Onto-traffic: A Semantic Traffic Analysis Tool Based on Gps Data for Smart-cities

Gabriel Alejandro Leon Paredes; Juan Jaime Sánchez Escobar; Liliana Ibeth Barbosa Santillán; Luis Francisco Barbosa Santillán; Fatemeh G. Nasri Shandiz

Onto-traffic: A Semantic Traffic Analysis Tool Based on Gps Data for Smart-cities

Título traducido de la contribución: Onto-traffic: una herramienta de análisis de tráfico semántico basada en datos GPS para ciudades inteligentes

Gabriel Alejandro Leon Paredes, Juan Jaime Sánchez Escobar, Liliana Ibeth Barbosa Santillán, Luis Francisco Barbosa Santillán, Fatemeh G. Nasri Shandiz

Grupo de Investigación en Cloud Computing Smart Cities & High Perfomance Computing (GIHP4C)

Producción científica: Contribución a una revista › Artículo

Resumen

Hoy en día, con el creciente uso de vehículos, el control y la supervisión del tráfico es una promesa para las ciudades inteligentes. Los sistemas de recomendación son una de las soluciones que ofrecen las ciudades inteligentes para estos problemas. Este tipo de sistemas son tendencia en las tecnologías de la información y están adquiriendo aceptación entre las comunidades científicas y las empresas. Estos sistemas proporcionan a los usuarios información valiosa sobre su entorno y les ayudan a tomar mejores decisiones relacionadas con los atascos. Este trabajo presenta OntoTraffic, un sistema de recomendación basado en ontologías, que ayuda al usuario a obtener información relacionada con el tráfico urbano en la ciudad de Pekín. La información se proporciona de acuerdo con las peticiones del usuario. Este sistema de recomendación funciona con trazas del Sistema de Posicionamiento Global (GPS) tomadas del conjunto de datos de Pekín, que cuenta con veintiún millones de trazas. Además, se recogieron 5888 trazas a través de los teléfonos inteligentes de los ciudadanos de Guadalajara. En el sistema OntoTraffic, los datos se recogen mediante determinados algoritmos que extraen los datos de una base de datos fiable. La información producida por dicho sistema, que depende de la otra recomendación, permite a los usuarios ser más productivos y eficientes en temas urbanos. Esta información también mejora la calidad de vida del usuario al gastar menos tiempo y dinero en sus actividades diarias. OntoTraffic también proporciona recomendaciones para obtener: (1) el tráfico medio en una calle específica, (2) encontrar las horas punta de cada calle, (3) descubrir las calles más concurridas en un momento determinado, (4) demostrar la distancia entre dos puntos en el mapa, y (5) mostrar las calles más concurridas entre las 7:00 pm y las 3:00 am. Por último, este documento sugiere algunas actividades para trabajos futuros, como: (a) construir nuevos modelos para apoyar a las ciudades inteligentes, (b) apoyar los desarrollos de sistemas recomendados para los tomadores de decisiones, (c) valorar el análisis de datos y las ontologías para el futuro centro de computación de alto rendimiento (HPC).

Título traducido de la contribución	Onto-traffic: una herramienta de análisis de tráfico semántico basada en datos GPS para ciudades inteligentes
Idioma original	Inglés estadounidense
Páginas (desde-hasta)	1-4
Número de páginas	4
Publicación	Industrial Law Journal
Volumen	2016
N.º	2016
Estado	Publicada - 22 may. 2016

Palabras clave

Gps data analysis
Ontotraffic system
Recommendation systems
Smart applications
Smart cites
Urban traffic

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title = "Onto-traffic: A Semantic Traffic Analysis Tool Based on Gps Data for Smart-cities",

abstract = "Nowadays by growing usage of vehicles, control and monitor the traffic is a promise for smart cities. Recommendation Systems are one of the solutions provided by smart cities for such issues. These kind of systems are trend in Information Technologies and they are acquiring acceptance among scientific communities and enterprises as well. Such systems feed users with valuable information about their environment and help them to take better decisions related to traffic jam. This paper presents OntoTraffic, a recommendation system based on ontologies, which supports the user to obtain information related to urban traffic in the city of Beijing. The information is provided according to the user{\textquoteright}s requests. This recommendation system works with Global Positioning System (GPS) traces taken from the Beijing data set, which has twenty one millions traces. In addition, 5888 traces were gathered through smartphones of Guadalajara citizens. In OntoTraffic system, data are collected by certain algorithms that extract the data from a reliable databases. Produced information from such system, which depends on the other recommendation, allows users being more productive and efficient in urban issues. This information also improves user{\textquoteright}s life quality by spending less time and money on their daily activities. OntoTraffic also provides recommendations to obtain: (1) average traffic on a specific street, (2) find rush hours for each street, (3) discover the most crowded streets in a certain time, (4) demonstrate the distance between two points on the map, and (5) show the bustle streets between 7:00 pm to 3:00 am. Finally this paper suggests some activities for future work such as: (a) build new models to support smart cities, (b) supporting recommended system developments for decision makers, (c) valuing data analysis and ontologies for future High Performance Computing (HPC) center.",

