Extracción y análisis de información de accidentes de tránsito desde redes sociales

ilustraciones, gráficas, mapas, tablas

Autores:: Suat Rojas, Nestor Eduardo

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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dc.title.spa.fl_str_mv	Extracción y análisis de información de accidentes de tránsito desde redes sociales
dc.title.translated.eng.fl_str_mv	Extraction and analysis of traffic accident data from social networks
title	Extracción y análisis de información de accidentes de tránsito desde redes sociales
spellingShingle	Extracción y análisis de información de accidentes de tránsito desde redes sociales 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Sistemas de transporte inteligente Redes sociales Accidente de tránsito Sensores sociales Procesamiento de lenguaje natural Aprendizaje automático Minería de texto Reconocimiento de entidades nombradas Twitter intelligent transportation system Social media Traffic accident Social sensors Natural language processing Machine learning Text mining Named entity recognition Twitter Análisis de datos Seguridad del transporte Data analysis Transport safety
title_short	Extracción y análisis de información de accidentes de tránsito desde redes sociales
title_full	Extracción y análisis de información de accidentes de tránsito desde redes sociales
title_fullStr	Extracción y análisis de información de accidentes de tránsito desde redes sociales
title_full_unstemmed	Extracción y análisis de información de accidentes de tránsito desde redes sociales
title_sort	Extracción y análisis de información de accidentes de tránsito desde redes sociales
dc.creator.fl_str_mv	Suat Rojas, Nestor Eduardo
dc.contributor.advisor.none.fl_str_mv	Pedraza Bonilla, Cesar Augusto Gutierrez Osorio, Camilo
dc.contributor.author.none.fl_str_mv	Suat Rojas, Nestor Eduardo
dc.contributor.referee.none.fl_str_mv	Suárez Páez, Julio Ernesto
dc.contributor.researchgroup.spa.fl_str_mv	Plas Programming languages And Systems
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
topic	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Sistemas de transporte inteligente Redes sociales Accidente de tránsito Sensores sociales Procesamiento de lenguaje natural Aprendizaje automático Minería de texto Reconocimiento de entidades nombradas Twitter intelligent transportation system Social media Traffic accident Social sensors Natural language processing Machine learning Text mining Named entity recognition Twitter Análisis de datos Seguridad del transporte Data analysis Transport safety
dc.subject.proposal.spa.fl_str_mv	Sistemas de transporte inteligente Redes sociales Accidente de tránsito Sensores sociales Procesamiento de lenguaje natural Aprendizaje automático Minería de texto Reconocimiento de entidades nombradas Twitter
dc.subject.proposal.eng.fl_str_mv	intelligent transportation system Social media Traffic accident Social sensors Natural language processing Machine learning Text mining Named entity recognition Twitter
dc.subject.unesco.spa.fl_str_mv	Análisis de datos Seguridad del transporte
dc.subject.unesco.eng.fl_str_mv	Data analysis Transport safety
description	ilustraciones, gráficas, mapas, tablas
publishDate	2021
dc.date.issued.none.fl_str_mv	2021
dc.date.accessioned.none.fl_str_mv	2022-02-10T14:06:14Z
dc.date.available.none.fl_str_mv	2022-02-10T14:06:14Z
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
