Towards spatiotemporal integration of bus transit with data-driven approaches




bus transit network, spatiotemporal integration, data-driven model, urban computing, smart city


This study aims to propose an approach for spatiotemporal integration of bus transit, which enables users to change bus lines by paying a single fare. This could increase bus transit efficiency and, consequently, help to make this mode of transportation more attractive. Usually, this strategy is allowed for a few hours in a non-restricted area; thus, certain walking distance areas behave like "virtual terminals". For that, two data-driven algorithms are proposed in this work. First, a new algorithm for detecting itineraries based on bus GPS data and the bus stop location. The proposed algorithm's results show that 90% of the database detected valid itineraries by excluding invalid markings and adding times at missing bus stops through temporal interpolation. Second, this study proposes a bus stop clustering algorithm to define suitable areas for these virtual terminals where it would be possible to make bus transfers outside the physical terminals. Using real-world origin-destination trips, the bus network, including clusters, can reduce traveled distances by up to 50%, at the expense of making twice as many connections on average.


Download data is not yet available.


Arriagada, J., Munizaga, M. A., Guevara, C. A., and Prato, C. (2022). Unveiling route choice strategy heterogeneity from smart card data in a large-scale public transport network. Transportation Research Part C: Emerging Technologies, 134. DOI: 10.1016/j.trc.2021.103467.

Bona, A. A. D., Fonseca, K. V., Rosa, M. O., Lüders, R., and Delgado, M. R. (2016). Analysis of public bus transportation of a Brazilian city based on the theory of complex networks using the p-space. Mathematical Problems in Engineering, 2016. DOI: 10.1155/2016/3898762.

Borges, J., Lüders, R., Silva, T., and Munaretto, A. (2023). Algoritmo para detecção de itinerários do transporte público usando dados de gps dos Ônibus. In Anais do VII Workshop de Computação Urbana, pages 1-14, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/courb.2023.739.

Chawuthai, R., Sumalee, A., and Threepak, T. (2023). GPS data analytics for the assessment of public city bus transportation service quality in Bangkok. Sustainability, 15(7). DOI: 10.3390/su15075618.

Curzel, J. L., Lüders, R., Fonseca, K. V., and Rosa, M. O. (2019). Temporal performance analysis of bus transportation using link streams. Mathematical Problems in Engineering, 2019. DOI: 10.1155/2019/6139379.

Desai, S., Suthar, R., Yadav, V., Ankar, V., and Gupta, V. (2022). Smart bus fleet management system using IoT. In 2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), pages 01-06. DOI: 10.1109/ICERECT56837.2022.10059646.

Gallotti, R. and Barthelemy, M. (2015). The multilayer temporal network of public transport in Great Britain. Scientific Data, 2:140056. DOI: 10.1038/sdata.2014.56.

Gubert, F. R., Santin, P., Fonseca, M., Munaretto, A., and Silva, T. H. (2023). On strategies to help reduce contamination on public transit: a multilayer network approach. Applied Network Science, 8(1):1-22. DOI: 10.1007/s41109-023-00562-7.

Hakeem, M. F. M. A., Sulaiman, N. A., Kassim, M., and Isa, N. M. (2022). IoT bus monitoring system via mobile application. In 2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS), pages 125-130. DOI: 10.1109/I2CACIS54679.2022.9815268.

IPPUC (2017). Consolidação de Dados de Oferta, Demanda, Sistema Viário e Zoneamento: Relatório 5 - Pesquisa Origem-Destino Domiciliar. Available online [link] (Last accessed: 2023-06-14).

Kumar, P. and Khani, A. (2023). Schedule-based transit assignment with online bus arrival information. Transportation Research Part C: Emerging Technologies, 155. DOI: 10.1016/j.trc.2023.104282.

Lawhead, J. (2015). Learning geospatial analysis with Python. Packt Publishing Ltd, Birmingham, 2nd edition.

