CERVA: Roteamento Contextual para Veículos com Risco Espaço-temporal
Resumo
Nos dias atuais existe uma escassez de dados de mobilidade reais disponíveis abertamente. Sendo assim, diversos trabalhos da literatura geram mobilidade sintética a qual não representa a mobilidade real. Alguns desses trabalhos fazem o uso de dados contextuais para propor recomendação de rotas, no entanto não estudam o comportamento de tais dados. Além disso, o impacto de cada tipo de dado contextual muda de acordo com o perfil do usuário. Para resolver os problemas citados anteriormente é proposto o CERVA, uma solução de roteamento contextual para veículos com risco espaço-temporal. O CERVA é composto por três módulos, sendo: identificação das janelas contextuais, mapeamento de contexto, e personalização do roteamento. Os resultados da avaliação mostram que o CERVA recomenda as melhores rotas de acordo com o perfil do usuário.
Palavras-chave:
Mobilidade urbana, ciente do contexto, roteamento veicular
Referências
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Felson, M. and Boivin, R. (2015). Daily crime flows within a city. Crime Science, 4(1):31.
Galbrun, E., Pelechrinis, K., and Terzi, E. (2016). Urban navigation beyond shortest route: The case of safe paths. Information Systems, 57:160 – 171.
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Gunantara, N. (2018). A review of multi-objective optimization: Methods and its applications. Cogent Engineering, 5(1):1502242.
Jaimes, L. M. S., Ullah, K., and dos Santos Moreira, E. (2016). A secure commercial ads dissemination scheme for vehicular networks. In 2016 8th IEEE Latin-American Conference on Communications (LATINCOM), pages 1–6.
Keler, A. and Mazimpaka, J. D. (2016). Safety-aware routing for motorised tourists based on open data and vgi. Journal of Location Based Services, 10(1):64–77.
Kim, J., Cha, M., and Sandholm, T. (2014). Socroutes: Safe routes based on tweet sentiments. In Proceedings of the 23rd International Conference on World Wide Web, WWW ’14 Companion, pages 179–182, New York, NY, USA. ACM.
Ladeira, L. Z., de Souza, A. M., Pereira, G., Silva, Thiago Henrique, M. F. S., and Villas, L. (2019a). Martini: Towards a mobile and variable time window identification for spatio-temporal data. In IEEE Latin-American Conference on Communications, pages 1–10. IEEE.
Ladeira, L. Z., de Souza, A. M., Pereira, G., Silva, T. H., and Villas, L. (2019b). Serviço de sugestão de rotas seguras para veı́culos. In Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuı́dos, pages 608–621. SBC.
Ledl, T. (2004). Kernel density estimation: Theory and application in discriminant analysis. AUSTRIAN JOURNAL OF STATISTICS Volume Austrian Journal of Statistics, 33:267–279.
Liu, L., Chen, C., Qiu, T., Zhang, M., Li, S., and Zhou, B. (2018). A data dissemination scheme based on clustering and probabilistic broadcasting in vanets. Vehicular Communications, 13:78 – 88.
Malleson, N. and Andresen, M. A. (2015). Spatio-temporal crime hotspots and the ambient population. Crime Science, 4(1):10.
Newton, A. and Felson, M. (2015). Editorial: crime patterns in time and space: the dynamics of crime opportunities in urban areas. Crime Science, 4(1):11.
Rezaei, M., Noori, H., Rahbari, D., and Nickray, M. (2017). Refocus: A hybrid fog-cloud based intelligent traffic re-routing system. In 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI), pages 0992–0998.
Sahana, M., Hong, H., and Sajjad, H. (2018). Analyzing urban spatial patterns and trend of urban growth using urban sprawl matrix: A study on kolkata urban agglomeration, india. Science of The Total Environment, 628-629:1557 – 1566.
Santos, F. A., Akabane, A. T., Yokoyama, R. S., Loureiro, A. A. F., and Villas, L. A. (2016). A roadside unit-based localization scheme to improve positioning for vehicular networks. In 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall), pages 1–5.
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.
Secron, T. M., da Silva, E. R., de Farias, C. M., and Cruz, T. (2016). Sigaciente: Uma ferramenta para inferência do trânsito e de rotas seguras baseada em dados sociais. In ERSI’2016, pages 58–65.
Shah, S., Bao, F., Lu, C.-T., and Chen, I.-R. (2011). Crowdsafe: Crowd sourcing of crime incidents and safe routing on mobile devices. In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS ’11, pages 521–524, New York, NY, USA. ACM.
Souza, A., Braun, T., and Villas, L. (2018). Efficient context-aware vehicular traffic re-routing based on pareto-optimality: A safe-fast use case. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pages 2905–2910.
Souza, A. M. d., Braun, T., Botega, L. C., Cabral, R., Garcia, I. C., and Villas, L. A. (2019). Better safe than sorry: a vehicular traffic re-routing based on traffic conditions and public safety issues. Journal of Internet Services and Applications, 10.
Utamima, A. and Djunaidy, A. (2017). Be-safe travel, a web-based geographic application to explore safe-route in an area. AIP Conference Proceedings, 1867(1):020023.
Yu, T.-Y., Zhu, X., and Maheswaran, M. (2018). Vehicular Crowdsensing for Smart Cities, pages 175–204. Springer International Publishing, Cham.
Zheng, Y., Zhang, Y., and Li, L. (2016). Reliable path planning for bus networks considering travel time uncertainty. IEEE Intelligent Transportation Systems Magazine, 8(1):35–50.
Andresen, M. A. and Malleson, N. (2015). Intra-week spatial-temporal patterns of crime. Crime Science, 4(1):12.
