Road accident forecast by using predictive modeling techniques
ilustraciones, diagramas, planos
- Autores:
-
Gutierrez Osorio, Camilo Albeiro
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2023
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/84616
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación
Accidentes de tránsito
Tráfico de carreteras
Traffic accidents - research
Road traffic
Machine learning
Deep learning
traffic accident risk prediction
traffic accidents
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| id |
UNACIONAL2_f83c923fc2577e7591519fb7cc9517dd |
|---|---|
| oai_identifier_str |
oai:repositorio.unal.edu.co:unal/84616 |
| network_acronym_str |
UNACIONAL2 |
| network_name_str |
Universidad Nacional de Colombia |
| repository_id_str |
|
| dc.title.eng.fl_str_mv |
Road accident forecast by using predictive modeling techniques |
| dc.title.translated.spa.fl_str_mv |
Pronóstico de accidentes de tráfico mediante el uso de técnicas de modelado predictivo |
| title |
Road accident forecast by using predictive modeling techniques |
| spellingShingle |
Road accident forecast by using predictive modeling techniques 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación Accidentes de tránsito Tráfico de carreteras Traffic accidents - research Road traffic Machine learning Deep learning traffic accident risk prediction traffic accidents |
| title_short |
Road accident forecast by using predictive modeling techniques |
| title_full |
Road accident forecast by using predictive modeling techniques |
| title_fullStr |
Road accident forecast by using predictive modeling techniques |
| title_full_unstemmed |
Road accident forecast by using predictive modeling techniques |
| title_sort |
Road accident forecast by using predictive modeling techniques |
| dc.creator.fl_str_mv |
Gutierrez Osorio, Camilo Albeiro |
| dc.contributor.advisor.none.fl_str_mv |
Pedraza Bonilla, César Augusto González Osorio, Fabio Augusto |
| dc.contributor.author.none.fl_str_mv |
Gutierrez Osorio, Camilo Albeiro |
| dc.contributor.researchgroup.spa.fl_str_mv |
Plas Programming languages And Systems |
| dc.contributor.orcid.spa.fl_str_mv |
https://orcid.org/0000-0002-9113-1369 |
| dc.contributor.scopus.spa.fl_str_mv |
https://www.scopus.com/authid/detail.uri?authorId=57209267130 |
| dc.contributor.researchgate.spa.fl_str_mv |
https://www.researchgate.net/profile/Camilo-Gutierrez-Osorio |
| dc.contributor.googlescholar.spa.fl_str_mv |
https://scholar.google.com/citations?user=E4ICanMAAAAJ&hl=en |
| dc.subject.ddc.spa.fl_str_mv |
000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación |
| topic |
000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación Accidentes de tránsito Tráfico de carreteras Traffic accidents - research Road traffic Machine learning Deep learning traffic accident risk prediction traffic accidents |
| dc.subject.lemb.spa.fl_str_mv |
Accidentes de tránsito Tráfico de carreteras |
| dc.subject.lemb.eng.fl_str_mv |
Traffic accidents - research Road traffic |
| dc.subject.proposal.eng.fl_str_mv |
Machine learning Deep learning traffic accident risk prediction traffic accidents |
| description |
ilustraciones, diagramas, planos |
| publishDate |
2023 |
| dc.date.accessioned.none.fl_str_mv |
2023-08-30T14:31:31Z |
| dc.date.available.none.fl_str_mv |
2023-08-30T14:31:31Z |
| dc.date.issued.none.fl_str_mv |
2023-08-29 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Doctorado |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_db06 |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TD |
| format |
http://purl.org/coar/resource_type/c_db06 |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/84616 |
| 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/84616 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 |
eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
