Mr. Gonçalo Galvão Profile

Gonçalo Galvão

at Instituto Superior de Engenharia de Lisboa

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Proceedings Article | 6 June 2024 Poster

Unlocking traffic efficiency: visible light communication for urban intersection optimization

M. Vieira, G. Galvão, M. Véstias, P. Vieira, P. Louro

Proceedings Volume 12999, (2024) https://doi.org/10.1117/12.3016811

KEYWORDS: Visible light communication, Telecommunications, Control systems, Signal attenuation, Safety, Roads, Receivers, Optical transmission, Mathematical optimization, Light scattering

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This paper introduces Visible Light Communication (VLC) as an integrated approach to improving traffic signal efficiency and vehicle trajectory management at urban intersections. By combining VLC localization services with learning-based traffic signal control, a multi-intersection traffic control system is proposed. VLC utilizes light communication between connected vehicles and infrastructure, enabling joint transmission and data collection via mobile optical receivers. Atmospheric conditions affecting communication quality are considered, with an analysis of outdoor coverage maps. The system aims to reduce waiting times for pedestrians and vehicles while enhancing overall traffic safety. Flexible and adaptive, it accommodates diverse traffic movements during multiple signal phases. Cooperative mechanisms, transmission ranges, and queue/response interactions balance traffic flow between intersections, improving road network performance. Evaluated using the SUMO urban mobility simulator, the multi-intersection scenario demonstrates reduced waiting and travel times for both vehicles and pedestrians. A reinforcement learning scheme, based on VLC queuing/response behaviors, optimally schedules traffic signals. Agents at each intersection control traffic lights using VLC-ready vehicles' communication, calculating strategies to enhance flow and communicate with each other for overall optimization. The decentralized and scalable nature of the proposed approach, particularly for multi-intersection scenarios, is discussed, showcasing its potential applicability in real-world traffic scenarios.

Proceedings Article | 13 March 2024 Poster + Paper

Traffic signals and cooperative trajectories at urban intersections: leveraging visible light communication for implementation

G. Galvão, M. Vieira, P. Vieira, P. Louro, M. Vestias, P. Lourenço

Proceedings Volume 12906, 129060O (2024) https://doi.org/10.1117/12.3000529

KEYWORDS: Visible light communication, Roads, Safety, Telecommunications, Transmitters, Receivers, Education and training, Control systems, Multiplexers, Light emitting diodes

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This study addresses the challenges and research gaps in traffic monitoring and control, as well as traffic simulation, by proposing an integrated approach that utilizes Visible Light Communication (VLC) to optimize traffic signals and vehicle trajectory at urban intersections. The feasibility of implementing Vehicle-to-Vehicle (V2V) VLC in adaptive traffic control systems is examined through experimental results. Environmental conditions and their impact on real-world implementation are discussed. The system utilizes modulated light to transmit information between connected vehicles (CVs) and infrastructure, such as street lamps and traffic signals. Cooperative CVs exchange position and speed information via V2V communication within the control zone, enabling flexibility and adaptation to different traffic movements during signal phases. A Reinforcement Learning, coupled with the Simulation of Urban Mobility (SUMO) agent-based simulator, is employed to find the best policies to control traffic lights. The simulation scenario was adapted from a real-world environment in Lisbon, and it considers the presence of roads that impact the traffic flow at two connected intersections. A deep reinforcement learning algorithm dynamically control traffic flows by minimizing bottlenecks during rush hour through V2V and Vehicle-to-Infrastructure (V2I) communications. Queue/request/response interactions are facilitated using VLC mechanisms and relative pose concepts. The system is integrated into an edge-cloud architecture, enabling daily analysis of collected information in upper layers for a fast and adaptive response to local traffic conditions. Comparative analysis reveals the benefits of the proposed approach in terms of throughput, delay, and vehicle stops, uncovering optimal patterns for signals and trajectory optimization. Separate training and test sets allow monitoring and evaluating our model.

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