Um Estudo Comparativo de Modelos de Fluxo de Tráfego de Veículos para Sistemas de Transporte Inteligentes
Resumo
Com o crescente processo de urbanização, a mobilidade se torna um tópico sensível. O gerenciamento do tráfego urbano de veículos é uma ferramenta chave para tornar a locomoção nas metrópoles mais eficiente. Através de sistemas de gerenciamento de tráfego (TMS), é possível coletar e processar dados em tempo real a fim de mitigar os efeitos dos congestionamentos. Um passo importante dentro deste processo é a identificação de congestionamentos nas vias urbanas. Essa identificação é feita através de modelos matemáticos, os quais existem em abundância na literatura. Este trabalho se propõe a analisar os principais modelos matemáticos através de simulações de fluxo de tráfego com o objetivo de compará-los, além de analisar o impacto da escolha do modelo no desempenho de um TMS. Enquanto muitos dos trabalhos existentes que propõem TMSs focam apenas na arquitetura do sistema, este trabalho mostra que a escolha do modelo de identificação de congestionamento tem um impacto significativo no desempenho de um TMS, resultando até 8, 2% de diferença no tempo médio de viagem variando apenas o modelo escolhido.
Referências
Akabane, A. T., Immich, R., Bittencourt, L. F., Madeira, E. R., and Villas, L. A. (2020). Towards a distributed and infrastructure-less vehicular traffic management system. Computer Communications, 151:306–319.
Asvial, M., Gita Pandoyo, M. F., and Setyo Arifin, A. (2018). Internet of things solution for motorcycle riders to overcome traffic jam in jakarta using ebksp. In 2018 International Conference on Information and Communication Technology Convergence (ICTC), pages 636–638.
Asvial, M., Pandoyo, M. F. G., and Arifin, A. S. (2020). Entropy-based k shortest-path routing for motorcycles: A simulated case study in jakarta. International Journal of Advanced Computer Science and Applications, 11(7).
Banks, J. (2002). Introduction to Transportation Engineering. McGraw-Hill series in transportation. McGraw-Hill.
da Silva, P. C. M. (1994). Apostila: Teoria do Fluxo de Tráfego. Universidade de Brasília.
de Souza, A. M., Braun, T., Botega, L. C., Cabral, R., Garcia, I. C., and Villas, L. A. (2019). Better safe than sorry: a vehicular traffic re-routing based on traffic conditions and public safety issues. Journal of Internet Services and Applications, 10:1–18.
de Souza, A. M., Yokoyama, R. S., Maia, G., Loureiro, A. A. F., and Villas, L. A. (2016). Real-time path planning to prevent traffic jam through an intelligent transportation system. 2016 IEEE Symposium on Computers and Communication (ISCC), pages 726–731.
Djahel, S., Doolan, R., Muntean, G.-M., and Murphy, J. (2015). A communications-oriented perspective on traffic management systems for smart cities: Challenges and innovative approaches. IEEE Communications Surveys Tutorials, 17(1):125–151.
Drake, J., Schofer, J., and May, A. (1966). A statistical analysis of speed-density hypotheses. Highway Research Record 154, pages 53–87.
Du, R., Chen, S., Dong, J., Ha, P. Y. J., and Labi, S. (2021). Gaq-ebksp: A drl-based urban traffic dynamic rerouting framework using fog-cloud architecture. In 2021 IEEE International Smart Cities Conference (ISC2), pages 1–7.
Edie, L. C. (1961). Car-following and steady-state theory for noncongested traffic. Operations Research, 9(1):66–76.
Eppstein, D. (1998). Finding the k shortest paths. SIAM Journal on computing, 28(2):652–673.
Gerlough, D. and Huber, M. (1975). Traffic Flow Theory: A Monograph. Special Reports. Transportation Research Board, National Research Council.
Greenberg, H. (1959). An analysis of traffic flow. Operations Research, 7(1):79–85.
Greenshields, B. D., Bibbins, J., Channing, W., and Miller, H. (1935). A study of traffic capacity. In Highway research board proceedings, volume 14, pages 448–477. Washington, DC.
Guidoni, D. L., Maia, G., Souza, F. S. H., Villas, L. A., and Loureiro, A. A. F. (2020). Vehicular traffic management based on traffic engineering for vehicular ad hoc networks. IEEE Access, 8:45167–45183.
Ministério da Infraestrutura (2022). Frota de veículos 2022. Disponível em: [link]. Acesso em: 17 de mar. de 2023.
Ministério da Infraestrutura (2023). Frota de veículos 2023. Disponível em: [link]. Acesso em: 17 de mar. de 2023.
Page, L., Brin, S., Motwani, R., and Winograd, T. (1999). The pagerank citation ranking: Bringing order to the web. Technical report, Stanford infolab.
Pan, J., Khan, M. A., Popa, I. S., Zeitouni, K., and Borcea, C. (2012). Proactive vehicle re-routing strategies for congestion avoidance. In 2012 IEEE 8th International Conference on Distributed Computing in Sensor Systems, pages 265–272.
Romanowska, A. and Jamroz, K. (2021). Comparison of traffic flow models with real traffic data based on a quantitative assessment. Applied Sciences, 11:9914.
Silveira, L. M., Almeida, J. M., Marques-Neto, H., and Ziviani, A. (2015). Mobdatu: A new model for human mobility prediction based on heterogeneous data. In 2015 XXXIII Brazilian Symposium on Computer Networks and Distributed Systems, pages 217–227.
Singh, T., Solanki, A., Sharma, S. K., Nayyar, A., and Paul, A. (2022). A decade review on smart cities: Paradigms, challenges and opportunities. IEEE Access, 10:68319–68364.
