Learning resources for Data Science for applications in road traffic data?
The best way to learn data science is through problem solving. I suggest you to head over to Kaggle and work through the for-knowledge problems. 

To get a good start on Machine Learning problems, acclimate yourself with the tree package in R. This will help you understand how decision trees work, and building upon that, how random forests, gradient boosting machines and other sophisticated tree based algorithms work. 

Then, there are SVMs and deep learning models. 

To get an understanding of unsupervised learning problems, learn k-means and employ it for clustering. 

Other general concepts/ ideas to understand are: 


cross-validation 
overfitting, regularization 
bias-variance trade off
dimensionality reduction/ variable selection
generalization error
ensemble learning 


For books, the most common recommendation to anyone who is familiar with statistics and wants to get into Machine Learning is "The Elements of Statistical Learning" by Hastie, Tibshirani, and Friedman.