Geospatial Modeling of Road Traffic Using a Semi-Supervised Regression Algorithm

Nowadays, big cities are facing many challenges with respect to traffic congestion, climate change, air and water pollution, among others. Thus, smart cities are intended to improve the life quality of the citizens, tackling such issues with the integration of information and communication technologies to reduce the impact and achieve a well-being state of citizens. In this work, a model to predict the traffic congestion applying a support vector machine method is proposed. In addition, a crowdsourcing approach based on mining the Twitter social networks collecting events associated with the traffic is also proposed. The main contribution of this research is focused on providing a methodology that characterizes the traffic congestion analyzing crowd-sensed data from a geospatial perspective. This approach was implemented over the Mexico City as a case study, in order to forecast possible future traffic events in the city, in which the citizens share their particular situation to discover alternatives routes for avoiding the traffic congestion. Future works are oriented towards designing mobile applications in order to introduce the proposed approach and integrate information from multiple platforms and navigation systems.