Satellite monitoring system to determine critical regions in a terrestrial transport route by applying supervised learning techniques

Karim Guevara Puente De La Vega, Miguel Angel Mamani Zeballos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

At present, the use of satellite monitoring systems involves much more than the storage of historical data. Because with proper processing, these large volumes of data can provide new, relevant, useful information of great importance to transport companies. Allowing companies to have information on the incidents in the vehicular displacement, to assess the level of security of the transport services provided. For this, it is necessary that all existing incidents or risks are counted and/or detected. In the present article a satellite monitoring system is presented for the determination of critical regions in terrestrial transport routes, the same one that was conceived from the use of supervised learning techniques. The analysis carried out starts from the structure of the standardized historical reports that correspond to the monitoring of vehicular units. These reports were generated by a current system of consultation and monitoring in real time. Therefore, through the use of these techniques it was possible to achieve the discovery of knowledge stored in databases.

Original languageEnglish
Title of host publication15th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology
Subtitle of host publicationGlobal Partnership for Development and Engineering Education, LACCEI 2017
EditorsMaria M. Larrondo Petrie, Humberto Alvarez
PublisherLatin American and Caribbean Consortium of Engineering Institutions
Pages1-10
Number of pages10
ISBN (Electronic)9780999344309
DOIs
StatePublished - 2017
Event15th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology, LACCEI 2017 - Boca Raton, United States
Duration: 19 Jul 201721 Jul 2017

Publication series

NameProceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
Volume2017-July
ISSN (Electronic)2414-6390

Conference

Conference15th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology, LACCEI 2017
Country/TerritoryUnited States
CityBoca Raton
Period19/07/1721/07/17

Bibliographical note

Funding Information:
Para que el algoritmo NN realice la clasificación más acertada de correspondencia, depende directamente del tamaño del universo del conjunto de entrenamiento o geopuntos registrados y asociados a una o varias rutas; sería ideal utilizar un conjunto de entrenamiento de similar tamaño para generar las reglas de decisión a partir de datos relacionados a los accidentes de tránsito registrados en el Perú, pero actualmente esa es una de las debilidades a afrontar según el Plan Nacional de Seguridad Vial 2015-2024 desarrollado por el Consejo Nacional de Seguridad Vial [13], el mismo que detalla que existe una deficiente recopilación de datos de accidentes de tránsito, así como un déficit en la calidad de datos recopilados de accidentes de tránsito. Ante esta situación; el sistema propuesto, utiliza un conjunto de entrenamiento relativamente pequeño para obtener las reglas de decisión; a pesar de ello, ese conjunto considera todas las combinaciones lógicas posibles en las cuales pueda existir riesgo.

Publisher Copyright:
© 2017 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.

Keywords

  • Algorithm NN
  • Data mining
  • Discovery of knowledge in databases
  • Rules of Decision
  • Vehicle monitoring

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