Modelagem e Simulação Comparativa entre Semáforo Inteligente e Tradicional para Detecção de Veículos de Urgência em Cruzamento Urbano
Resumo
Veículos de urgência enfrentam dificuldades na prestação de socorro em vias urbanas. Esta complexidade, exige soluções que possam melhorar e otimizar o tráfego para travessias de cruzamentos durante o resgate. Os recursos dos Sistemas de Transporte Inteligente integrados com tecnologias da Internet das Coisas, embarcados aos semáforos, estabelecem um Semáforo Inteligente (Smart Traffic Light STL). Soluções STL contribuem em monitorar e gerenciar cruzamentos, liberando a passagem de veículos de urgência. Dez minutos de atraso no atendimento pré-hospitalar aumentam a chance de óbito em 9%. No Brasil em 44.822 ocorrências do SAMU-192, apenas 11% dos atendimentos ocorreram em menos de cinco minutos, enquanto 62% ultrapassaram 10 minutos. Diante desse problema, este trabalho realizou uma modelagem analítica e análise comparativa por simulação (SUMO) entre semáforo tradicional e semáforo inteligente para priorização da travessia de veículos de urgência em cruzamento urbano realístico. Também foi implementada uma maquete experimental, para reproduzir fisicamente o cenário investigado. Os resultados demonstraram ganhos consistentes para os veículos de urgência, reduzindo o tempo total no atendimento para 5,7 seg e com mínimos impactos (trade-offs) para os veículos comuns do cruzamento.Referências
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Shilpa, B., Mayank, M., and Fakruddin, S. M. (2025). Adaptive Urban Traffic Control Using Camera Vision and Emergency Vehicle Mobile Interface. In 2025 Second International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications (COSMIC), pages 48–53. IEEE.
Tomar, I., Sreedevi, I., and Pandey, N. (2022). State-of-art review of traffic light synchronization for intelligent vehicles: Current status, challenges, and emerging trends. Electronics, 11(3).
Valentini, E. P., Filho, G. P. R., De Grande, R. E., Ranieri, C. M., Júnior, L. A. P., and Meneguette, R. I. (2023). A novel mechanism for misbehavior detection in vehicular networks. IEEE Access, 11:68113–68126.
Vandana, C., Sringeri, A. B., and Reddy, A. P. (2025). LifeLane: A Dual-Layer Emergency Vehicle Traffic Clearance System Using Embedded Communication and Machine Learning. In 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG), pages 1–6. IEEE.
Divyesh, R., Vibusha, P., and Chitra, P. (2024). Dynamic Traffic Flow Optimization with YOLO-Based Object Detection. In 2024 International Conference on Smart Technologies for Sustainable Development Goals (ICSTSDG), pages 1–6. IEEE.
Elassy, M., Al-Hattab, M., Takruri, M., and Badawi, S. (2024). Intelligent transportation systems for sustainable smart cities. Transportation Engineering, 16:100252.
Forastieri Filho, H. L. A., Ferraz de Araujo, C. M., Mendonça Junior, A. d. S., and Forastieri, H. L. C. (2022). Tempo resposta no SAMU–192 e suas implicações. Cadernos UniFOA, 17(49):173–183.
Gauss, T., Ageron, F.-X., Devaud, M.-L., Debaty, G., Travers, S., Garrigue, D., Raux, M., Harrois, A., Bouzat, P., and French Trauma Research Initiative (2019). Association of prehospital time to in-hospital trauma mortality in a physician-staffed emergency medicine system. JAMA Surgery, 154(12):1117–1124. PMC6764001.
Hao, Z., Wang, Y., and Yang, X. (2024). Every second counts: A comprehensive review of route optimization and priority control for urban emergency vehicles. Sustainability, 16(7):2917.
Khang, A. and Singh, K. (2025). Internet of Things (IoT) Smart Sensing Traffic Lights for Revolutionizing Urban Traffic Management, pages 105–118. Springer Nature Switzerland, Cham.
Kleinrock, L. (1975). Queueing Systems, Volume 1: Theory. Wiley, New York, NY, USA.
Köksalan, M. M. (1991). A replication approach to interval estimation in simulation. In Proceedings of the 23rd conference on Winter Simulation. ACM. Available in the ACM Digital Library.
Law, A. M. (2015). Statistical analysis of simulation output data. In Proceedings of the 2015 Winter Simulation Conference. ACM / Winter Simulation Conference. Tutorial paper indexed in the ACM Digital Library.
Little, J. D. (1961). A proof for the queuing formula: L= λ w. Operations research, 9(3):383–387.
Lopez, P. A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., and Wiessner, E. (2018). Microscopic traffic simulation using sumo. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pages 2575–2582.
Maschi, L. F. C., Pinto, A. S. R., Meneguette, R. I., and Baldassin, A. (2018). Data summarization in the node by parameters (dsnp): Local data fusion in an iot environment. Sensors, 18(3).
Meneguette, R., Grande, R. D., Ueyama, J., Filho, G. P. R., and Madeira, E. (2023). Vehicular Edge Computing: Architecture, Resource Management, Security, and Challenges. ACM Computing Surveys, 55(1):1–46.
Oblakova, A., Al Hanbali, A., Boucherie, R., van Ommeren, J., and Zijm, W. (2022). An analytical model for a tandem of two traffic-light intersections under semi-actuated and fixed control. Transportation Research Interdisciplinary Perspectives, 16:100715.
OpenStreetMap (2026). OpenStreetMap. [link]. Dados cartográficos abertos sob a Open Database License (ODbL). Acesso em: 19 mar. 2026.
Prabha, R., G., A. M., Safie, N., Hasan, M. K., Abdullah, N. H. S., and K.G., M. (2025). Modeling of Novel Ensemble Learning Method for Emergency Intelligent Transportation System Tracking and Mobility Management. In 2025 International Conference on Electrical Engineering and Informatics (ICEEI), pages 1–6. IEEE.
SenthilPrabha, R., Sasikumar, D., Sriram, G., Nelson, K., and Harish, P. (2023). Smart Traffic Management System Through Optimized Network Architecture for the Smart City Paradigm Shift. In International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS).
Shilpa, B., Mayank, M., and Fakruddin, S. M. (2025). Adaptive Urban Traffic Control Using Camera Vision and Emergency Vehicle Mobile Interface. In 2025 Second International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications (COSMIC), pages 48–53. IEEE.
Tomar, I., Sreedevi, I., and Pandey, N. (2022). State-of-art review of traffic light synchronization for intelligent vehicles: Current status, challenges, and emerging trends. Electronics, 11(3).
Valentini, E. P., Filho, G. P. R., De Grande, R. E., Ranieri, C. M., Júnior, L. A. P., and Meneguette, R. I. (2023). A novel mechanism for misbehavior detection in vehicular networks. IEEE Access, 11:68113–68126.
Vandana, C., Sringeri, A. B., and Reddy, A. P. (2025). LifeLane: A Dual-Layer Emergency Vehicle Traffic Clearance System Using Embedded Communication and Machine Learning. In 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG), pages 1–6. IEEE.
Publicado
25/05/2026
Como Citar
MASCHI, Luis Fernando Castilho; VALENTINI, Edivaldo Pastori; AVALLONE, Elson; VIANA, Fábio Luiz; BRUSCHI, Sarita Mazzini; MENEGUETTE, Rodolfo Ipolito.
Modelagem e Simulação Comparativa entre Semáforo Inteligente e Tradicional para Detecção de Veículos de Urgência em Cruzamento Urbano. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 10. , 2026, Praia do Forte/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 155-168.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2026.23163.
