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

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 forsuch 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 inthe 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 traceswere 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 HighPerformance Computing (HPC) center.