Estimate and return either the traffic speed or the car entries in the city of Thessaloniki using historical traffic data. It's used in transport pilot of the 'BigDataEurope' project. There are functions for processing these data, training a neural network, select the most appropriate model and predict the traffic speed or the car entries for a selected time date.

Tools for simulating and modeling traffic flow on road networks
    using spatial conditional autoregressive (CAR) models. The package
    represents road systems as graphs derived from 'OpenStreetMap' data 
    <https://www.openstreetmap.org/> and
    supports network-based spatial dependence, basic preprocessing, and
    visualization for spatial traffic analysis.

Methods to unify the different ways of creating predictive models and their different predictive formats for classification and regression. It includes  methods such as K-Nearest Neighbors Schliep, K. P. (2004) <doi:10.5282/ubm/epub.1769>, Decision Trees Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone (2017) <doi:10.1201/9781315139470>,  ADA Boosting Esteban Alfaro, Matias Gamez, Noelia García (2013) <doi:10.18637/jss.v054.i02>, Extreme Gradient Boosting Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>,  Random Forest Breiman (2001) <doi:10.1023/A:1010933404324>, Neural Networks Venables, W. N., & Ripley, B. D. (2002) <ISBN:0-387-95457-0>, Support Vector Machines Bennett, K. P. & Campbell, C. (2000) <doi:10.1145/380995.380999>, Bayesian Methods Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (1995) <doi:10.1201/9780429258411>,  Linear Discriminant Analysis Venables, W. N., & Ripley, B. D. (2002) <ISBN:0-387-95457-0>, Quadratic Discriminant Analysis Venables, W. N., & Ripley, B. D. (2002) <ISBN:0-387-95457-0>,  Logistic Regression Dobson, A. J., & Barnett, A. G. (2018) <doi:10.1201/9781315182780> and Penalized Logistic Regression Friedman, J. H., Hastie, T., & Tibshirani, R. (2010) <doi:10.18637/jss.v033.i01>.

The goal of 'trainR' is to provide a simple interface to the 
    National Rail Enquiries (NRE) systems. There are few data feeds 
    available, the simplest of them is Darwin, which provides real-time 
    arrival and departure predictions, platform numbers, delay estimates, 
    schedule changes and cancellations. Other data feeds provide historical 
    data, Historic Service Performance (HSP), and much more. 'trainR' 
    simplifies the data retrieval, so that the users can focus on their 
    analyses. For more details visit 
    <https://www.nationalrail.co.uk/46391.aspx>.