Simulation and Prediction of Public Transportation with Maps of Local Density Blobs

Public transportation in urban areas is a subject that receives today considerable attention from both academic and industrial research. The main objective of the proposed approaches is to improve the public transportation system (and more specifically buses) by making it more accurate, reliable and convenient. We show that a global prediction approach, under some global macro-parameters (total number of vehicles, pedestrians...) is not feasible. This observation leads us to the introduction of a finer granularity approach, where the traffic conditions are represented in terms of maps of local density blobs. Under this new paradigm, the experimental results obtained with both linear and SVR regressors show promising prediction performances.