Traffic Prediction for Intelligent Transportation System using Machine Learning

Abstract :  This paper aims to develop a tool for predicting accurate and timely traffic flow Information. Traffic Environment involves everything that can affect the traffic flowing on the road, whether it’s traffic signals, accidents, rallies, even repairing of roads that can cause a jam. If we have prior information which is very near approximate about all the above and many more daily life situations which can affect traffic then, a driver or rider can make an informed decision. Also, it helps in the future of autonomous vehicles. In the current decades, traffic data have been generating exponentially, and we have moved towards the big data concepts for transportation. Available prediction methods for traffic flow use some traffic prediction models and are still unsatisfactory to handle real-world applications. This fact inspired us to work on the traffic flow forecast problem build on the traffic data and models.It is cumbersome to forecast the traffic flow accurately because the data available for the transportation system is insanely huge. In this work, we planned to use machine learning, genetic, soft computing, and deep learning algorithms to analyse the big-data for the transportation system with much-reduced complexity. Also, Image Processing algorithms are involved in traffic sign recognition, which eventually helps for the right training of autonomous vehicles.
 EXISTING SYSTEM :
 ? Existing traffic prediction methods are mainly based on short-to-medium-term prediction, and there are very few studies on long-term forecasting. ? Most existing solutions are data intensive. However, abnormal conditions (extreme weather, temporary traffic control, etc) are usually non-recurrent, it is difficult to obtain data, which makes the training sample size smaller and learning more difficult than that under normal traffic conditions. ? Existing methods usually treat data processing and model prediction as two separate tasks. It is of great practical significance to design a robust and effective traffic prediction model in the case of various noises and errors in the data.
 DISADVANTAGE :
 ? It requires extra lands and also the extensive infrastructure to maintain it, and due to this, the cost of expenditure also high. Sometimes many problems came into the network like in the urban area. This land facility is not available for the expansion of the roads and lanes. ? It can be used to solve both regression and classification problem. DT identify its results by performing a set of tests on the training dataset. ? Recently, deep learning concepts attract many persons involving academicians and industrialist due to their ability to deal with classification problems, understanding of natural language, dimensionality reduction, detection of objects, motion modelling.
 PROPOSED SYSTEM :
 • In, the researchers proposed an end-to-end neural framework as an industrial solution for the travel time prediction function in mobile map applications, aiming at exploration of spatiotemporal relation and contextual information in traffic prediction. • As the field grows, more and more models have been proposed, and these models are often presented in a similar way. • Although recent approaches have been proposed, these researches have not been thoroughly studied, such as how to design a high-quality mathematical model to match two regions, or how to integrate other available auxiliary data sources, etc., are still worth considering and investigating.
 ADVANTAGE :
 ? It is easy to coach the deep network by applying the BP methodology with the gradient-based improvement technique. Unfortunately, it’s notable that deep networks trained during this method have dangerous performance. ? It helps the riders and drivers to make better travel judgement to alleviate traffic congestion, improve traffic operation efficiency, and reduce carbon emissions. ? The main advantage of ITS is to provide a smooth and safe movement of road transportation. ? It’s also helpful in the perspective of environmentfriendliness to reduce carbon emission. ? It provides many opportunities for automotive or automobile industries to enhance the safety and security of their travellers.

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