Li, T., Meredith-Karam, P., Kong, H., Stewart, A., Attanucci, J. P., and Zhao, J. (2021). Comparison of door-to-door transit travel time estimation using schedules, real-time vehicle arrivals, and smartcard inference methods, volume 2675, pages 1003-1014. SAGE Publications Ltd. DOI: 10.1177/03611981211023768.

Li, T. and Rong, L. (2022). Spatiotemporally complementary effect of high-speed rail network on robustness of aviation network. Transportation Research Part A: Policy and Practice, 155:95-114. DOI: 10.1016/j.tra.2021.10.020.

Liu, T., Ji, W., Gkiotsalitis, K., and Cats, O. (2023). Optimizing public transport transfers by integrating timetable coordination and vehicle scheduling. Computers and Industrial Engineering, 184. DOI: 10.1016/j.cie.2023.109577.

Maduako, I. D., Wachowicz, M., and Hanson, T. (2019). Transit performance assessment based on graph analytics. Transportmetrica A: Transport Science, 15(2):1382-1401. DOI: 10.1080/23249935.2019.1596991.

Martins, T., Kozievitch, N., Gadda, T., Rosa, M., and Gutierrez, M. (2022). Map matching: Uma análise de dados streaming de trajetórias de GPS no transporte público. In Temas Emergentes: Cidades Inteligentes (XVIII SBSI), pages 294-301. SBC. DOI: 10.5753/sbsi_estendido.2022.221647.

Motta, R. A., Silva, P. C. M. D., and Santos, M. P. D. S. (2013). Crisis of public transport by bus in developing countries: a case study from brazil. International Journal of Sustainable Development and Planning, 8:348-361. DOI: 10.2495/SDP-V8-N3-348-361.

Noichan, R. and Dewancker, B. (2018). Analysis of accessibility in an urban mass transit node: A case study in a bangkok transit station. Sustainability (Switzerland), 10. DOI: 10.3390/su10124819.

Pan, L., Waygood, E. O., and Patterson, Z. (2023). Public transit itinerary choice analysis considering various incentives. Transportation Research Record, 2677:722-733. DOI: 10.1177/03611981231166682.

Panigrahi, N. (2014). Computing in geographic information systems. CRC Press, Boca Raton, Florida, 1st edition.

Peixoto, A., Rosa, M., Lüders, R., and Fonseca, K. (2020). Plataforma computacional para construção de um banco de dados de grafo do sistema de transporte de Curitiba. In IV Workshop de Computação Urbana, pages 125-137. SBC. DOI: 10.5753/courb.2020.12358.

Prathyusha, C., Singh, S., and Shivananda, P. (2021). Strategies for sustainable, efficient, and economic integration of public transportation systems. volume 121 LNCE, pages 157-169. Springer Science and Business Media Deutschland GmbH. DOI: 10.1007/978-981-33-4114-2_13.

Queiroz, A. R., Santos, V., Nascimento, D., and Pires, C. E. (2019). Conformity analysis of GTFS routes and bus trajectories. In XXXIV Simpósio Brasileiro de Banco de Dados, pages 199-204. SBC. DOI: 10.5753/sbbd.2019.8823.

Rodrigues, D. O., Boukerche, A., Silva, T. H., Loureiro, A. A., and Villas, L. A. (2017). SMAFramework: Urban Data Integration Framework for Mobility Analysis in Smart Cities. In Proceedings of the 20th ACM International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems, MSWiM '17, page 227–236, New York, NY, USA. Association for Computing Machinery. DOI: 10.1145/3127540.3127569.

Rosa, M. O., Fonseca, K. V. O., Kozievitch, N. P., De-Bona, A. A., Curzel, J. L., Pando, L. U., Prestes, O. M., and Lüders, R. (2020). Advances on Urban Mobility Using Innovative Data-Driven Models, pages 1-38. Springer International Publishing, Cham. DOI: 10.1007/978-3-030-15145-4_57-1.

