A measure for computing the speed limit rate in a region

ilustraciones, diagramas

Autores:: Zea Gallego, Simon

Tipo de recurso:

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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dc.title.eng.fl_str_mv	A measure for computing the speed limit rate in a region
dc.title.translated.spa.fl_str_mv	Una medida para calcular la tasa de límite de velocidad en una región
title	A measure for computing the speed limit rate in a region
spellingShingle	A measure for computing the speed limit rate in a region 380 - Comercio , comunicaciones, transporte::388 - Transporte 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación Flujo de tráfico Transporte terrestre a alta velocidad Traffic flow High speed ground transportation Trajectories Moving objects Speed Speed limit rate Congestion Trayectorias Objetos en movimiento Velocidad Tasa de límite de velocidad Congestión
title_short	A measure for computing the speed limit rate in a region
title_full	A measure for computing the speed limit rate in a region
title_fullStr	A measure for computing the speed limit rate in a region
title_full_unstemmed	A measure for computing the speed limit rate in a region
title_sort	A measure for computing the speed limit rate in a region
dc.creator.fl_str_mv	Zea Gallego, Simon
dc.contributor.advisor.none.fl_str_mv	Moreno Arboleda, Francisco Javier
dc.contributor.author.none.fl_str_mv	Zea Gallego, Simon
dc.contributor.orcid.spa.fl_str_mv	Moreno Arboleda, Francisco Javier [0000-0001-7806-6278]
dc.subject.ddc.spa.fl_str_mv	380 - Comercio , comunicaciones, transporte::388 - Transporte 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación
topic	380 - Comercio , comunicaciones, transporte::388 - Transporte 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación Flujo de tráfico Transporte terrestre a alta velocidad Traffic flow High speed ground transportation Trajectories Moving objects Speed Speed limit rate Congestion Trayectorias Objetos en movimiento Velocidad Tasa de límite de velocidad Congestión
dc.subject.lemb.spa.fl_str_mv	Flujo de tráfico Transporte terrestre a alta velocidad
dc.subject.lemb.eng.fl_str_mv	Traffic flow High speed ground transportation
dc.subject.proposal.eng.fl_str_mv	Trajectories Moving objects Speed Speed limit rate Congestion
dc.subject.proposal.spa.fl_str_mv	Trayectorias Objetos en movimiento Velocidad Tasa de límite de velocidad Congestión
description	ilustraciones, diagramas
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-11-10T13:40:37Z
dc.date.available.none.fl_str_mv	2022-11-10T13:40:37Z
dc.date.issued.none.fl_str_mv	2022-11-08
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
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identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.indexed.spa.fl_str_mv	RedCol LaReferencia
dc.relation.references.spa.fl_str_mv	A. Raffaetà, L. L. (2011). visual mobility analysis using t-warehouse. International Journal of Data Warehousing and Mining Volume 7 Issue 1, 1-23. Abish Malik, R. M. (2010). Visual Analytics Law Enforcement Toolkit. IEEE International Conference on Technologies for Homeland Security (HST), 222–228. Afrin, T., & Yodo, N. (2020). A survey of road traffic congestion measures towards a sustainable and resilient transportation system. Sustainability, 12(11), 4660. Allen, J. F. (1983). Maintaining knowledge about temporal intervals. Communications of the ACM, 26(11), 832-843. Alsahfi, T., Almotairi, M., & Elmasri, R. (2020). A survey on trajectory data warehouse. Spatial Information Research, 28(1), 53-66. Andrienko, N., & Andrienko, G. (2013). Visual analytics of movement: An overview of methods, tools and procedures. Information visualization, 12(1), 3-24. Anna Fredrikson, C. N. (1999). Temporal, Geographical and Categorical Aggregations Viewed through Coordinated Displays: A Case Study with Highway Incident Data. NPIVM '99 Proceedings of the 1999 workshop on new paradigms in information visualization and manipulation in conjunction with the eighth ACM internation conference on Information and knowledge management, 26-34 . Beliakov, G., Gagolewski, M., James, S., Pace, S., Pastorello, N., Thilliez, E., & Vasa, R. (2018). Measuring traffic congestion: an approach based on learning weighted inequality, spread and aggregation indices from comparison data. Applied Soft Computing, 67, 910- 919. Chen, W., Guo, F., & Wang, F. Y. (2015). A survey of traffic data visualization. IEEE Transactions on Intelligent Transportation Systems, 16(6), 2970-2984. Dodge, S., Weibel, R., & Lautenschütz, A. K. (2008). Towards a taxonomy of movement patterns. Information visualization, 7(3-4), 240-252. Felice, P., Clementini, E. (2009). Topological Relationships. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0- 387-39940-9_432. Gonzalez-Calderon, C. A., Posada Henao, J. J., & SÁNCHEZ-DÍAZ, I. D. (2012). The need for congestion pricing in medellin: an economic perspective. Dyna, 79(171), 123-131. Güting, R. H., & Schneider, M. (2005). Moving objects databases. Elsevier. He, F., Yan, X., Liu, Y., & Ma, L. (2016). A traffic congestion assessment method for urban road networks based on speed performance index. Procedia engineering, 137, 425-433. Joseph M. Hellerstein, P. J. (1997). Online Aggregation. SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data, 171-182. Kohan, M., & Ale, J. M. (2020). Discovering traffic congestion through traffic flow patterns generated by moving object trajectories. Computers, Environment and Urban Systems, 80, 101426. Leonardi, L., Orlando, S., Raffaetà, A., Roncato, A., Silvestri, C., Andrienko, G., & Andrienko, N. (2014). A general framework for trajectory data warehousing and visual OLAP. GeoInformatica, 18(2), 273-312. Magdy, N., Sakr, M. A., & El-Bahnasy, K. (2017). A generic trajectory similarity operator in moving object databases. Egyptian Informatics Journal, 18(1), 29-37. Malinowski, E.; Zimanyi, E. (2008). Advanced Data Warehouse Design—From Conventional to Spatial and Temporal Applications; Data-Centric Systems and Applications; Springer: Berlin, Germany; doi:10.1007/978-3-540-74405-4. Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., & Damas, L. (2013). Predicting taxi–passenger demand using streaming data. IEEE Transactions on Intelligent Transportation Systems, 14(3), 1393-1402. Mockus, A. (1998). Navigating Aggregation Spaces. Proc. IEEE Conference on Information Visualization. Natalia Adrienko, G. A. (2010). Spatial generalisation and aggregation of massive movement data. IEEE Transactions on Visualization and Computer Graphics ( Volume: 17, Issue: 2 Feb. 2011), 205–219. Natalia Andrienko, G. A. (2013). Visual analytics of movement: An overview of methods, tools and procedures. Information Visualization Volume 12 Issue 1, January 2013 , 3–24. Ossama, O., Mokhtar, H. M., & El-Sharkawi, M. E. (2011). An extended k-means technique for clustering moving objects. Egyptian Informatics Journal, 12(1), 45-51. Raffaetà, A., Leonardi, L., Marketos, G., Andrienko, G., Andrienko, N., Frentzos, E., ... & Silvestri, C. (2013). Visual mobility analysis using T-warehouse. In Developments in Data Extraction, Management, and Analysis (pp. 1-22). IGI Global. Ranacher, P., & Tzavella, K. (2014). How to compare movement? A review of physical movement similarity measures in geographic information science and beyond. Cartography and geographic information science, 41(3), 286-307. Rao, A. M., & Rao, K. R. (2012). Measuring urban traffic congestion-a review. International Journal for Traffic & Transport Engineering, 2(4). Robert Krueger, D. T. (2014). Visual Analysis of Movement Behavior using Web Data. Visualization Symposium (PacificVis), 2014 IEEE Pacific, 193- 200. Salau, T. A. O., Adeyefa, A. O., & Oke, S. A. (2004). Vehicle speed control using road bumps. Transport, 19(3), 130-136. Schönig, H. J. (2018). Mastering PostgreSQL 11: Expert techniques to build scalable, reliable, and fault-tolerant database applications. Packt Publishing Ltd. TIBCO Software inc. (2017, 03 20). Spotfire. Retrieved from Spotfire: https://spotfire.tibco.com/. Vlahogianni, E. I., Karlaftis, M. G., & Golias, J. C. (2014). Short-term traffic forecasting: Where we are and where we’re going.Transportation Research Part C: Emerging Technologies, 43, 3-19. Yanagisawa, Y., & Satoh, T. (2006, April). Clustering multidimensional trajectories based on shape and velocity. In 22nd International Conference on Data Engineering Workshops (ICDEW'06) (pp. 12-12). IEEE.