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dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/80927 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	G. Cookson and B. Pishue, INRIX Global Traffic Scorecard. No. February, 2018. “Víctimas fallecidas y lesionadas valoradas por inmlcf. nacionales. agencia nacional de seguridad vial,” 2017. S. Wang, L. He, L. Stenneth, P. S. Yu, and Z. Li, “Citywide traffic congestion estimation with social media,” in Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS ’15, pp. 1–10, 2015. X. Fan, B. He, C. Wang, J. Li, M. Cheng, H. Huang, and X. Liu, “Big Data Analytics and Visualization with Spatio-Temporal Correlations for Traffic Accidents,” in 15th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2015), vol. 9531, (Zhangjiajie, China), pp. 255–268, 2015. M. B. Subaweh and E. P. Wibowo, “Implementation of Pixel Based Adaptive Segmenter method for tracking and counting vehicles in visual surveillance,” in 2016 International Conference on Informatics and Computing, ICIC 2016, no. Icic, pp. 1–5, 2016. L. Li, J. Zhang, Y. Zheng, and B. Ran, “Real-Time Traffic Incident Detection with Classification Methods,” in Green Intelligent Transportation Systems, Lecture Notes in Electrical Engineering, vol. 419, pp. 777–788, 2018. S. Zhang, G. Wu, J. P. Costeira, and J. M. Moura, “FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras,” in Proceedings of the IEEE International Conference on Computer Vision, vol. 2017-Octob, pp. 3687–3696, 2017. N. Krausz, T. Lovas, and A. Barsi, “Radio frequency identification in supporting traffic safety,” Periodica Polytechnica Civil Engineering, vol. 61, no. 4, pp. 727–731, 2017. M. Chaturvedi and S. Srivastava, “Edge-level real-time traffic estimation with limited infrastructure,” 2015 IEEE 82nd Vehicular Technology Conference, VTC Fall 2015 - Proceedings, 2016. W. Zuo, C. Guo, J. Liu, X. Peng, and M. Yang, “A police and insurance joint management system based on high precision BDS/GPS positioning,” Sensors (Switzerland), vol. 18, no. 1, 2018. J. Aslam, S. Lim, X. Pan, and D. Rus, “City-scale traffic estimation from a roving sensor network,” in SenSys 2012 - Proceedings of the 10th ACM Conference on Embedded Networked Sensor Systems, pp. 141–154, Association for Computing Machinery, 2012. S. Wang, X. Zhang, J. Cao, L. He, L. Stenneth, P. S. Yu, Z. Li, and Z. Huang, “Computing Urban Traffic Congestions by Incorporating Sparse GPS Probe Data and Social Media Data,” ACM Transactions on Information Systems, vol. 35, no. 4, pp. 1–30, 2017. T. Kuflik, E. Minkov, S. Nocera, S. Grant-Muller, A. Gal-Tzur, and I. Shoor, “Automating a framework to extract and analyse transport related social media content: The potential and the challenges,” Transportation Research Part C: Emerging Technologies, vol. 77, pp. 275–291, 2017. Y. Gu, Z. S. Qian, and F. Chen, “From Twitter to detector: Real-time traffic incident detection using social media data,” Transportation Research Part C: Emerging Technologies, vol. 67, pp. 321–342, 2016. Z. Zhang, Q. He, J. Gao, and M. Ni, “A deep learning approach for detecting traffic accidents from social media data,” Transportation Research Part C: Emerging Technologies, vol. 86, no. November 2017, pp. 580–596, 2018. B. Arias, G. Orellana, M. Orellana, and M.-I. Acosta, A Text Mining Approach to Discover Real-Time Transit Events from Twitter, vol. 884. Springer International Publishing, 2019. K. Ikeda, T. Sakaki, F. Toriumi, and S. Kurihara, “An examination of a novel information diffusion model: Considering of twitter user and Twitter system features,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10002 LNAI, pp. 180–191, 2016. D. A. Kurniawan, S. Wibirama, and N. A. Setiawan, “Real-time traffic classification with Twitter data mining,” in 2016 8th International Conference on Information Technology and Electrical Engineering (ICITEE), pp. 1–5, 2016. A. Salas, P. Georgakis, C. Nwagboso, A. Ammari, and I. Petalas, “Traffic Event Detection Framework Using Social Media,” in IEEE International Conference on Smart Grid and Smart Cities, no. July, p. 5, 2017. A. Salas, P. Georgakis, and Y. Petalas, “Incident Detection Using Data from Social Media,” 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 751–755, 2017. K.