Uma análise comparativa de técnicas de detecção de pontos de parada em ambientes urbanos
Resumo
Este artigo apresenta um framework para a criação de conjuntos de dados de referência (ground-truth) destinados à detecção automatizada de pontos de parada. O framework utiliza dados do OpenStreetMap e o SUMO (Simulation of Urban MObility) como fontes de informação essenciais. Além disso, são implementados e comparados métodos amplamente discutidos na literatura para a detecção de pontos de parada, utilizando conjuntos de dados gerados por meio desse framework. Os resultados da análise confirmam a confiabilidade dos métodos estudados. O estudo também introduz novos algoritmos à análise, que demonstram ser promissores na detecção de pontos de parada, além de identificar áreas para melhorias futuras. Destacam-se a necessidade de explorar análises adicionais que considerem métodos alternativos de aquisição de dados e avaliem seus impactos na detecção de pontos de parada.Referências
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Haidri, S., Haranwala, Y. J., Bogorny, V., Renso, C., da Fonseca, V. P., and Soares, A. (2021). Ptrail–a python package for parallel trajectory data preprocessing. arXiv:2108.13202.
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Lopez, P. A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., and Wießner, E. (2018). Microscopic traffic simulation using sumo. In 2018 21st international conference on intelligent transportation systems (ITSC), pages 2575–2582. IEEE.
Nazia, N., Butt, Z. A., Bedard, M. L., Tang, W.-C., Sehar, H., and Law, J. (2022). Methods used in the spatial and spatiotemporal analysis of covid-19 epidemiology: a systematic review. International Journal of Environmental Research and Public Health, 19(14):8267.
Nogueira, T. P., Celes, C., Martin, H., Loureiro, A. A., and Andrade, R. M. (2018). A statistical method for detecting move, stop, and noise: A case study with bus trajectories. Journal of Information and Data Management, 9(3):214–214.
Pappalardo, L., Simini, F., Barlacchi, G., and Pellungrini, R. (2022). scikit-mobility: A python library for the analysis, generation, and risk assessment of mobility data. Journal of Statistical Software, 103(4):1–38.
Sanches, A. d. J. A. M. (2019). Uma arquitetura e implementação do módulo de pré-processamento para biblioteca pymove.
Seidel, D. P., Dougherty, E. R., and Getz, W. M. (2019). Exploratory movement analysis and report building with r package stmove. bioRxiv, page 758987.
Spang, R., Pieper, K., Oesterle, B., Brauer, M., Haeger, C., Mümken, S., Gellert, P., and Voigt-Antons, J.-N. (2022a). Making sense of the noise: integrating multiple analyses for stop and trip classification. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48:435–441.
Spang, R., Pieper, K., Oesterle, B., Brauer, M., Haeger, C., Mümken, S., Gellert, P., and Voigt-Antons, J.-N. (2022b). The staga-dataset: Stop and trip annotated gps and accelerometer data of everyday life. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48:443–448.
Vargas-Munoz, J. E., Srivastava, S., Tuia, D., and Falcao, A. X. (2020). Openstreetmap: Challenges and opportunities in machine learning and remote sensing. IEEE Geoscience and Remote Sensing Magazine, 9(1):184–199.
Zheng, Y. (2015). Trajectory data mining: an overview. ACM Transactions on Intelligent Systems and Technology (TIST), 6(3):1–41.
Bráz, M. C. (2020). Implementação de algoritmos para análise de similaridade de trajetória na biblioteca pymove. Monografia. Universidade Federal do Ceará.
Custers, B., Kerkhof, M. V. D., Meulemans, W., Speckmann, B., and Staals, F. (2021). Maximum physically consistent trajectories. ACM Transactions on Spatial Algorithms and Systems, 7(4):1–33.
Deng, D., Leung, C. K., Zhao, C., Wen, Y., and Zheng, H. (2021). Spatial-temporal data science of covid-19 data. In 2021 IEEE 15th International Conference on Big Data Science and Engineering (BigDataSE), pages 7–14. IEEE.
