A Neural Network-based Approach for Public Transportation Prediction with Traffic Density Matrix

In today's modern cities, mobility is of crucial importance, and public transportation is particularly concerned. The main objective is to propose solutions to a given, practical problem, which specifically concerns the bus arrival time at various bus stop stations, by taking to account local traffic conditions. We show that a global prediction approach, under some global macro-parameters (e.g., total number of vehicles or 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 a traffic density matrix. Under this new paradigm, the experimental results obtained with both linear and neural networks (NN) approaches show promising prediction performances. Thus, the NN approach yields 24% more accurate prediction performances than a basic, linear regression.