Improved Traffic Prediction by Applying KNN and Euclidean Distance ARIMA (Ke-Arima) Approach

In the modern era, the road infrastructure failed to cope up with the exponential increase of road traffic. There is a thrust to find a smarter way to deal with such transportation system. Intelligent Transport System is at the forefront edge of this, one of the points is exact and hassle-free forecasts that guarantee smooth and bother free driving and authoritative experience. In such manner, Intelligent Transport System (ITS) being looked into for quite a few years and furthermore a field of consistent growth of works and advancement after some time, there is a wealth of writing on traffic expectation. Traffic datasets generated through the application of IOT are operated upon by the existing techniques. Traffic flow analysis is conducted to tackle the issues of traffic forecasting. This paper presents a systematic analysis of previous aggregate work on traffic prediction, highlight the marked changes and presents future directions for research work.