-H. Chao and P.-Y. Chen, “An Intelligent Traffic Flow Control System Based on Radio Frequency Identification and Wireless Sensor Networks,” International Journal of Distributed Sensor Networks, vol. 10, no. 5, p. 694545, 2014. R. Ke, Z. Li, S. Kim, J. Ash, Z. Cui, and Y. Wang, “Real-Time Bidirectional Traffic Flow Parameter Estimation from Aerial Videos,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 4, pp. 890–901, 2017. H.-c. Kwak and S. Kho, “Predicting crash risk and identifying crash precursors on Korean expressways using loop detector data,” Accident Analysis and Prevention, vol. 88, pp. 9–19, 2016. H. M. Sherif, M. Shedid, and S. A. Senbel, “Real time traffic accident detection system using wireless sensor network,” in 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR), pp. 59–64, 2014. N. Ya, A. E. Azhar, A. L. Yusof, S. S. Sarnin, and D. M. Ali, “Real Time Wireless Accident Tracker using Mobile Phone,” no. October, pp. 2–3, 2017. Z. Li, S. K. Cha, C. Wan, B. Cui, N. Zhang, and J. Xu, “Detecting Anomaly in Traffic Flow from Road Similarity Analysis,” in 17th International Conference, WAIM 2016, Proceedings, Part II (B. Cui, N. Zhang, J. Xu, X. Lian, and D. Liu, eds.), vol. 9659 of Lecture Notes in Computer Science, (Cham), pp. V–VI, Springer International Publishing, 2016. A. B. Nikolaev, Y. S. Sapego, A. M. Ivakhnenko, E. Mel, and V. Y. Stroganov, “Analysis of the Incident Detection Technologies and Algorithms in Intelligent Transport Systems,” vol. 12, no. 15, pp. 4765–4774, 2017. I. Moncada, “Análisis espacio-temporal de los accidentes de tránsito en Bogotá utilizando patrones puntuales,” tech. rep., Universidad Nacional de Colombia, Bogotá, 2018. J. Pereira, A. Pasquali, P. Saleiro, and R. Rossetti, “Transportation in Social Media: An Automatic Classifier for Travel-Related Tweets,” in 16th Portuguese Conference on Artificial Intelligence, EPIA 2013, vol. 8154, pp. 355–366, 2017. Y. G. Petalas, A. Ammari, P. Georgakis, and C. Nwagboso, “A Big Data Architecture for Traffic Forecasting Using Multi-Source Information,” in ALGOCLOUD 2016, Springer International Publishing AG, vol. 10230, pp. 65–83, 2017. N. G. Polson and V. O. Sokolov, “Deep learning for short-term traffic flow prediction,” Transportation Research Part C: Emerging Technologies, vol. 79, pp. 1–17, 2017. Q. Chen, X. Song, H. Yamada, and R. Shibasaki, “Learning Deep Representation from Big and Heterogeneous Data for Traffic Accident Inference,” in Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16), pp. 338–344, 2016. B. Caimmi, S. Vallejos, L. Berdun, ̈I. Soria, A. Amandi, and M. Campo, “Detección de incidentes de tránsito en Twitter,” in 2016 IEEE Biennial Congress of Argentina, ARGENCON 2016, pp. 1–6, 2016. H. Nguyen, W. Liu, P. Rivera, and F. Chen, “TrafficWatch: Real-Time Traffic Incident Detection and Monitoring Using Social Media Hoang,” in PAKDD: Pacific-Asia Conference on Knowledge Discovery and Data Mining (J. Bailey, L. Khan, T. Washio, G. Dobbie, J. Z. Huang, and R. Wang, eds.), vol. 9651 of Lecture Notes in Computer Science, (Cham), pp. 540–551, Springer International Publishing, 2016. A. Schulz, P. Ristoski, and H. Paulheim, “I see a car crash: Real-time detection of small scale incidents in microblogs,” in The Semantic Web: ESWC 2013 Satellite Events. ESWC 2013. Lecture Notes in Computer Science, vol. 7955 LNCS, pp. 22–33, 