5 research outputs found

    OntoTouTra: tourist traceability ontology based on big data analytics

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    Tourist traceability is the analysis of the set of actions, procedures, and technical measures that allows us to identify and record the space–time causality of the tourist’s touring, from the beginning to the end of the chain of the tourist product. Besides, the traceability of tourists has implications for infrastructure, transport, products, marketing, the commercial viability of the industry, and the management of the destination’s social, environmental, and cultural impact. To this end, a tourist traceability system requires a knowledge base for processing elements, such as functions, objects, events, and logical connectors among them. A knowledge base provides us with information on the preparation, planning, and implementation or operation stages. In this regard, unifying tourism terminology in a traceability system is a challenge because we need a central repository that promotes standards for tourists and suppliers in forming a formal body of knowledge representation. Some studies are related to the construction of ontologies in tourism, but none focus on tourist traceability systems. For the above, we propose OntoTouTra, an ontology that uses formal specifications to represent knowledge of tourist traceability systems. This paper outlines the development of the OntoTouTra ontology and how we gathered and processed data from ubiquitous computing using Big Data analysis techniquesThis research was financially supported by the Ministry of Science, Technology, and Innovation of Colombia (733-2015) and by the Universidad Santo Tomás Seccional Tunja

    Adaptive Regenerative Braking in Electric Vehicles

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    Elektrofahrzeuge fahren lokal emissionsfrei und tragen damit dazu bei, die Emissionen in Städten zu reduzieren. Zusätzlich, zeichnen sich Elektrofahrzeuge durch ein dynamisches Fahrverhalten aus. Nachteilig wirkt sich bei den meisten Elektrofahrzeugen, die geringe Reichweite auf die Akzeptanz bei Neuwagenkäufern aus. Eine der Maßnahmen zur Erhöhung der Reichweite von Elektrofahrzeuge ist das regenerative Bremsen. Hierbei wird die kinetische Energie des Fahrzeugs durch generatorisches Bremsen als elektrische Energie zurückgewonnen. Diese zurückgewonnene Energie erhöht die Reichweite des Autos. In dieser Dissertation, wird ein adaptives regeneratives Bremssystem vorgestellt. Dieses System wählt abhängig vom Fahrertyp und der aktuellen Verkehrssituation ein geeignetes regeneratives Bremsniveau aus. Um ein solches System zu realisieren, wurden Verfahren entwickelt, welche einerseits den Fahrertyp und andererseits die Fahrerintention durch Analyse des Fahrbetriebs ermitteln. Dazu wurde u.a. ein mehrdimensionales verstecktes Markov-Modell (MDHMM) entwickelt. Bei Verwendung des Fahrertyps und der Intention des Fahrers, kann so eine geeignete Bremsstufe ausgewählt werden, die die physikalische Begrenzung der Fahrzeugkomponenten berücksichtigt. Durch den Einsatz des entwickelten Systems, kann gezeigt werden, dass eine Erhöhung der Reichweite erreicht werden kann, ohne den Komfort des Fahrers zu beeinträchtigen

    Development of an Optimisation Model for Scheduling of Street Works Schemes

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    The coordination of street works activities in urban networks has been highlighted by the Government as one of the most important aspects of street works practice, benefiting street authorities, undertakers and road users alike (Department for Transport, 2012c). The present research aims to develop an optimisation model for minimising the overall costs and disruptions incurred by all stakeholders as a result of implementing a number of street works schemes in an urban traffic network. The output of the optimisation model consists of optimum values for the underlying decision variables of the model such as start time of each street works scheme, type of traffic management strategy for each link, sequence of link closures and the level of resources allocated to undertake each scheme. The following two distinct objective functions, which are subject to minimisation by the optimisation model, have been developed: A primary objective function which captures the monetised effects of street works schemes such as cost of delays to road users, and cost of undertaking street works schemes. A secondary objective function (developed as a fuzzy inference system) to capture the non-monetised disruptive effects of street works schemes. The fuzzy variables of this inference system correspond to the level of ‘accessibility degradation’ of the network links, ‘connectivity degradation’ of the origin-destinations of the network, and ‘time sensitivity’ of the disruptive events (i.e. street works schemes). Next the street works optimisation problem was mathematically formulated as a bi-level optimisation programming problem, where the higher level problem is associated with minimising the aforementioned objective functions, and the lower level problem deals with predicting traffic flows, and thus the amount of delays incurred by the road users. Subsequently this study developed a genetic algorithm solution method to solve the resulting non-convex and NP-hard optimisation problem with integer or mixed type variables. Finally the performance of the optimisation algorithm was verified by a number of experimental tests on a small hypothetical network for three street works schemes

    Pertanika Journal of Science & Technology

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