Traffic Pattern Analysis from Object Oriented Perspective

Abstract

The Knowledge representation is the extensively known and applied method for scrutinizing huge set of database in order to spring innovative information. Data extraction techniques are almost used in every field to retrieve the hidden information in order to reduce real world complexity. In this paper, the issues involved in traffic consequences are analyzed. The semantic net representation and inferences are used to highlight the density associated with each object. The characterized knowledge is taken to derive finest assertions using AND-OR-graphs. The assertions improve the intelligibility in accepting the prediction of traffic occurrence under different age group of people. The knowledge depiction techniques demonstrate the problem from objected oriented viewpoint which outlines the state and behavior of a thing directly related with the stated goal. Objectives are branded into sub goals based on the functionality allied within the object. The mandatory hypothesis needs to be framed and verified once the individual task is applied. In this paper, a case study of Traffic Pattern is gauged with the hazards associated with it.