Traffic dynamics estimation by using raw floating car data

Massive datasets of Floating Car Data (FCD) are collected and thereafter processed to estimate and predict traffic conditions. In the framework of short-term traffic forecasting, machine learning techniques have become very popular. However, the big datasets available today contain for the most part easily predictable data, that are data observed during recurrent conditions. Integration of different machine learning techniques with traffic engineering notions must contribute to obtain new transportation-oriented data-driven methods. In this paper we address traffic dynamics estimation by using individual FCD in order to develop an integrative framework able to recognize and select the suitable method for traffic forecasting. Taking into account the spatial distributions of individual FCD positions we retrieve a new spatial-based criterion for the integration of models.