Cunha, F., Maia, G., Celes, C., Santos, B., Rettore, P., Campolina, A., Guidoni, D., Souza, F., Ramos, H., Villas, L., Mini, R., and Loureiro, A. (2017). Sistemas de transporte inteligentes: Conceitos, aplicações e desafios.
Elsmore, S., Subastian, I. F., Salim, F. D., and Hamilton, M. (2014). Vdim: Vector-based diffusion and interpolation matrix for computing region-based crowdsourced ratings: Towards safe route selection for human navigation. In Proceedings of the 13th International Conference on Mobile and Ubiquitous Multimedia, MUM ’14, pages 212–215, New York, NY, USA. ACM.
Felson, M. and Boivin, R. (2015). Daily crime flows within a city. Crime Science, 4(1):31.
Galbrun, E., Pelechrinis, K., and Terzi, E. (2016). Urban navigation beyond shortest route: The case of safe paths. Information Systems, 57:160 – 171.
Ghorai, C. and Banerjee, I. (2017). A multi-objective data dissemination protocol for intelligent transportation systems. In 2017 IEEE 7th International Advance Computing Conference (IACC), pages 144–149.
Gunantara, N. (2018). A review of multi-objective optimization: Methods and its applications. Cogent Engineering, 5(1):1502242.
Jaimes, L. M. S., Ullah, K., and dos Santos Moreira, E. (2016). A secure commercial ads dissemination scheme for vehicular networks. In 2016 8th IEEE Latin-American Conference on Communications (LATINCOM), pages 1–6.
Keler, A. and Mazimpaka, J. D. (2016). Safety-aware routing for motorised tourists based on open data and vgi. Journal of Location Based Services, 10(1):64–77.
Kim, J., Cha, M., and Sandholm, T. (2014). Socroutes: Safe routes based on tweet sentiments. In Proceedings of the 23rd International Conference on World Wide Web, WWW ’14 Companion, pages 179–182, New York, NY, USA. ACM.
Ladeira, L. Z., de Souza, A. M., Pereira, G., Silva, Thiago Henrique, M. F. S., and Villas, L. (2019a). Martini: Towards a mobile and variable time window identification for spatio-temporal data. In IEEE Latin-American Conference on Communications, pages 1–10. IEEE.
Ladeira, L. Z., de Souza, A. M., Pereira, G., Silva, T. H., and Villas, L. (2019b). Serviço de sugestão de rotas seguras para veı́culos. In Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuı́dos, pages 608–621. SBC.
Ledl, T. (2004). Kernel density estimation: Theory and application in discriminant analysis. AUSTRIAN JOURNAL OF STATISTICS Volume Austrian Journal of Statistics, 33:267–279.
Liu, L., Chen, C., Qiu, T., Zhang, M., Li, S., and Zhou, B. (2018). A data dissemination scheme based on clustering and probabilistic broadcasting in vanets. Vehicular Communications, 13:78 – 88.
Malleson, N. and Andresen, M. A. (2015). Spatio-temporal crime hotspots and the ambient population. Crime Science, 4(1):10.
Newton, A. and Felson, M. (2015). Editorial: crime patterns in time and space: the dynamics of crime opportunities in urban areas. Crime Science, 4(1):11.
Rezaei, M., Noori, H., Rahbari, D., and Nickray, M. (2017). Refocus: A hybrid fog-cloud based intelligent traffic re-routing system. In 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI), pages 0992–0998.
Sahana, M., Hong, H., and Sajjad, H. (2018). Analyzing urban spatial patterns and trend of urban growth using urban sprawl matrix: A study on kolkata urban agglomeration, india. Science of The Total Environment, 628-629:1557 – 1566.
Santos, F. A., Akabane, A. T., Yokoyama, R. S., Loureiro, A. A. F., and Villas, L. A. (2016). A roadside unit-based localization scheme to improve positioning for vehicular networks. In 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall), pages 1–5.
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.
Secron, T. M., da Silva, E. R., de Farias, C. M., and Cruz, T. (2016). Sigaciente: Uma ferramenta para inferência do trânsito e de rotas seguras baseada em dados sociais. In ERSI’2016, pages 58–65.
Shah, S., Bao, F., Lu, C.-T., and Chen, I.-R. (2011). Crowdsafe: Crowd sourcing of crime incidents and safe routing on mobile devices. In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS ’11, pages 521–524, New York, NY, USA. ACM.
Souza, A., Braun, T., and Villas, L. (2018). Efficient context-aware vehicular traffic re-routing based on pareto-optimality: A safe-fast use case. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pages 2905–2910.
Souza, A. M. d., Braun, T., Botega, L. C., Cabral, R., Garcia, I. C., and Villas, L. A. (2019). Better safe than sorry: a vehicular traffic re-routing based on traffic conditions and public safety issues. Journal of Internet Services and Applications, 10.
Utamima, A. and Djunaidy, A. (2017). Be-safe travel, a web-based geographic application to explore safe-route in an area. AIP Conference Proceedings, 1867(1):020023.
Yu, T.-Y., Zhu, X., and Maheswaran, M. (2018). Vehicular Crowdsensing for Smart Cities, pages 175–204. Springer International Publishing, Cham.
Zheng, Y., Zhang, Y., and Li, L. (2016). Reliable path planning for bus networks considering travel time uncertainty. IEEE Intelligent Transportation Systems Magazine, 8(1):35–50.
Publicado
07/12/2020
Como Citar
LADEIRA, Lucas Zanco; DE SOUZA, Allan Mariano; SILVA, Thiago Henrique; ROCHA FILHO, Geraldo Pereira; MACIEL PEIXOTO, Maycon Leone; VILLAS, Leandro Aparecido.
CERVA: Roteamento Contextual para Veículos com Risco Espaço-temporal. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 38. , 2020, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 379-392.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2020.12296.