Duarte, M. M. and Sakr, M. (2023). Outlier detection and cleaning in trajectories: A benchmark of existing tools. In EDBT/ICDT Workshops.
Ester, M., Kriegel, H.-P., Sander, J., Xu, X., et al. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In kdd, volume 96, pages 226–231.
Freitas, C. and Freitas, M. C. (2022). Package ‘argosfilter’.
Graser, A. and Dragaschnig, M. (2020). Exploring movement data in notebook environments. In IEEE VIS 2020 - MoVis.
Haidri, S., Haranwala, Y. J., Bogorny, V., Renso, C., da Fonseca, V. P., and Soares, A. (2021). Ptrail–a python package for parallel trajectory data preprocessing. arXiv:2108.13202.
Hariharan, R. and Toyama, K. (2004). Project lachesis: parsing and modeling location histories. In International Conference on Geographic Information Science, pages 106–124. Springer.
Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu,W., and Ma,W.-Y. (2008). Mining user similarity based on location history. In Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems, pages 1–10.
Lopez, P. A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., and Wießner, E. (2018). Microscopic traffic simulation using sumo. In 2018 21st international conference on intelligent transportation systems (ITSC), pages 2575–2582. IEEE.
Nazia, N., Butt, Z. A., Bedard, M. L., Tang, W.-C., Sehar, H., and Law, J. (2022). Methods used in the spatial and spatiotemporal analysis of covid-19 epidemiology: a systematic review. International Journal of Environmental Research and Public Health, 19(14):8267.
Nogueira, T. P., Celes, C., Martin, H., Loureiro, A. A., and Andrade, R. M. (2018). A statistical method for detecting move, stop, and noise: A case study with bus trajectories. Journal of Information and Data Management, 9(3):214–214.
Pappalardo, L., Simini, F., Barlacchi, G., and Pellungrini, R. (2022). scikit-mobility: A python library for the analysis, generation, and risk assessment of mobility data. Journal of Statistical Software, 103(4):1–38.
Sanches, A. d. J. A. M. (2019). Uma arquitetura e implementação do módulo de pré-processamento para biblioteca pymove.
Seidel, D. P., Dougherty, E. R., and Getz, W. M. (2019). Exploratory movement analysis and report building with r package stmove. bioRxiv, page 758987.
Spang, R., Pieper, K., Oesterle, B., Brauer, M., Haeger, C., Mümken, S., Gellert, P., and Voigt-Antons, J.-N. (2022a). Making sense of the noise: integrating multiple analyses for stop and trip classification. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48:435–441.
Spang, R., Pieper, K., Oesterle, B., Brauer, M., Haeger, C., Mümken, S., Gellert, P., and Voigt-Antons, J.-N. (2022b). The staga-dataset: Stop and trip annotated gps and accelerometer data of everyday life. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48:443–448.
Vargas-Munoz, J. E., Srivastava, S., Tuia, D., and Falcao, A. X. (2020). Openstreetmap: Challenges and opportunities in machine learning and remote sensing. IEEE Geoscience and Remote Sensing Magazine, 9(1):184–199.
Zheng, Y. (2015). Trajectory data mining: an overview. ACM Transactions on Intelligent Systems and Technology (TIST), 6(3):1–41.
Publicado
23/11/2023
Como Citar
OLIVEIRA, Edgar; CELES, Clayson; OLIVEIRA, Carina; BRAGA, Reinaldo.
Uma análise comparativa de técnicas de detecção de pontos de parada em ambientes urbanos. In: ESCOLA REGIONAL DE COMPUTAÇÃO DO CEARÁ, MARANHÃO E PIAUÍ (ERCEMAPI), 11. , 2023, Aracati/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
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p. 62-71.
DOI: https://doi.org/10.5753/ercemapi.2023.236434.
