Fábio de Souza Pereira Borges
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Fábio de Souza Pereira Borges has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Adelaida Pallavicini Fonseca
Department of Civil and Environmental Engineering, Faculty of Technology, Darcy Ribeiro Campi, University of Brasilia, Brasilia 70910-900, DF, Brazil
1 shared publication - Reinaldo Crispiniano Garcia
Industrial Engineering Department, Faculty of Technology, Darcy Ribeiro Campus, University of Brasilia, Brasilia 70910-900, DF, Brazil
1 shared publication
Latest Publications
- Open Access
- 26 September 2021
- Published by MDPI AG in Infrastructures
- Vol. 6 (10) , 138
Deep Reinforcement Learning Model to Mitigate Congestion in Real-Time Traffic Light Networks
Urban traffic congestion has a significant detrimental impact on the environment, public health and the economy, with at a high cost to society worldwide. Moreover, it is not possible to continually modify urban road infrastructure in order to mitigate increasing traffic demand. Therefore, it is important to develop traffic control models that can handle high-volume traffic data and synchronize traffic lights in an urban network in real time, without interfering with other initiatives. Within this context, this study proposes a model, based on deep reinforcement learning, for synchronizing the traffic signals of an urban traffic network composed of two intersections. The calibration of this model, including training of its neural network, was performed using real traffic data collected at the approach to each intersection. The results achieved through simulations were very promising, yielding significant improvements in indicators measured in relation to the pre-existing conditions in the network. The model was able to deal with a broad spectrum of traffic flows and, in peak demand periods, reduced delays and queue lengths by more than 28% and 42%, respectively.
- 26 September 2021
- Published by MDPI AG in Infrastructures
- Vol. 6 (10) , 138
Open AccessDeep Reinforcement Learning Model to Mitigate Congestion in Real-Time Traffic Light Networks
Urban traffic congestion has a significant detrimental impact on the environment, public health and the economy, with at a high cost to society worldwide. Moreover, it is not possible to continually modify urban road infrastructure in order to mitigate increasing traffic demand. Therefore, it is important to develop traffic control models that can handle high-volume traffic data and synchronize traffic lights in an urban network in real time, without interfering with other initiatives. Within this context, this study proposes a model, based on deep reinforcement learning, for synchronizing the traffic signals of an urban traffic network composed of two intersections. The calibration of this model, including training of its neural network, was performed using real traffic data collected at the approach to each intersection. The results achieved through simulations were very promising, yielding significant improvements in indicators measured in relation to the pre-existing conditions in the network. The model was able to deal with a broad spectrum of traffic flows and, in peak demand periods, reduced delays and queue lengths by more than 28% and 42%, respectively.