A RNN based Spatio Temporal Data Mining model for urban road Planning

A RNN BASED SPATIO TEMPORAL DATA MINING MODEL FOR URBAN ROAD As road planning is essential in urban areas, it is necessary to find the mechanisms for implementing the same. For which first the data need to be extracted. For extracting, the raster data is used from GPS. The raster data will take the image using GPS. Based on the elevation the surface was plotted. The different colors as mentioned in figure 1 will categorize the land surfaces into agriculture, bare ground, water, grass, etc. The surface signified by every cell contains the same width as well as height. The images are represented in pixels. It is represented in rows and columns. Then that data needs to be converted into cell (pixel) representation using a cartesian coordinate system and UTM projected coordinate system. Based on this the east and north coordinates of the particular region were identified with its position. Now using RNN and spatio temporal data mining, Directed Acyclic Graph a temporal road using the vector data was built. Then the shortest path is constructed and the replication was avoided using the Krusal's algorithm. This results in the way where the road planning was carried out smoothly and efficiently. UTM-Projected Coordinate System 6,000,000 -r North 300,000 East 300,700 coordinates are 300,500 E, 5,900,600 N Figure 5: UTM Projected Coordinate System Unfold tw w w w C I u f tU tU IU [out out out2 out XM x ~x x Ix M x BB B0 B 2 B3 x x x M x AO A, A2 A, MX x x Mx in, in, in2 in, Figure 6: Unfold RNN