User-centered analysis of a safe bus routing strategy




context-aware mobility, public transportation, safe routing, flow extraction


Context-aware mobility has the potential to make the way we travel more efficient, safer, and more sustainable. Among the possible contexts, safety, in terms of crime levels in city regions, is one that has been used to calculate safer routes. Making a bus route safer is important to improve the quality of life of the passengers, who often are victims of criminals during their journey. However, existing studies focus only on private vehicles and do not assess the impact for citizens as a whole. In this work, an existing solution for calculating safe routes is evaluated in the context of public bus transport in terms of the impact caused to passengers. The results showed that, in general, changing a bus route to make it safer increases the distance traveled by a few kilometers for most passengers. This small increase in distance is not harmful to the passengers, given that they will be at less risk to face any kind of criminal situation. In addition to this analysis, a scalable tool for extracting mobility flow was also developed.


Download data is not yet available.


Almeida, V. G. J., Silva, T. R. M. B., and Silva, F. A. (2022). Se for, vá na paz: Construindo rotas seguras para veículos coletivos urbanos. In Anais do Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/sbrc.2022.221978.

Babu, V. S. and Viswanath, P. (2008). An efficient and fast parzen-window density based clustering method for large data sets. In International Conference on Emerging Trends in Engineering and Technology, pages 531-536. DOI: 10.1109/ICETET.2008.166.

Barbosa, H., Barthelemy, M., Ghoshal, G., James, C. R., Lenormand, M., Louail, T., Menezes, R., Ramasco, J. J., Simini, F., and Tomasini, M. (2018). Human mobility: Models and applications. Physics Reports, 734:1-74. Human mobility: Models and applications. DOI: 10.1016/j.physrep.2018.01.001.

Galbrun, E., Pelechrinis, K., and Terzi, E. (2016). Urban navigation beyond shortest route: The case of safe paths. Information Systems, 57:160-171. DOI: 10.1016/

Graser, A. (2019). MovingPandas: Efficient structures for movement data in Python. GI_Forum - Journal of Geographic Information Science 2019, 7:54-68. DOI: 10.1553/giscience2019_01_s54.

Guo, D., Zhu, X., Jin, H., Gao, P., and Andris, C. (2012). Discovering spatial patterns in origin–destination mobility data. Transactions in GIS, 16. DOI: 10.1111/j.1467-9671.2012.01344.x.

Iqbal, M. S., Choudhury, C. F., Wang, P., and González, M. C. (2014). Development of origin–destination matrices using mobile phone call data. Transportation Research Part C: Emerging Technologies, 40:63-74. DOI: 10.1016/j.trc.2014.01.002.

Kon, F., Ferreira, E., Souza, H., Duarte, F., Santi, P., and Ratti, C. (2021). Abstracting mobility flows from bike-sharing systems. Public Transport. DOI: 10.1007/s12469-020-00259-5.

Ladeira, L., Souza, A., Pereira, G., Silva, T. H., and Villas, L. (2019). Serviço de sugestão de rotas seguras para veículos. In Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 608-621, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/sbrc.2019.7390.

Liu, Q., Kumar, S., and Mago, V. (2017). Safernet: Safe transportation routing in the era of internet of vehicles and mobile crowd sensing. 2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC). DOI: 10.1109/ccnc.2017.7983123.

Martins, T., Lago, N., Zambom Santana, E. F., Telea, A., Kon, F., and Souza, H. (2021). Using bundling to visualize multivariate urban mobility structure patterns in the são paulo metropolitan area. Journal of Internet Services and Applications, 12:6. DOI: 10.1186/s13174-021-00136-9.

Mata, F., Torres-Ruiz, M., Guzmán, G., Quintero, R., Zagal-Flores, R., Moreno-Ibarra, M., and Loza, E. (2016). A mobile information system based on crowd-sensed and official crime data for finding safe routes: A case study of mexico city. Mobile Information Systems, 2016:1-11. DOI: 10.1155/2016/8068209.

Montoliu, R., Blom, J., and Gática-Pérez, D. (2011). Discovering places of interest in everyday life from smartphone data. Multimedia Tools and Applications, 62:179-207. DOI: 10.1007/s11042-011-0982-z.

Moreno-Monroy, A., Lovelace, R., and Ramos, F. (2017). Public transport and school location impacts on educational inequalities: Insights from são paulo. Journal of Transport Geography, 67. DOI: 10.1016/j.jtrangeo.2017.08.012.

Pappalardo, L., Simini, F., Barlacchi, G., and Pellungrini, R. (2021). scikit-mobility: a Python library for the analysis, generation and risk assessment of mobility data. DOI: 10.48550/arXiv.1907.07062.

Santos, F. A., Rodrigues, D. O., Silva, T. H., Loureiro, A. A. F., Pazzi, R. W., and Villas, L. A. (2018). Context-aware vehicle route recommendation platform: Exploring open and crowdsourced data. In 2018 IEEE International Conference on Communications (ICC), pages 1-7. DOI: 10.1109/ICC.2018.8422972.

Santos, F. A., Rodrigues, D. O., Silva, T. H., Loureiro, A. A. F., and Villas, L. A. (2017). Rotas veiculares cientes de contexto: Arcabouço e aná lise usando dados oficiais e sensoriados por usuários sobre crimes. In Anais do XXII Workshop de Gerência e Operação de Redes e Serviços, Porto Alegre, RS, Brasil. SBC. Available online [link].

Tompson, L., Partridge, H., and Shepherd, N. (2009). Hot routes: Developing a new technique for the spatial analysis of crime. Crime Mapping: A Journal of Research and Practice, 1(1):77-96. Available online [link].

Yu, J., Zhang, Z., and Sarwat, M. (2019). Spatial data management in Apache Spark: The GeoSpark perspective and beyond. Geoinformatica, 23(1):37–78. DOI: 10.1007/s10707-018-0330-9.

Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauly, M., Franklin, M. J., Shenker, S., and Stoica, I. (2012). Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. In 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI 12), pages 15-28, San Jose, CA. USENIX Association. Available online [link].




How to Cite

Ramos, J. M. A. M., Almeida, V. G. J., Santana, H. S., Braga Silva, T. R. M., & Silva, F. A. (2023). User-centered analysis of a safe bus routing strategy. Journal of Internet Services and Applications, 14(1), 84–94.



Research article