Scalable Data Analysis for Public Bus Systems
Resumo
A mobilidade urbana por meio de transporte público de qualidade é um dos principais desafios para a consolidação de cidades inteligentes. Pesquisadores desenvolveram diferentes abordagens para melhorar a confiabilidade do sistema de ônibus e a qualidade das informações, incluindo algoritmos de previsão do tempo de viagem, avaliações do estado da rede e estratégias de prevenção de agrupamento de ônibus. As informações fornecidas por essas abordagens são complementares e podem ser agregadas para obter melhores previsões. Neste trabalho, propomos a arquitetura e apresentamos uma implementação protótipo de uma estrutura que permite a integração de várias abordagens, que chamamos de modelos, em modelos compostos escaláveis e eficientes.
Palavras-chave:
Cidades inteligentes, Sistemas de ônibus públicos, Previsão de tempo de viagem, Aprendizado de máquina
Referências
Adachi, H., Suzuki, H., Asahi, K., Matsumoto, Y., and Watanabe, A. (2015). Estimation of bus traveling section using wireless sensor network. In 2015 Eighth International Conference on Mobile Computing and Ubiquitous Networking (ICMU), pages 120–125. IEEE.
Choudhary, R., Khamparia, A., and Gahier, A. K. (2016). Real time prediction of bus arrival time: A review. Proceedings on 2016 2nd International Conference on Next Generation Computing Technologies, NGCT 2016, (October):25–29.
Liu, Z., Zhao, T., and Yan, W. (2010). A novel architecture and open issues toward sinfrastructure-less city-wide traffic information systems using vehicular ad hoc networks. In2010 International Conference on Communications and Mobile Computing,volume 3, pages 522–527.
Liying, W. (2009). Design of the distributed framework and dataflow process for urban road traffic information systems. In 2009 Second International Conference on Intelligent Computation Technology and Automation, volume 3, pages 558–561.
Mazloumi, E., Rose, G., Currie, G., and Sarvi, M. (2011). An integrated framework to predict bus travel time and its variability using traffic flow data. Journal of Intelligent Transportation Systems, 15(2):75–90.
Mori, U., Mendiburu, A., Álvarez, M., and Lozano, J. A. (2015). A review of travel time estimation and forecasting for Advanced Traveller Information Systems. Transportmetrica A: Transport Science, 11(2):119–157.
Yu, H., Wu, Z., Chen, D., and Ma, X. (2017). Probabilistic prediction of bus headway using relevance vector machine regression. IEEE Transactions on Intelligent Trans-portation Systems, 18(7):1772–1781.
Zhang, X., Chen, G., Han, Y., and Gao, M. (2016). Modeling and analysis of bus weighted complex network in qingdao city based on dynamic travel time. Multimedia Tools and Applications, 75(24):17553–17572.
Choudhary, R., Khamparia, A., and Gahier, A. K. (2016). Real time prediction of bus arrival time: A review. Proceedings on 2016 2nd International Conference on Next Generation Computing Technologies, NGCT 2016, (October):25–29.
Liu, Z., Zhao, T., and Yan, W. (2010). A novel architecture and open issues toward sinfrastructure-less city-wide traffic information systems using vehicular ad hoc networks. In2010 International Conference on Communications and Mobile Computing,volume 3, pages 522–527.
Liying, W. (2009). Design of the distributed framework and dataflow process for urban road traffic information systems. In 2009 Second International Conference on Intelligent Computation Technology and Automation, volume 3, pages 558–561.
Mazloumi, E., Rose, G., Currie, G., and Sarvi, M. (2011). An integrated framework to predict bus travel time and its variability using traffic flow data. Journal of Intelligent Transportation Systems, 15(2):75–90.
Mori, U., Mendiburu, A., Álvarez, M., and Lozano, J. A. (2015). A review of travel time estimation and forecasting for Advanced Traveller Information Systems. Transportmetrica A: Transport Science, 11(2):119–157.
Yu, H., Wu, Z., Chen, D., and Ma, X. (2017). Probabilistic prediction of bus headway using relevance vector machine regression. IEEE Transactions on Intelligent Trans-portation Systems, 18(7):1772–1781.
Zhang, X., Chen, G., Han, Y., and Gao, M. (2016). Modeling and analysis of bus weighted complex network in qingdao city based on dynamic travel time. Multimedia Tools and Applications, 75(24):17553–17572.
Publicado
19/08/2020
Como Citar
MORAIS, Mayuri A.; DE CAMARGO, Raphael Y..
Scalable Data Analysis for Public Bus Systems. In: ESCOLA REGIONAL DE ALTO DESEMPENHO DE SÃO PAULO (ERAD-SP), 11. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 54-57.
DOI: https://doi.org/10.5753/eradsp.2020.16885.
