• Dr. M.Manimekalai
• A. Kiruthika

These days transportation systems rely increasingly on the convenience and accuracy of traffic detector data to monitor traffic operational conditions and assess system performance. Quality traffic data is an essential for traffic related researches and developing transport applications. There are several techniques to detect the missing data ranging from simply deleting the data to using complex algorithms to impute missing data, traditional methods such as deletion and single imputation and advanced methods such as multiple imputation, model-based procedures, and machine learning techniques. . In this paper, a unique method for missing traffic data imputation is proposed using KNA optimized by a combination of KNN algorithm and SVM. In this method, KNA is the basic algorithm and the parameters of KNA are optimized. Firstly, the patterns of missing traffic data are analysed and the demonstration of missing traffic data is given using matrix-based data structure. Next, traffic data from municipal areas are used to assess spatial-temporal association of the traffic data to resolve.

KNA, SVM, KNN, Data mining.

"Multi Output Gaussian Processes for Data Imputation using KNA ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.14-16, June 2019, Available :http://www.jetir.org/papers/JETIRDB06003.pdf

"Multi Output Gaussian Processes for Data Imputation using KNA ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp14-16, June 2019, Available at : http://www.jetir.org/papers/JETIRDB06003.pdf