2013. C. Gutiérrez, P. Figueiras, P. Oliveira, R. Costa, and R. Jardim-goncalves, “An Approach for Detecting Traffic Events Using Social Media,” in Emerging Trends and Advanced Technologies for Computational Intelligence, vol. 647, ch. An Approac, 2016. P. Anantharam, P. Barnaghi, K. Thirunarayan, and A. Sheth, “Extracting City Traffic Events from Social Streams,” ACM Transactions on Intelligent Systems and Technology, vol. 6, no. 4, pp. 1–27, 2015. Y. Chen, Y. Lv, X. Wang, and F. Y. Wang, “A convolutional neural network for traffic information sensing from social media text,” in IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, vol. 2018-March, pp. 1–6, 2017. R. Peres, D. Esteves, and G. Maheshwari, “Bidirectional LSTM with a Context Input Window for Named Entity Recognition in Tweets,” in In Proceedings ofK-CAP 2017: Knowledge Capture Conference (K- CAP 2017)., pp. 1–4, Association for Computing Machinery, 2017. G. Aguilar, A. P. López Monroy, F. González, and T. Solorio, “Modeling Noisiness to Recognize Named Entities using Multitask Neural Networks on Social Media,” in Proceedings of NAACL-HLT 2018 Association for Computational Linguistics, vol. 1, pp. 1401–1412, Association for Computational Linguistics, 2018. J. Gelernter and S. Balaji, “An algorithm for local geoparsing of microtext,” GeoInformatica, vol. 17, no. 4, pp. 635–667, 2013. A. Ritter, S. Clark, Mausam, and O. Etzioni, “Named entity recognition in tweets: an experimental study,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1524–1534, Association for Computational Linguistics, 2011. S. Malmasi and M. Dras, “Location Mention Detection in Tweets and Microblogs,” in PACLING 2015, CCIS, pp. 123–134, Oxford University Press, may 2016. J. Gelernter and W. Zhang, “Cross-lingual geo-parsing for non-structured data,” in Proceedings of the 7th Workshop on Geographic Information Retrieval, pp. 64–71, 2013. M. Sagcan and P. Karagoz, “Toponym Recognition in Social Media for Estimating the Location of Events,” in 2015 IEEE International Conference on Data Mining Workshop (ICDMW), pp. 33–39, 2015. Q. Le and T. Mikolov, “Distributed Representations of Sentences and Documents,” in Proceedings of the 31st International Conference on Machine Learning, vol. 32, 2014. E. Okur, H. Demir, and A. Ozg ̈ur, “Named entity recognition on twitter for Turkish using semi-supervised learning with word embeddings,” Proceedings of the 10th Inter- national Conference on Language Resources and Evaluation, LREC 2016, pp. 549–555, 2016. E. Okur, H. Demir, and A. Özgür, “Named entity recognition on twitter for Turkish using semi-supervised learning with word embeddings,” Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016, pp. 549–555, 2016. T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings, pp. 1–12, 2013. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, no. Mlm, 2019. J. Cañete, G. Chaperon, R. Fuentes, and J. Pérez, “Spanish Pre-Trained BERT Model and Evaluation Data,” PML4DC at ICLR 2020, pp. 1–10, 2020. P. Norvig, “Natural Language Corpus Data,” in Beautiful Data: The Stories Behind Elegant Data Solutions, pp. 219–242, 0’Reilly, 2009. M. Honnibal and M. Johnson, “An improved non-monotonic transition system for dependency parsing,” Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing, no. September, pp. 1373–1378, 2015. N. Taufik, A. F. Wicaksono, and M. Adriani, “Named entity recognition on Indonesian microblog messages,” Proceedings of the 2016 International Conference on Asian Language Processing, IALP 2016, pp. 358–361, 2017. A. García-Pablos, N. Perez, and M. Cuadros, “Sensitive Data Detection and Classification in Spanish Clinical Text: Experiments with BERT,” 2019.
