Análise da Qualidade de Serviço do Sistema Público de Ônibus Urbanos através de Monitoramento por GPS
Resumo
A introdução de dispositivos IoT equipados com GPS nos ônibus urbanos possibilita a coleta de dados para monitorar a frota e avaliar a qualidade do serviço prestado. Este trabalho analisa dados de GPS dos ônibus do Rio de Janeiro, incluindo trajetórias de viagens, linha e identificador do veículo. A partir dessas informações, são estimadas métricas de desempenho de cada linha. Em particular, são investigadas as métricas de intervalo de passagem, número de agrupamentos de ônibus, tempo para percorrer uma fração da trajetória e entropia como indicadores de qualidade de serviço. Os resultados demonstram a regularidade ou imprevisibilidade das diferentes linhas analisadas. Por fim, investiga-se a correlação entre as diferentes métricas a fim de mostrar se a piora ou melhora em um critério de desempenho reflete em algum dos outros.
Palavras-chave:
Cidades Inteligentes, Internet das Coisas, Mobilidade Humana, Sistemas de Transporte Inteligentes, Visualização de Dados Urbanos
Referências
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Du, B., & Dublanche, P.-A. (2018). Bus bunching identification using smart card data. In 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS) (pp. 1087–1092).
He, S.-X. (2015). An anti-bunching strategy to improve bus schedule and headway reliability by making use of the available accurate information. Computers & Industrial Engineering, 85, 17–32.
Huang, Z., Xu, S., Wang, M., Wu, H., Xu, Y., & Jin, Y. (2024). Human mobility prediction with causal and spatial-constrained multi-task network. EPJ Data Science, 13(1), 22.
Ikanovic, E. L., & Mollgaard, A. (2017). An alternative approach to the limits of predictability in human mobility. EPJ Data Science, 6(1), 12.
Li, G., Knoop, V. L., & van Lint, H. (2022). Estimate the limit of predictability in short-term traffic forecasting: An entropy-based approach. Transportation Research Part C: Emerging Technologies, 138, 103607.
Nguyen, K., Yang, J., Lin, Y., Lin, J., Chiang, Y.-Y., & Shahabi, C. (2023). Los Angeles Metro Bus Data Analysis Using GPS Trajectory and Schedule Data. Tech Report CA18-3124, University of Southern California, California, USA.
Prefeitura do Rio de Janeiro. (2025). Data.rio - portal de dados abertos da cidade do Rio de Janeiro. Accessed March 14, 2025.
Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379–423.
Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010). Limits of predictability in human mobility. Science, 327(5968), 1018–1021.
Teixeira, D. d. C., Almeida, J. M., & Viana, A. C. (2021). On estimating the predictability of human mobility: the role of routine. EPJ Data Science, 10(1), 49.
Teixeira, D. d. C., Viana, A. C., Alvim, M. S., & Almeida, J. M. (2019). Deciphering predictability limits in human mobility. In SIGSPATIAL 2019 - 7th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Chicago, United States.
Wang, P., Chen, X., Zheng, Y., Cheng, L., Wang, Y., & Lei, D. (2021). Providing real-time bus crowding information for passengers: A novel policy to promote high-frequency transit performance. Transportation Research Part A: Policy and Practice, 148, 316–329.
Zhao, K., Khryashchev, D., Freire, J., Silva, C., & Vo, H. (2016). Predicting taxi demand at high spatial resolution: Approaching the limit of predictability. In 2016 IEEE International Conference on Big Data (Big Data) (pp. 833–842).
Zhou, C., Tian, Q., & Wang, D. Z. (2022). A novel control strategy in mitigating bus bunching: Utilizing real-time information. Transport Policy, 123, 1–13.
Du, B., & Dublanche, P.-A. (2018). Bus bunching identification using smart card data. In 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS) (pp. 1087–1092).
He, S.-X. (2015). An anti-bunching strategy to improve bus schedule and headway reliability by making use of the available accurate information. Computers & Industrial Engineering, 85, 17–32.
Huang, Z., Xu, S., Wang, M., Wu, H., Xu, Y., & Jin, Y. (2024). Human mobility prediction with causal and spatial-constrained multi-task network. EPJ Data Science, 13(1), 22.
Ikanovic, E. L., & Mollgaard, A. (2017). An alternative approach to the limits of predictability in human mobility. EPJ Data Science, 6(1), 12.
Li, G., Knoop, V. L., & van Lint, H. (2022). Estimate the limit of predictability in short-term traffic forecasting: An entropy-based approach. Transportation Research Part C: Emerging Technologies, 138, 103607.
Nguyen, K., Yang, J., Lin, Y., Lin, J., Chiang, Y.-Y., & Shahabi, C. (2023). Los Angeles Metro Bus Data Analysis Using GPS Trajectory and Schedule Data. Tech Report CA18-3124, University of Southern California, California, USA.
Prefeitura do Rio de Janeiro. (2025). Data.rio - portal de dados abertos da cidade do Rio de Janeiro. Accessed March 14, 2025.
Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379–423.
Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010). Limits of predictability in human mobility. Science, 327(5968), 1018–1021.
Teixeira, D. d. C., Almeida, J. M., & Viana, A. C. (2021). On estimating the predictability of human mobility: the role of routine. EPJ Data Science, 10(1), 49.
Teixeira, D. d. C., Viana, A. C., Alvim, M. S., & Almeida, J. M. (2019). Deciphering predictability limits in human mobility. In SIGSPATIAL 2019 - 7th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Chicago, United States.
Wang, P., Chen, X., Zheng, Y., Cheng, L., Wang, Y., & Lei, D. (2021). Providing real-time bus crowding information for passengers: A novel policy to promote high-frequency transit performance. Transportation Research Part A: Policy and Practice, 148, 316–329.
Zhao, K., Khryashchev, D., Freire, J., Silva, C., & Vo, H. (2016). Predicting taxi demand at high spatial resolution: Approaching the limit of predictability. In 2016 IEEE International Conference on Big Data (Big Data) (pp. 833–842).
Zhou, C., Tian, Q., & Wang, D. Z. (2022). A novel control strategy in mitigating bus bunching: Utilizing real-time information. Transport Policy, 123, 1–13.
Publicado
19/05/2025
Como Citar
BORZINO, Tiago R.; SILVA, Fernando D. M.; VIANA, Aline C.; COSTA, Luís Henrique M. K..
Análise da Qualidade de Serviço do Sistema Público de Ônibus Urbanos através de Monitoramento por GPS. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 9. , 2025, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 265-278.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2025.9544.