keywords = "Gps data analysis, Ontotraffic system, Recommendation systems, Smart applications, Smart cites, Urban traffic, Gps data analysis, Ontotraffic system, Recommendation systems, Smart applications, Smart cites, Urban traffic",

author = "{Leon Paredes}, {Gabriel Alejandro} and {S{\'a}nchez Escobar}, {Juan Jaime} and {Barbosa Santill{\'a}n}, {Liliana Ibeth} and {Barbosa Santill{\'a}n}, {Luis Francisco} and {Nasri Shandiz}, {Fatemeh G.}",

year = "2016",

month = may,

day = "22",

language = "English (US)",

volume = "2016",

pages = "1--4",

journal = "Industrial Law Journal",

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publisher = "Oxford University Press",

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T1 - Onto-traffic: A Semantic Traffic Analysis Tool Based on Gps Data for Smart-cities

AU - Leon Paredes, Gabriel Alejandro

AU - Sánchez Escobar, Juan Jaime

AU - Barbosa Santillán, Liliana Ibeth

AU - Barbosa Santillán, Luis Francisco

AU - Nasri Shandiz, Fatemeh G.

PY - 2016/5/22

Y1 - 2016/5/22

N2 - Nowadays by growing usage of vehicles, control and monitor the traffic is a promise for smart cities. Recommendation Systems are one of the solutions provided by smart cities for such issues. These kind of systems are trend in Information Technologies and they are acquiring acceptance among scientific communities and enterprises as well. Such systems feed users with valuable information about their environment and help them to take better decisions related to traffic jam. This paper presents OntoTraffic, a recommendation system based on ontologies, which supports the user to obtain information related to urban traffic in the city of Beijing. The information is provided according to the user’s requests. This recommendation system works with Global Positioning System (GPS) traces taken from the Beijing data set, which has twenty one millions traces. In addition, 5888 traces were gathered through smartphones of Guadalajara citizens. In OntoTraffic system, data are collected by certain algorithms that extract the data from a reliable databases. Produced information from such system, which depends on the other recommendation, allows users being more productive and efficient in urban issues. This information also improves user’s life quality by spending less time and money on their daily activities. OntoTraffic also provides recommendations to obtain: (1) average traffic on a specific street, (2) find rush hours for each street, (3) discover the most crowded streets in a certain time, (4) demonstrate the distance between two points on the map, and (5) show the bustle streets between 7:00 pm to 3:00 am. Finally this paper suggests some activities for future work such as: (a) build new models to support smart cities, (b) supporting recommended system developments for decision makers, (c) valuing data analysis and ontologies for future High Performance Computing (HPC) center.

AB - Nowadays by growing usage of vehicles, control and monitor the traffic is a promise for smart cities. Recommendation Systems are one of the solutions provided by smart cities for such issues. These kind of systems are trend in Information Technologies and they are acquiring acceptance among scientific communities and enterprises as well. Such systems feed users with valuable information about their environment and help them to take better decisions related to traffic jam. This paper presents OntoTraffic, a recommendation system based on ontologies, which supports the user to obtain information related to urban traffic in the city of Beijing. The information is provided according to the user’s requests. This recommendation system works with Global Positioning System (GPS) traces taken from the Beijing data set, which has twenty one millions traces. In addition, 5888 traces were gathered through smartphones of Guadalajara citizens. In OntoTraffic system, data are collected by certain algorithms that extract the data from a reliable databases. Produced information from such system, which depends on the other recommendation, allows users being more productive and efficient in urban issues. This information also improves user’s life quality by spending less time and money on their daily activities. OntoTraffic also provides recommendations to obtain: (1) average traffic on a specific street, (2) find rush hours for each street, (3) discover the most crowded streets in a certain time, (4) demonstrate the distance between two points on the map, and (5) show the bustle streets between 7:00 pm to 3:00 am. Finally this paper suggests some activities for future work such as: (a) build new models to support smart cities, (b) supporting recommended system developments for decision makers, (c) valuing data analysis and ontologies for future High Performance Computing (HPC) center.

KW - Gps data analysis

KW - Ontotraffic system

KW - Recommendation systems

KW - Smart applications

KW - Smart cites

KW - Urban traffic

KW - Gps data analysis

KW - Ontotraffic system

KW - Recommendation systems

KW - Smart applications

KW - Smart cites

KW - Urban traffic

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M3 - Article

SN - 0305-9332

VL - 2016

SP - 1

EP - 4

JO - Industrial Law Journal

JF - Industrial Law Journal

IS - 2016

ER -

Onto-traffic: A Semantic Traffic Analysis Tool Based on Gps Data for Smart-cities

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