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dc.format.extent.spa.fl_str_mv	68 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Medellín - Minas - Maestría en Ingeniería - Analítica
dc.publisher.faculty.spa.fl_str_mv	Facultad de Minas
dc.publisher.place.spa.fl_str_mv	Medellín, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Medellín
institution	Universidad Nacional de Colombia
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spelling	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Moreno Arboleda, Francisco Javierd1fe92c3ac167b7f99958ab50ff471cc600Zea Gallego, Simon2fdb1139b73e49b0557f8615085c0316Moreno Arboleda, Francisco Javier [0000-0001-7806-6278]2022-11-10T13:40:37Z2022-11-10T13:40:37Z2022-11-08https://repositorio.unal.edu.co/handle/unal/82679Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasIn this thesis, we propose a measure that, based on the trajectories of moving objects, determines the speed limit rate, given a speed limit, in each of the cells in which a region is segmented (the space where the objects move). To do this, we formally define the concept of speed limit rate, which is based on speed segments. The time is also segmented into intervals. In this way, we can analyze the movement of objects in a cell in each time interval. We implemented the corresponding algorithm and conducted experiments with trajectories of taxis in Porto (Portugal). Our results showed that our speed limit rate measure can be helpful for analyzing the behavior of moving objects regarding their speed. Our measure also might serve as a rough estimate for congestion in a (sub)region. This could be useful for traffic analysis including prediction techniquesEsta tesis propone una medida que, a partir de las trayectorias de objetos móviles, determina la tasa límite de velocidad dado un límite de velocidad en cada una de las celdas en las que se segmenta una región (el espacio donde se mueven los objetos). Para ello, se define formalmente el concepto de tasa de límite de velocidad, basada en segmentos de velocidad. El tiempo también se segmenta en intervalos. Por tanto, Se puede analizar el movimiento de los objetos en una celda en un intervalo de tiempo determinado. Para ello, se implementó el algoritmo correspondiente y se hicieron experimentos con trayectorias de taxis en Oporto (Portugal). Los resultados mostraron que la medida de tasa de límite de velocidad puede ser útil para analizar el comportamiento de los objetos móviles con respecto a su velocidad. Además, la medida también podría servir como una estimación aproximada de la congestión en una (sub)región, siendo útil para el análisis del tráfico, incluidas las técnicas de predicción. (Texto tomado de la fuente)MaestríaMagíster en Ingeniería - AnalíticaBases de datosÁrea Curricular de Ingeniería de Sistemas e Informática68 páginasapplication/pdfengUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - AnalíticaFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín380 - Comercio , comunicaciones, transporte::388 - Transporte000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computaciónFlujo de tráficoTransporte terrestre a alta velocidadTraffic flowHigh speed ground transportationTrajectoriesMoving objectsSpeedSpeed limit rateCongestionTrayectoriasObjetos en movimientoVelocidadTasa de límite de velocidadCongestiónA measure for computing the speed limit rate in a regionUna medida para calcular la tasa de límite de velocidad en una regiónTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMRedColLaReferenciaA. Raffaetà, L. L. (2011). visual mobility analysis using t-warehouse. International Journal of Data Warehousing and Mining Volume 7 Issue 1, 1-23.Abish Malik, R. M. (2010). Visual Analytics Law Enforcement Toolkit. IEEE International Conference on Technologies for Homeland Security (HST), 222–228.Afrin, T., & Yodo, N. (2020). A survey of road traffic congestion measures towards a sustainable and resilient transportation system. Sustainability, 12(11), 4660.Allen, J. F. (1983). Maintaining knowledge about temporal intervals. Communications of the ACM, 26(11), 832-843.Alsahfi, T., Almotairi, M., & Elmasri, R. (2020). A survey on trajectory data warehouse. Spatial Information Research, 28(1), 53-66.Andrienko, N., & Andrienko, G. (2013). Visual analytics of movement: An overview of methods, tools and procedures. Information visualization, 12(1), 3-24.Anna Fredrikson, C. N. (1999). Temporal, Geographical and Categorical Aggregations Viewed through Coordinated Displays: A Case Study with Highway Incident Data. NPIVM '99 Proceedings of the 1999 workshop on new paradigms in information visualization and manipulation in conjunction with the eighth ACM internation conference on Information and knowledge management, 26-34 .Beliakov, G., Gagolewski, M., James, S., Pace, S., Pastorello, N., Thilliez, E., & Vasa, R. (2018). Measuring traffic congestion: an approach based on learning weighted inequality, spread and aggregation indices from comparison data. Applied Soft Computing, 67, 910- 919.Chen, W., Guo, F., & Wang, F. Y. (2015). A survey of traffic data visualization. IEEE Transactions on Intelligent Transportation Systems, 16(6), 2970-2984.Dodge, S., Weibel, R., & Lautenschütz, A. K. (2008). Towards a taxonomy of movement patterns. Information visualization, 7(3-4), 240-252.Felice, P., Clementini, E. (2009). Topological Relationships. