I think this is a typical machine learning problem. You have to first identify whether it is supervised or unsupervised problem. Are the historical data you have labeled? Check also whether it is a clustering or classification problem. Many algorithms are available, like neural networks, logistic regression, etc.

Can you provide sample data? I mean, just the column labels which you have (first column, second, ......)

This problem has been solved using a fluid flow analogy of water through a pipe, i.e., water = traffic, pipe = highway. They use linear parameter varying models in the state space domain, see the work of Michel Verhaegen. Look at the book in the following link. http://searchworks.stanford.edu/view/9582214, I believe he has a chapter there.

All the forecasted model fails in India.The following are the factor which were not taken into account . Role of IT and intervention in today life . Role of space technology, courier service creation of shopping complex , increased life span of human being, change in life style due to electronic and mobile phone, change in agriculture produce and role of new robust statistical model 

Time series analysis may a possible approach.

Typical machine learning techniques can be used to solve your problem.

First, decide which kind of prediction you want to perform: binary prediction(low traffic load or high traffic load) or a range of traffic load states(low/medium/high and so on). While in the former it will be a classification problem, in the latter it will be a regression problem.

Second, as it has been stated, find out whether your problem is a supervised or  an unsupervised problem(data labeled or not).

Finally, have a look to the basics of Machine learning to check which kind of approaches better fits your problem. In theory, simple neural networks should be sufficient.

Regards.

@ Baraka Maiseli : I have following columns-

Device, speed, location params, direction, roadId, timestamp.

Thanks for your suggestion.

@ALL : Thanks all for your suggestions, This will help me in my research.

Some more questions related to the subject issue might be: 

What is the rate at which the traffic density has been changing ( both increase and decrease)?

How much averages are consistent in historical data?

What is the heaviest traffic load in certain months as compared to other months?

Another suggestion that once predictions begin to evolve and your machine starts to learn record both the predicted value and the actual value to know the difference between the predictions and actual values in order to show the variation range.

You can try simple MLP for this in which input vector size will be equal to inputs in MLP. In short you can have MLP architecture like I-m-n-O

where I = input size , m & n = no. of node in hidden layer (to be found out by trial and error) and O = output node(like high, medium, low).