Semantic Approach to Spatial Information Retrieval - Case Study: Vancouver Downtown Traffic Nodes During Rainfall

In modern cities with high traffic congestion, rainfall has always been one of the main causes of disruption of the urban traffic system. On the other hand, data processing and analysis, especially for spatial data, today is more limited to separate and independent data sets that need to make correlations among related entities to solve complex problems and perform various processes. Previous studies such as data mining on linked data, search and retrieval spatial data and traffic density monitoring during rainfall have been performed، but due to the many hours without rainfall, excessive data is stored that caused data redundancy and the accumulation of worthless data, but the use of spatial and non-spatial data available in different sources with values and information Differentiation and conversion of data into triple graph model in the use of semantic web is one of the activities that has been less considered in previous researches. In this study, while investigating the effect of rainfall on traffic flow using linked data to Let's take a look at the advantages of choosing this technology. We use Vancouver's rainfall and traffic data for two weeks . with using semantic web and related technologies by converting web-extracted data into RDF data model And link them to a database We retrieved data using GEOSPARQL technology. In this study, using the RDF triple data model and storing data in a base graph database and using semantic-spatial concepts, we observed that this method has the ability to provide solutions for using different sources of information. In addition, we were able to prevent the entry of valueless data, which prevented the creation of dependencies in the model and the entry of worthless data, which maintains the consistency of the model and prevents any complexity from entering the model.

Semantic Approach to Spatial Information Retrieval - Case Study: Vancouver Downtown Traffic Nodes During Rainfall Keywords:

Linked-data , sematic-web , GeoSPARQL , Traffic , Query language , geospatial data , RDF