Souza, A., Brennand, C., Yokoyama, R., Donato, E., Madeira, E., and Villas, L. (2017). Traffic management systems: A classification, review, challenges, and future perspectives. International Journal of Distributed Sensor Networks, 13:155014771668361.
Underwood, R. (1961). Speed, volume, and density relationship. quality and theory of traffic flow, yale bur.
Wang, S., Djahel, S., Zhang, Z., and McManis, J. (2016). Next road rerouting: A multiagent system for mitigating unexpected urban traffic congestion. IEEE Transactions on Intelligent Transportation Systems, 17(10):2888–2899.
Asvial, M., Gita Pandoyo, M. F., and Setyo Arifin, A. (2018). Internet of things solution for motorcycle riders to overcome traffic jam in jakarta using ebksp. In 2018 International Conference on Information and Communication Technology Convergence (ICTC), pages 636–638.
Asvial, M., Pandoyo, M. F. G., and Arifin, A. S. (2020). Entropy-based k shortest-path routing for motorcycles: A simulated case study in jakarta. International Journal of Advanced Computer Science and Applications, 11(7).
Banks, J. (2002). Introduction to Transportation Engineering. McGraw-Hill series in transportation. McGraw-Hill.
da Silva, P. C. M. (1994). Apostila: Teoria do Fluxo de Tráfego. Universidade de Brasília.
de Souza, A. M., Braun, T., Botega, L. C., Cabral, R., Garcia, I. C., and Villas, L. A. (2019). Better safe than sorry: a vehicular traffic re-routing based on traffic conditions and public safety issues. Journal of Internet Services and Applications, 10:1–18.
de Souza, A. M., Yokoyama, R. S., Maia, G., Loureiro, A. A. F., and Villas, L. A. (2016). Real-time path planning to prevent traffic jam through an intelligent transportation system. 2016 IEEE Symposium on Computers and Communication (ISCC), pages 726–731.
Djahel, S., Doolan, R., Muntean, G.-M., and Murphy, J. (2015). A communications-oriented perspective on traffic management systems for smart cities: Challenges and innovative approaches. IEEE Communications Surveys Tutorials, 17(1):125–151.
Drake, J., Schofer, J., and May, A. (1966). A statistical analysis of speed-density hypotheses. Highway Research Record 154, pages 53–87.
Du, R., Chen, S., Dong, J., Ha, P. Y. J., and Labi, S. (2021). Gaq-ebksp: A drl-based urban traffic dynamic rerouting framework using fog-cloud architecture. In 2021 IEEE International Smart Cities Conference (ISC2), pages 1–7.
Edie, L. C. (1961). Car-following and steady-state theory for noncongested traffic. Operations Research, 9(1):66–76.
Eppstein, D. (1998). Finding the k shortest paths. SIAM Journal on computing, 28(2):652–673.
Gerlough, D. and Huber, M. (1975). Traffic Flow Theory: A Monograph. Special Reports. Transportation Research Board, National Research Council.
Greenberg, H. (1959). An analysis of traffic flow. Operations Research, 7(1):79–85.
Greenshields, B. D., Bibbins, J., Channing, W., and Miller, H. (1935). A study of traffic capacity. In Highway research board proceedings, volume 14, pages 448–477. Washington, DC.
Guidoni, D. L., Maia, G., Souza, F. S. H., Villas, L. A., and Loureiro, A. A. F. (2020). Vehicular traffic management based on traffic engineering for vehicular ad hoc networks. IEEE Access, 8:45167–45183.
Ministério da Infraestrutura (2022). Frota de veículos 2022. Disponível em: [link]. Acesso em: 17 de mar. de 2023.
Ministério da Infraestrutura (2023). Frota de veículos 2023. Disponível em: [link]. Acesso em: 17 de mar. de 2023.
Page, L., Brin, S., Motwani, R., and Winograd, T. (1999). The pagerank citation ranking: Bringing order to the web. Technical report, Stanford infolab.
Pan, J., Khan, M. A., Popa, I. S., Zeitouni, K., and Borcea, C. (2012). Proactive vehicle re-routing strategies for congestion avoidance. In 2012 IEEE 8th International Conference on Distributed Computing in Sensor Systems, pages 265–272.
Romanowska, A. and Jamroz, K. (2021). Comparison of traffic flow models with real traffic data based on a quantitative assessment. Applied Sciences, 11:9914.
Silveira, L. M., Almeida, J. M., Marques-Neto, H., and Ziviani, A. (2015). Mobdatu: A new model for human mobility prediction based on heterogeneous data. In 2015 XXXIII Brazilian Symposium on Computer Networks and Distributed Systems, pages 217–227.
Singh, T., Solanki, A., Sharma, S. K., Nayyar, A., and Paul, A. (2022). A decade review on smart cities: Paradigms, challenges and opportunities. IEEE Access, 10:68319–68364.
Souza, A., Brennand, C., Yokoyama, R., Donato, E., Madeira, E., and Villas, L. (2017). Traffic management systems: A classification, review, challenges, and future perspectives. International Journal of Distributed Sensor Networks, 13:155014771668361.
Underwood, R. (1961). Speed, volume, and density relationship. quality and theory of traffic flow, yale bur.
Wang, S., Djahel, S., Zhang, Z., and McManis, J. (2016). Next road rerouting: A multiagent system for mitigating unexpected urban traffic congestion. IEEE Transactions on Intelligent Transportation Systems, 17(10):2888–2899.
Publicado
20/05/2024
Como Citar
SILVA, João Pedro F.; MAIA, Guilherme.
Um Estudo Comparativo de Modelos de Fluxo de Tráfego de Veículos para Sistemas de Transporte Inteligentes. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 8. , 2024, Niterói/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 183-196.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2024.3336.