dc.rights.spa.fl_str_mv	Derechos reservados al autor, 2021
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional Derechos reservados al autor, 2021 http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.extent.spa.fl_str_mv	xi, 60 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación
dc.publisher.department.spa.fl_str_mv	Departamento de Ingeniería de Sistemas e Industrial
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial-SinDerivadas 4.0 InternacionalDerechos reservados al autor, 2021http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Pedraza Bonilla, Cesar Augustoc9f3a45785520d570c3ce7b608546d43Gutierrez Osorio, Camiloa21a56f44544128fd2bb88e232cf5f43600Suat Rojas, Nestor Eduardofa39bff9ea835c6d06b4150b240dceaf600Suárez Páez, Julio ErnestoPlas Programming languages And Systems2022-02-10T14:06:14Z2022-02-10T14:06:14Z2021https://repositorio.unal.edu.co/handle/unal/80927Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, mapas, tablasLa detección de accidentes de tránsito es una estrategia importante para que los gobiernos implementen políticas que reduzcan este fenómeno. Usualmente usan técnicas como procesamiento de imágenes, dispositivos RFID, y otras. La detección en redes sociales ha surgido como una alternativa de bajo costo. Sin embargo las redes sociales presentan varios retos y desafíos como uso de lenguaje informal y falta de ortografía. Este trabajo propone un método para extraer y analizar los datos de accidentes de tránsito desde Twitter. Cuatro fases componen el método. La primera fase establece los mecanismos para obtener datos. El segundo consiste en representar vectorialmente los mensajes y clasificarlos como accidentes de tránsito o no. La tercera usa técnicas de reconocimiento de entidades nombradas para la detección de ubicaciones. En la cuarta estas ubicaciones pasan por un geocoder que devuelve sus coordenadas geográficas. Aplicamos este método para la ciudad de Bogotá y comparamos los datos de Twitter con la fuente oficial de tránsito, las comparaciones muestran una influencia en Twitter sobre la zona comercial e industrial de la ciudad. Los resultados revelan la efectividad de los accidentes reportados en Twitter como información adicional y su uso debe considerarse como fuentes complementarias a los métodos de detección existentes. (Texto tomado de la fuente)The detection of traffic accidents is an important strategy for governments to implement policies that reduce this phenomenon. They usually use techniques like image processing, RFID devices, and others. Social media detection has emerged as a low-cost alternative. However, social media presents several challenges such as use of non-formal language and misspelling. This work proposes a method to extract and analyze traffic accident data from Twitter. The method is composed of four phases. The first phase establishes the mechanisms for obtaining data. The second consists of representing the messages in vectors and classif- ying them as traffic accidents or not. The third uses named entity recognition techniques for location detection. In the fourth, these locations go through a geocoder that returns their geographic coordinates. We apply this method for the city of Bogota and compare the data on Twitter with the official transit source, the comparisons show an influence on Twitter on the commercial and industrial area of the city. The results reveal the effectiveness of the accidents reported on Twitter as additional information and their use should be considered as complementary sources to the existing detection methods.MaestríaMagíster en Ingeniería - Ingeniería de Sistemas y ComputaciónEste trabajo propone un método para extraer y analizar los datos de accidentes de tránsito desde Twitter. Cuatro fases componen el método. La primera fase establece los mecanismos para obtener datos. El segundo consiste en representar vectorialmente los mensajes y clasificarlos como accidentes de tránsito o no. La tercera usa técnicas de reconocimiento de entidades nombradas para la detección de ubicaciones. En la cuarta estas ubicaciones pasan por un geocoder que devuelve sus coordenadas geográficas.Computación aplicadaxi, 60 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y ComputaciónDepartamento de Ingeniería de Sistemas e IndustrialFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresSistemas de transporte inteligenteRedes socialesAccidente de tránsitoSensores socialesProcesamiento de lenguaje naturalAprendizaje automáticoMinería de textoReconocimiento de entidades nombradasTwitterintelligent transportation systemSocial mediaTraffic accidentSocial sensorsNatural language processingMachine learningText miningNamed entity recognitionTwitterAnálisis de datosSeguridad del transporteData analysisTransport safetyExtracción y análisis de información de accidentes de tránsito desde redes socialesExtraction and analysis of traffic accident data from social networksTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMG. Cookson and B. Pishue, INRIX Global Traffic Scorecard. No. February, 2018.