STEIN - Sistema de Tráfego e Intervenções - Desenvolvimento de um Sistema de Transporte Inteligente

• Matheus Sanches Quessada Universidade Estadual Paulista
• Rickson Simioni Pereira Universidade Estadual Paulista
• Euclydes Nasorri Gottsfritz Universidade Estadual Paulista
• Douglas Dias Lieira Universidade Estadual Paulista
• Luis Hideo Vasconcelos Nakamura Instituto Federal de São Paulo
• Marco Antonio Colombo da Silva Instituto Federal de São Paulo
• Rodolfo Ipolito Meneguette Universidade de São Paulo

DOI: https://doi.org/10.5753/sbrc_estendido.2020.12397

Abstract

The increase in urban centers has several side effects, such as socioeconomic and health problems for the general population, due to the generation of toxic gases, as well as obstacles involving traffic, thus generating loss of money due to lost time. The proposed work is the development of a traffic monitoring system and the generation of information about it, such as adverse events present on the roads. For the evaluation of the proposed system, a use case was developed for the city of Catanduva - SP, which was carried out a test with the proposed systems, it is possible to verify the reduction of the vehicle downtime by 45% and a reduction of travel time of 50% with an average speed of 33km/h.

Keywords: intervenção, dataset, simulação, sistema de transporte inteligente

References

Al-Sultan, S., Al-Doori, M. M., Al-Bayatti, A. H., and Zedan, H. (2014). A comprehen-sive survey on vehicular ad hoc network. Journal of network and computer applica-tions, 37:380-392.

Cookson, G. and Pishue, B. (2017). Inrix global traffic scorecard-appendices. INRIXresearch.

Lafferriere, G. (2019). Traffic signal consensus control. NITC-RR-1165. Portland, OR:Transportation Research and Education Center (TREC).

Liebig, T., Piatkowski, N., Bockermann, C., and Morik, K. (2017). Dynamic route plan-ning with real-time traffic predictions. Information Systems, 64:258-265.

Lin, J., Yu, W., Yang, X., Yang, Q., Fu, X., and Zhao, W. (2016). A real-time en-routeroute guidance decision scheme for transportation-based cyberphysical systems. IEEETransactions on Vehicular Technology, 66(3):2551-2566.

Meneguette, R. I., De Grande, R., and Loureiro, A. A. (2018). Intelligent TransportSystem in Smart Cities. Springer.

Pan, J., Popa, I. S., Zeitouni, K., and Borcea, C. (2013). Proactive vehicular traffic rerout-ing for lower travel time. IEEE Transactions on vehicular technology, 62(8):3551-3568.

Pereira, R. S., Lieira, D. D., da Silva, M. A. C., Pimenta, A. H. M., da Costa, J. B. D.,Rosário, D., and Meneguette, R. I. (2019). A novel fog-based resource allocationpolicy for vehicular clouds in the highway environment. In 2019 IEEE Latin-AmericanConference on Communications (LATINCOM), pages 1-6.

Quessada, M., Cristiani, A., Ranzani, P., Pereira, R., Leal, M., and Meneguette, R. (2019).Desenvolvimento de um modelo de mobilidade urbana em tempo real para simuladoresde rede. In Anais Estendidos do XXXVII Simpósio Brasileiro de Redes de Computa-dores e Sistemas Distribuídos, pages 81-88, Porto Alegre, RS, Brasil. SBC.

Silva, B. N., Khan, M., and Han, K. (2018). Towards sustainable smart cities: A reviewof trends, architectures, components, and open challenges in smart cities. SustainableCities and Society, 38:697-713.

Souza, L. (2019). Vendas de veículos crescem 11,4% no acumulado até setembro.Disponível em: http://agenciabrasil.ebc.com.br/economia/noticia/2019-10/vendas-de-veiculos-crescem-1 14-no-acumulado-ate-setembro.

Zhong, R., Sumalee, A., Pan, T., and Lam, W. (2013). Stochastic cell transmission model for traffic network with demand and supply uncertainties. Transportmetrica A: Trans-port Science, 9(7):567-602.