Sadeghian, P., Håkansson, J., and Zhao, X. (2021). Review and evaluation of methods in transport mode detection based on GPS tracking data. Journal of Traffic and Transportation Engineering (English Edition), 8(4):467-482. DOI: 10.1016/j.jtte.2021.04.004.

Santin, P., Gubert, F. R., Fonseca, M., Munaretto, A., and Silva, T. H. (2020). Characterization of public transit mobility patterns of different economic classes. Sustainability, 12(22). DOI: 10.3390/su12229603.

Singla, L. and Bhatia, P. (2015). GPS based bus tracking system. In 2015 International Conference on Computer, Communication and Control (IC4), pages 1-6. DOI: 10.1109/IC4.2015.7375712.

Sridevi, K., Jeevitha, A., Kavitha, K., Sathya, K., and Narmadha, K. (2017). Smart bus tracking and management system using IoT. Asian Journal of Applied Science and Technology (AJAST), 1(2). Available at SSRN: [link].

Steiner, K. and Irnich, S. (2020). Strategic planning for integrated mobility-on-demand and urban public bus networks. Transportation Science, 54:1616-1639. DOI: 10.1287/trsc.2020.0987.

URBS (2022a). Características da rede integrada de transporte. URL: [link] (Last accessed: 2023-03-27).

URBS (2022b). Web-service: Dados públicos da rede integrada do transporte coletivo de Curitiba. URL: [link] (Last accessed: 2023-03-27).

Vila, J. J. R., Kozievitch, N. P., Gadda, T. M., Fonseca, K., Rosa, M. O., Gomes-Jr, L. C., and Akbar, M. (2016). Urban mobility challenges-an exploratory analysis of public transportation data in Curitiba. Revista de Informática Aplicada, 12(1). DOI: 10.13037/ria.vol12n1.145.

Wang, S., Sun, Y., Musco, C., and Bao, Z. (2021). Public transport planning: When transit network connectivity meets commuting demand. pages 1906-1919. Association for Computing Machinery. DOI: 10.1145/3448016.3457247.

War, M. M., Rakhra, M., and Singh, D. (2022). Review on application based bus tracking system. In 2022 5th International Conference on Contemporary Computing and Informatics (IC3I), pages 876-880. DOI: 10.1109/IC3I56241.2022.10072449.

Wehmuth, K., Costa, B., Bechara, J. V., and Ziviani, A. (2018). A multilayer and time-varying structural analysis of the Brazilian air transportation network. In Latin America Data Science Workshop, volume 2170 of CEUR Workshop Proceedings, pages 57-64. DOI: 10.48550/arXiv.1709.03360.

Welch, T. F. and Widita, A. (2019). Big data in public transportation: a review of sources and methods. Transport Reviews, 39(6):795-818. DOI: 10.1080/01441647.2019.1616849.

Wilson, N. H., Zhao, J., and Rahbee, A. (2009). The potential impact of automated data collection systems on urban public transport planning., pages 1-25. DOI: 10.1007/978-0-387-84812-9_5.

Yen, J. Y. (1971). Finding the k shortest loopless paths in a network. Management Science, 17:712-716. DOI: 10.1287/mnsc.17.11.712.

Yin, L., Hu, J., Huang, L., Zhang, F., and Ren, P. (2014). Detecting illegal pickups of intercity buses from their GPS traces. In 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pages 2162-2167. DOI: 10.1109/ITSC.2014.6958023.

Żochowska, R., Kłos, M. J., Soczówka, P., and Pilch, M. (2022). Assessment of accessibility of public transport by using temporal and spatial analysis. Sustainability (Switzerland), 14. DOI: 10.3390/su142316127.




How to Cite

Borges, J. C., Peixoto, A. M., Silva, T. H., Munaretto, A., & Lüders, R. (2024). Towards spatiotemporal integration of bus transit with data-driven approaches. Journal of Internet Services and Applications, 15(1), 59–71.



Research article