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Título:
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Evaluation of transport events with the use of big data, artificial intelligence and augmented reality techniques
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Autor/a:
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Pérez Diez, Fernando; Cabrerizo Sinca, Julià; Roche Vallès, David; Campos Cacheda, José Magín
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Notas:
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The phenomenon of "smart cities" generalizes the use of Information and Communication Technologies. The generation and use of data to manage mobility is a challenge that many cities are betting on and investing in. Through the Internet of all things (IoT) and the use of sensors and mechanisms for capturing information, the number of data analysis tools such as Big Data, Artificial Intelligence (AI), and Augmented Reality (AR) has increased. With the constant use of assisted process learning (Machine Learning), it’s possible to improve event interpretation through the customization of learning protocols. Repetitively trained software can identify relevant events and report changes in critical scenarios that can trigger a series of protocols. The use of artificial intelligence techniques makes it possible to automate monotonous processes and improve transport management. This article analyzes different technologies used to generate transport information and data validation. It is intended to experiment with the use of technologies in the detection of relevant facts, changes of state, and identification of events. It also measures the reliability level when detecting events, and studies the implementation of possible solutions into the transport management system, in order to assist in decision making processes. |
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Materia(s):
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-Ciutats intel·ligents
-Dades massives
-Intel·ligència artificial
-Realitat augmentada
-Artificial Intelligence
-Ciudades inteligentes
-Grandes datos
-Inteligencia artificial
-Realidad aumentada
-Smart cities
-Big data
-Augmented reality
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Derechos:
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© 2021 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
http://creativecommons.org/licenses/by-nc-nd/4.0/
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Tipo de documento:
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Artículo
Artículo - Versión publicada |
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Editor:
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Elsevier
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