• K. SWETHAN KUMAR
• M. SUBHAKAR
• Dr. K. SARITHA

Safety for roadway traffic is a crucial role for vehicle organizations and transport agencies and also for each and every common man. Study and investigation is imperative to provide safe driving recommendations on the road traffic. In this paper we study and explore the statistical analysis on road network traffic. We would like to propose and explore the correlations among traffic conditions of different roads and propose a method that improves the complete traffic map from sparse traffic samplings. For the most part, it is complex to reveal the complicated road traffic affinities with direct lookup over the traffic information. Once extensive researching the correlations of road traffic is over, then we move to mathematical modeling and represent them with Multiple Linear Regression (MLR) model. The outcomes illustrates that the technique proficiently demonstrates the resultant road traffic correlations.

Statistical Analysis, Dense Traffic Network, Multiple Linear Regression, Data Mining, Classification, Global Positioning System.

"Mining Techniques for Analyzing the Network Correlation for Dense Road Traffic", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.73-76, June 2019, Available :http://www.jetir.org/papers/JETIR1907314.pdf

"Mining Techniques for Analyzing the Network Correlation for Dense Road Traffic", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp73-76, June 2019, Available at : http://www.jetir.org/papers/JETIR1907314.pdf