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0- 387-39940-9_432.Gonzalez-Calderon, C. A., Posada Henao, J. J., & SÁNCHEZ-DÍAZ, I. D. (2012). The need for congestion pricing in medellin: an economic perspective. Dyna, 79(171), 123-131.Güting, R. H., & Schneider, M. (2005). Moving objects databases. Elsevier.He, F., Yan, X., Liu, Y., & Ma, L. (2016). A traffic congestion assessment method for urban road networks based on speed performance index. Procedia engineering, 137, 425-433.Joseph M. Hellerstein, P. J. (1997). Online Aggregation. SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data, 171-182.Kohan, M., & Ale, J. M. (2020). Discovering traffic congestion through traffic flow patterns generated by moving object trajectories. Computers, Environment and Urban Systems, 80, 101426.Leonardi, L., Orlando, S., Raffaetà, A., Roncato, A., Silvestri, C., Andrienko, G., & Andrienko, N. (2014). A general framework for trajectory data warehousing and visual OLAP. GeoInformatica, 18(2), 273-312.Magdy, N., Sakr, M. A., & El-Bahnasy, K. (2017). A generic trajectory similarity operator in moving object databases. Egyptian Informatics Journal, 18(1), 29-37.Malinowski, E.; Zimanyi, E. (2008). Advanced Data Warehouse Design—From Conventional to Spatial and Temporal Applications; Data-Centric Systems and Applications; Springer: Berlin, Germany; doi:10.1007/978-3-540-74405-4.Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., & Damas, L. (2013). Predicting taxi–passenger demand using streaming data. IEEE Transactions on Intelligent Transportation Systems, 14(3), 1393-1402.Mockus, A. (1998). Navigating Aggregation Spaces. Proc. IEEE Conference on Information Visualization.Natalia Adrienko, G. A. (2010). Spatial generalisation and aggregation of massive movement data. IEEE Transactions on Visualization and Computer Graphics ( Volume: 17, Issue: 2 Feb. 2011), 205–219.Natalia Andrienko, G. A. (2013). Visual analytics of movement: An overview of methods, tools and procedures. Information Visualization Volume 12 Issue 1, January 2013 , 3–24.Ossama, O., Mokhtar, H. M., & El-Sharkawi, M. E. (2011). An extended k-means technique for clustering moving objects. Egyptian Informatics Journal, 12(1), 45-51.Raffaetà, A., Leonardi, L., Marketos, G., Andrienko, G., Andrienko, N., Frentzos, E., ... & Silvestri, C. (2013). Visual mobility analysis using T-warehouse. In Developments in Data Extraction, Management, and Analysis (pp. 1-22). IGI Global.Ranacher, P., & Tzavella, K. (2014). How to compare movement? A review of physical movement similarity measures in geographic information science and beyond. Cartography and geographic information science, 41(3), 286-307.Rao, A. M., & Rao, K. R. (2012). Measuring urban traffic congestion-a review. International Journal for Traffic & Transport Engineering, 2(4).Robert Krueger, D. T. (2014). Visual Analysis of Movement Behavior using Web Data. Visualization Symposium (PacificVis), 2014 IEEE Pacific, 193- 200.Salau, T. A. O., Adeyefa, A. O., & Oke, S. A. (2004). Vehicle speed control using road bumps. Transport, 19(3), 130-136.Schönig, H. J. (2018). Mastering PostgreSQL 11: Expert techniques to build scalable, reliable, and fault-tolerant database applications. Packt Publishing Ltd.TIBCO Software inc. (2017, 03 20). Spotfire. Retrieved from Spotfire: https://spotfire.tibco.com/.Vlahogianni, E. I., Karlaftis, M. G., & Golias, J. C. (2014). Short-term traffic forecasting: Where we are and where we’re going.Transportation Research Part C: Emerging Technologies, 43, 3-19.Yanagisawa, Y., & Satoh, T. (2006, April). Clustering multidimensional trajectories based on shape and velocity. In 22nd International Conference on Data Engineering Workshops (ICDEW'06) (pp. 12-12). IEEE.EstudiantesInvestigadoresMaestrosPúblico generalORIGINAL1037639269.2022.pdf1037639269.2022.pdfTesis de Maestría en Ingeniería - Analíticaapplication/pdf7189859https://repositorio.unal.edu.co/bitstream/unal/82679/3/1037639269.2022.pdf4301585a3c5f02d73631eb07b6b90645MD53LICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/82679/4/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD54THUMBNAIL1037639269.2022.pdf.jpg1037639269.2022.pdf.jpgGenerated Thumbnailimage/jpeg4498https://repositorio.unal.edu.co/bitstream/unal/82679/5/1037639269.2022.pdf.jpg4403cca7f2dab22bde4cbe9e73a83840MD55unal/82679oai:repositorio.unal.edu.co:unal/826792023-08-10 23:04:23.568Repositorio Institucional Universidad Nacional de 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A measure for computing the speed limit rate in a region

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