Prediction algorithm for traffic I am working on making predictions about traffic trends in a big city. Traffic is very seasonnal, and depends on the time of the day (with peaks at 9am and 6pm), day of the week (Monday to Friday are alike compared to Sat and Sun), holidays (Xmas, school holidays etc). And I have 2 years (at hourly rate) of data.

I am using a RandomForestRegressor to make a prediction on the year to come. Is that a correct algorithm for this class of problem?
I think the best way to deal with this problem is to use blending (although it is not a bad idea to start with random forest just to get sense about the problem you have). If you are not familiar with the word blending you can check this page:
http://mlwave.com/kaggle-ensembling-guide/

But let me explain what is the idea:
I believe that there is a kind of relation between the features you have: you can group all the features related to Weather together, The day, time ..etc together. Each set of features can be used to train a random forest. The output of these random forests can be combined by either linear or non linear regression blending techniques.

If the database you have is very large it is better to read more about blending techniques and do some experiments before going to your problem to make yourself familiar with it