Analysis of the Quality of Service of Public Urban Buses Based on GPS Monitoring
DOI:
https://doi.org/10.5753/jisa.2026.6668Keywords:
Internet of Things, intelligent transportation systems, smart citiesAbstract
The introduction of GPS-equipped IoT devices onboard of urban buses allows the collection of data to monitor the fleet and assess the quality of service of this public transportation modality. The present work analyzes the GPS data from Rio de Janeiro buses, which includes executed trajectories, bus routes and identifiers to estimate per-route performance metrics with the collected data. In particular, the time between two buses, the number of bunched buses, the time spent on the inner fraction of the route, and the entropy are investigated as indicators of quality of service. The results reveal the unpredictability degree of each analyzed route. Lastly, we investigate the correlations between different metrics as a means of discovering relationships between metric performances. Our analysis shows that some routes exhibit greater regularity than others when individual metrics are considered. In addition, we observe a correlation between bus interval and bus bunching, with longer intervals reducing the likelihood of bunching. Also, we observed a correlation between entropy and time spent in the inner fraction of the route. Those results provide useful information in fleet planning activities for public transport.
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Cuttone, A., Lehmann, S., and González, M. C. (2018). Understanding predictability and exploration in human mobility. EPJ Data Science, 7(1):2. DOI: 10.1140/epjds/s13688-017-0129-1.
DATA.RIO (2025). Data.rio - portal de dados abertos da cidade do rio de janeiro. Available at:[link]. Acesso: 14-Mar-2025.
dos Santos, J. B. and Lima, J. P. (2021). Quality of public transportation based on the multi-criteria approach and from the perspective of user's satisfaction level: A case study in a brazilian city. Case Studies on Transport Policy, 9(3):1233-1244. DOI: 10.1016/j.cstp.2021.05.015.
Du, B. and Dublanche, P.-A. (2018). Bus bunching identification using smart card data. In 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS), pages 1087-1092. DOI: 10.1109/PADSW.2018.8644611.
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. DOI: 10.1016/j.cie.2015.03.004.
Hosseini, S. S., Ardabili, B. R., Azarbayjani, M., and Tabkhi, H. (2025). Demographic disparities, service efficiency, safety, and user satisfaction in public bus transit system: A survey-based case study in the city of charlotte, nc. Transportation Research Interdisciplinary Perspectives, 29:101296. DOI: 10.1016/j.trip.2024.101296.
Huang, Z., Xu, S., Wang, M., Wu, H., Xu, Y., and Jin, Y. (2024). Human mobility prediction with causal and spatial-constrained multi-task network. EPJ Data Science, 13(1):22. DOI: 10.1140/epjds/s13688-024-00460-7.
Ikanovic, E. L. and Mollgaard, A. (2017). An alternative approach to the limits of predictability in human mobility. EPJ Data Science, 6(1):12. DOI: 10.1140/epjds/s13688-017-0107-7.
Li, G., Knoop, V. L., and 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. DOI: 10.1016/j.trc.2022.103607.
Lu, X., Wetter, E., Bharti, N., Tatem, A. J., and Bengtsson, L. (2013). Approaching the limit of predictability in human mobility. Scientific Reports, 3(1):2923. DOI: 10.1038/srep02923.
Nguyen, K., Yang, J., Lin, Y., Lin, J., Chiang, Y.-Y., and Shahabi, C. (2023). Los Angeles Metro Bus Data Analysis Using GPS Trajectory and Schedule Data. Available at:[link] Sponsored By California Department of Transportation.
Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3):379-423. DOI: 10.1002/j.1538-7305.1948.tb01338.x.
Smith, G., Wieser, R., Goulding, J., and Barrack, D. (2014). A refined limit on the predictability of human mobility. In 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom), pages 88-94. DOI: 10.1109/PerCom.2014.6813948.
Song, C., Qu, Z., Blumm, N., and Barabási, A.-L. (2010). Limits of predictability in human mobility. Science, 327(5968):1018-1021. DOI: 10.1126/science.1177170.
Teixeira, D. d. C., Almeida, J. M., and Viana, A. C. (2021). On estimating the predictability of human mobility: the role of routine. EPJ Data Science, 10(1):49. DOI: 10.1140/epjds/s13688-021-00304-8.
Teixeira, D. d. C., Viana, A. C., Alvim, M. S., and 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. DOI: 10.1145/3347146.3359093.
Wang, P., Chen, X., Zheng, Y., Cheng, L., Wang, Y., and 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. DOI: 10.1016/j.tra.2021.04.007.
Zhao, K., Khryashchev, D., Freire, J., Silva, C., and 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), pages 833-842. DOI: 10.1109/BigData.2016.7840676.
Zheng, Y., Kong, H., Petzhold, G., Barcelos, M. M., Zegras, C. P., and Zhao, J. (2022). Gender differences in the user satisfaction and service quality improvement priority of public transit bus system in porto alegre and fortaleza, brazil. Travel Behaviour and Society, 28:22-37. DOI: 10.1016/j.tbs.2022.02.003.
Zhou, C., Tian, Q., and Wang, D. Z. (2022). A novel control strategy in mitigating bus bunching: Utilizing real-time information. Transport Policy, 123:1-13. DOI: 10.1016/j.tranpol.2022.04.022.
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