Abstract
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.
Highlights
Big cities are the most affected by vehicular traffic; every day thousands of people move from their homes to their works, schools, and activities, centers; overcrowding the main roads of the cities, increasing the air pollution and the problems related to traffic jams, such as car accidents [11]
The smart cities paradigm tries to introduce technology into the people activities with a view to improving their life; its main characteristic is that working together with the urban computing, creates the adequate framework for technologies related to the Internet of Things (IoT)
In the investigation of Conte and Contini [8], how road traffic contributes to particle concentrations in the air of Lecce (Italy) is analyzed, stuffing sizeresolver emission factors (EF), using the eddy-covariance measurements of vertical turbulent fluxes, through statistical results the authors show how particles from cars pollute the air in urban areas
Summary
Big cities are the most affected by vehicular traffic; every day thousands of people move from their homes to their works, schools, and activities, centers; overcrowding the main roads of the cities, increasing the air pollution and the problems related to traffic jams, such as car accidents [11].The smart cities paradigm tries to introduce technology into the people activities with a view to improving their life; its main characteristic is that working together with the urban computing, creates the adequate framework for technologies related to the Internet of Things (IoT). Big cities are the most affected by vehicular traffic; every day thousands of people move from their homes to their works, schools, and activities, centers; overcrowding the main roads of the cities, increasing the air pollution and the problems related to traffic jams, such as car accidents [11]. In the investigation of Conte and Contini [8], how road traffic contributes to particle concentrations in the air of Lecce (Italy) is analyzed, stuffing sizeresolver emission factors (EF), using the eddy-covariance measurements of vertical turbulent fluxes, through statistical results the authors show how particles from cars pollute the air in urban areas. Other research works as [30] and [3] argued that the quantification of road traffic and vehicles concentration, and the posterior spatio-temporal modeling of the data is difficult, the analysis of EF becomes a complex process
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
