to the driver's needs can prove much more successful than giving general instructions that do not solve their inefficiencies. Therefore, a properly description of the driver's behavior and the adaptation of the evaluation analysis to the context is a key factor for the learning process in efficient driving. In this paper we propose an adaptive learning system for efficient driving, which allows the evaluation of professional drivers of urban public transport in their work environment. With the proposed system, we can identify failure points relating to the context, making a focused evaluation. The first evaluation results show that the set of patterns designed to evaluate the application of efficient driving techniques can identify the incorrect actions of the drivers. Based on the results, it is possible to make personalized recommendations to improve driver performance. © 2015 IEEE.

Rionda A.,Adn mobile solution | Marin I.,Adn mobile solution | Martinez D.,Adn mobile solution | Aparicio F.,Adn mobile solution | And 4 more authors.
Conference and Exhibition - 2013 International Conference on New Concepts in Smart Cities: Fostering Public and Private Alliances, SmartMILE 2013 | Year: 2013

UrVAMM is a revolutionary system for environmental-urban monitoring in the next smart cities scenario. UrVAMM is constructing a new technology to be integrated in professional fleets of vehicles such as urban buses, garbage trucks, personal vehicles, etc. Based on the combination of two well-established technologies from Ingenieros Asesores and ADN Mobile Solutions. UrVAMM has two key objectives: a) to complement air quality monitoring in cities and b) reduction of emissions by efficient driving actions. UrVAMM is fully aligned with the new paradigm opened by the smartcity concept complying with secure, private, open and friendly data available for citizens and stakeholders. UrVAMM goes beyond the current state of art in air quality monitoring due to its innovative integration in service vehicles and its indicators in efficient driving. UrVAMM is being designed and tested under government funding and the collaboration of Gijón and Valencia bus fleets and councils. © 2013 IEEE.

Sanchez J.A.,University of Oviedo | Pozueco L.,University of Oviedo | Melendi D.,University of Oviedo | G Paneda X.,University of Oviedo | And 3 more authors.
IEEE Latin America Transactions | Year: 2015

Air quality is an important problem because it has a direct impact on human health and global warming. For years, governments have been monitoring air quality using fixed stations. However, these stations only examine pollution at specific locations. Therefore, pollution studies can only be created by extrapolation. This situation may be improved by increasing the number of stations or by deploying mobile monitoring networks. When it comes to representing data, a network with fixed stations may show an evolution of measurements in time. In the case of mobile networks, a problem appears when information needs to be represented since they involve physical movement. Due to the novelty of mobile monitoring networks, there is no general consensus on how data must be represented. In this paper, we present a study conducted to determine how environmental data gathered from mobile networks should be represented. We evaluate different methods for environmental data representation according to users interpretability. We also determine the preferred type of display to access this type of information. Finally, we obtain users preferences regarding the usefulness of different types of information according to their profile. A total of 110 users have participated in the study and the results show the importance of an adequate representation of data in order to achieve a correct interpretation of the information shown. © 2003-2012 IEEE.

Rodriguez A.R.,Adn mobile solution | Alvarez D.M.,Adn mobile solution | Paneda X.G.,University of Oviedo | Carbajal D.A.,Adn mobile solution | And 2 more authors.
Revista Iberoamericana de Tecnologias del Aprendizaje | Year: 2013

One of the sectors that currently generates pollution is road transport. Every day, millions of tons of CO2 are released into the atmosphere because of this type of human activity. Governments see the reduction of such emissions as a priority-which, according to various studies, could be achieved through more efficient driving. This paper presents a driver tutoring system based on active learning and ubiquity paradigms. Through visual and auditory recommendations, we are able to help drivers achieve more efficient driving. This system is complemented with a Web portal where drivers can check their driving and receive recommendations for further improvement. To evaluate the performance of the tutoring system, driving is monitored and analyzed over a period of six weeks with 150 volunteer drivers achieving results that improved efficient driving metrics and consumption by ∼10%. © 2013 IEEE.

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