Analysis of the Quality of Service of Public Urban Buses Using GPS Monitoring

• Tiago R. Borzino UFRJ
• Fernando D. M. Silva UFRJ https://orcid.org/0000-0003-2164-0675
• Aline C. Viana INRIA https://orcid.org/0000-0002-1483-6269
• Luís Henrique M. K. Costa UFRJ https://orcid.org/0000-0002-7789-3359

DOI: https://doi.org/10.5753/courb.2025.9544

Abstract

The introduction of IoT devices with GPS on Urban buses allows the usage of data collection to monitor the fleet and assess the quality of service. This work analyses the GPS data of Rio de Janeiro buses, including trip trajectories, routes and bus identifiers. Metrics of performance are estimated by route using the collected data. In particular, the time between two buses, the number of bunched buses, the time to cross a fraction of the route and entropy are investigated as indicators of quality of service. The results show the regularity or unpredictability of the different routes analyzed. Lastly, the correlation between the different metrics is investigated as a means of discovering whether the improvement or the worsening of a metric reflects in the performance of another.

Keywords: Smart Cities, Internet of Things, Human Mobility, Intelligent Transport Systems, Urban Data Visualization

References

Cuttone, A., Lehmann, S., & González, M. C. (2018). Understanding predictability and exploration in human mobility. EPJ Data Science, 7(1), 2.

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.