“Víctimas fallecidas y lesionadas valoradas por inmlcf. nacionales. agencia nacional de seguridad vial,” 2017.S. Wang, L. He, L. Stenneth, P. S. Yu, and Z. Li, “Citywide traffic congestion estimation with social media,” in Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS ’15, pp. 1–10, 2015.X. Fan, B. He, C. Wang, J. Li, M. Cheng, H. Huang, and X. Liu, “Big Data Analytics and Visualization with Spatio-Temporal Correlations for Traffic Accidents,” in 15th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2015), vol. 9531, (Zhangjiajie, China), pp. 255–268, 2015.M. B. Subaweh and E. P. Wibowo, “Implementation of Pixel Based Adaptive Segmenter method for tracking and counting vehicles in visual surveillance,” in 2016 International Conference on Informatics and Computing, ICIC 2016, no. Icic, pp. 1–5, 2016.L. Li, J. Zhang, Y. Zheng, and B. Ran, “Real-Time Traffic Incident Detection with Classification Methods,” in Green Intelligent Transportation Systems, Lecture Notes in Electrical Engineering, vol. 419, pp. 777–788, 2018.S. Zhang, G. Wu, J. P. Costeira, and J. M. Moura, “FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras,” in Proceedings of the IEEE International Conference on Computer Vision, vol. 2017-Octob, pp. 3687–3696, 2017.N. Krausz, T. Lovas, and A. Barsi, “Radio frequency identification in supporting traffic safety,” Periodica Polytechnica Civil Engineering, vol. 61, no. 4, pp. 727–731, 2017.M. Chaturvedi and S. Srivastava, “Edge-level real-time traffic estimation with limited infrastructure,” 2015 IEEE 82nd Vehicular Technology Conference, VTC Fall 2015 - Proceedings, 2016.W. Zuo, C. Guo, J. Liu, X. Peng, and M. Yang, “A police and insurance joint management system based on high precision BDS/GPS positioning,” Sensors (Switzerland), vol. 18, no. 1, 2018.J. Aslam, S. Lim, X. Pan, and D. Rus, “City-scale traffic estimation from a roving sensor network,” in SenSys 2012 - Proceedings of the 10th ACM Conference on Embedded Networked Sensor Systems, pp. 141–154, Association for Computing Machinery, 2012.S. Wang, X. Zhang, J. Cao, L. He, L. Stenneth, P. S. Yu, Z. Li, and Z. Huang, “Computing Urban Traffic Congestions by Incorporating Sparse GPS Probe Data and Social Media Data,” ACM Transactions on Information Systems, vol. 35, no. 4, pp. 1–30, 2017.T. Kuflik, E. Minkov, S. Nocera, S. Grant-Muller, A. Gal-Tzur, and I. Shoor, “Automating a framework to extract and analyse transport related social media content: The potential and the challenges,” Transportation Research Part C: Emerging Technologies, vol. 77, pp. 275–291, 2017.Y. Gu, Z. S. Qian, and F. 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Cuadros, “Sensitive Data Detection and Classification in Spanish Clinical Text: Experiments with BERT,” 2019.InvestigadoresPúblico generalORIGINAL1121893894.2021.pdf1121893894.2021.pdfTesis de Maestría en Ingeniería - Ingeniería de Sistemas y Computaciónapplication/pdf3755666https://repositorio.unal.edu.co/bitstream/unal/80927/1/1121893894.2021.pdf133dfc84022c722a4f45fff97d049a10MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/80927/2/license.txt8153f7789df02f0a4c9e079953658ab2MD52Licencia y autorización para publicación de obras en el repositorio institucional UN ok - Nestor Eduardo Suat Rojas.pdfLicencia y autorización para publicación de obras en el repositorio institucional UN ok - Nestor Eduardo Suat Rojas.pdfLicencia y autorización para 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EVESURBIFBPUiBMQSBTRUNSRVRBUsONQSBHRU5FUkFMLiAqTEEgVEVTSVMgQSBQVUJMSUNBUiBERUJFIFNFUiBMQSBWRVJTScOTTiBGSU5BTCBBUFJPQkFEQS4gCgpBbCBoYWNlciBjbGljIGVuIGVsIHNpZ3VpZW50ZSBib3TDs24sIHVzdGVkIGluZGljYSBxdWUgZXN0w6EgZGUgYWN1ZXJkbyBjb24gZXN0b3MgdMOpcm1pbm9zLiBTaSB0aWVuZSBhbGd1bmEgZHVkYSBzb2JyZSBsYSBsaWNlbmNpYSwgcG9yIGZhdm9yLCBjb250YWN0ZSBjb24gZWwgYWRtaW5pc3RyYWRvciBkZWwgc2lzdGVtYS4KClVOSVZFUlNJREFEIE5BQ0lPTkFMIERFIENPTE9NQklBIC0gw5psdGltYSBtb2RpZmljYWNpw7NuIDE5LzEwLzIwMjEK

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