Ahmed, M. M., & Abdel-Aty, M. A. (2012). The viability of using automatic vehicle identification data for real-time crash prediction. IEEE Transactions on Intelligent Transportation Systems, 13(2), 459–468. https://doi.org/10.1109/TITS.2011.2171052 Alkheder, S., Taamneh, M., & Taamneh, S. (2017). Severity Prediction of Traffic Accident Using an Artificial Neural Network. Journal of Forecasting, 36(1). https://doi.org/10.1002/for.2425 Amin-Naseri, M., Chakraborty, P., Sharma, A., Gilbert, S. B., & Hong, M. (2018). Evaluating the Reliability, Coverage, and Added Value of Crowdsourced Traffic Incident Reports from Waze. Transportation Research Record. https://doi.org/10.1177/0361198118790619 Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J. Z., & Wang, R. (2016). TrafficWatch: Real-Time Traffic Incident Detection and Monitoring Using Social Media. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9651, 540–551. https://doi.org/10.1007/978-3-319-31753-3 Bao, J., Liu, P., & Ukkusuri, S. V. (2019). A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data. Accident Analysis and Prevention, 122(November 2018), 239–254. https://doi.org/10.1016/j.aap.2018.10.015 Bao, J., Liu, P., Yu, H., & Xu, C. (2017). Incorporating twitter-based human activity information in spatial analysis of crashes in urban areas. Accident Analysis and Prevention, 106(July), 358–369. https://doi.org/10.1016/j.aap.2017.06.012 Bengio, Y., Courville, A., & Vincent, P. (2013). Representation Learning: A Review and New Perspectives. Pattern Analysis and Machine Intelligence, IEEE Transactions On, 35(8), 1798–1828. https://doi.org/10.1109/TPAMI.2013.50 Bocarejo, J. P., Velasquez, J. M., Díaz, C. A., & Tafur, L. E. (2012). Impact of bus rapid transit systems on road safety: Lessons from Bogotá, Colombia. In Transportation Research Record (Vol. 2317). https://doi.org/10.3141/2317-01 Bonilla, J. A. (2019). The More Stringent, the Better? Rationing Car Use in Bogotá with Moderate and Drastic Restrictions. World Bank Economic Review, 33(2), 516–534. https://doi.org/10.1093/wber/lhw053 Branco, P., Ribeiro, R. P., Torgo, L., Krawczyk, B., & Moniz, N. (2017). SMOGN: a Pre-processing Approach for Imbalanced Regression. Proceedings of Machine Learning Research, 74, 36–50. Cao, G., Michelini, J., Grigoriadis, K., Ebrahimi, B., & Franchek, M. A. (2015a). Cluster-based correlation of severe braking events with time and location. 2015 10th System of Systems Engineering Conference, SoSE 2015, 2450(June), 187–192. https://doi.org/10.1109/SYSOSE.2015.7151986 Castro, Y., & Kim, Y. J. (2016). Data mining on road safety: factor assessment on vehicle accidents using classification models. International Journal of Crashworthiness, 21(2), 104–111. https://doi.org/10.1080/13588265.2015.1122278 Chen, C., Fan, X., Zheng, C., Xiao, L., Cheng, M., & Wang, C. (2018). SDCAE: Stack Denoising Convolutional Autoencoder Model for Accident Risk Prediction Via Traffic Big Data. Proceedings - 2018 6th International Conference on Advanced Cloud and Big Data, CBD 2018, 328–333. https://doi.org/10.1109/CBD.2018.00065 Chen, Q., Song, X., Yamada, H., & Shibasaki, R. (2016). Learning deep representation from big and heterogeneous data for traffic accident inference. 30th AAAI Conference on Artificial Intelligence, AAAI 2016. Chen, Y., Lv, Y., Wang, X., & Wang, F. Y. (2018). A convolutional neural network for traffic information sensing from social media text. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2018-March, 1–6. https://doi.org/10.1109/ITSC.2017.8317650 Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. 1–9. Retrieved from http://arxiv.org/abs/1412.3555 Çodur, M. Y., & Tortum, A. (2015). An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey. PROMET - Traffic&Transportation, 27(3), 217–225. https://doi.org/10.7307/ptt.v27i3.1551 D’Andrea, E., Ducange, P., Lazzerini, B., & Marcelloni, F. (2015). Real-Time Detection of Traffic from Twitter Stream Analysis. IEEE Transactions on Intelligent Transportation Systems, 16(4), 2269–2283. https://doi.org/10.1109/TITS.2015.2404431 Dabiri, S., & Heaslip, K. (2019). Developing a Twitter-based traffic event detection model using deep learning architectures. Expert Systems with Applications, 118, 425–439. https://doi.org/10.1016/j.eswa.2018.10.017 Delen, D., Sharda, R., & Bessonov, M. (2006). Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks. Accident Analysis and Prevention, 38(3), 434–444. https://doi.org/10.1016/j.aap.2005.06.024 Fan, X., He, B., Wang, C., Li, J., Cheng, M., Huang, H., & Liu, X. (2015). Big data analytics and visualization with spatio-temporal correlations for traffic accidents. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9529). https://doi.org/10.1007/978-3-319-27122-4_18 Fawcett, L., Thorpe, N., Matthews, J., & Kremer, K. (2017). A novel Bayesian hierarchical model for road safety hotspot prediction. Accident Analysis and Prevention, 99, 262–271. https://doi.org/10.1016/j.aap.2016.11.021 Ganaie, M. A., Hu, M., Malik, A. K., Tanveer, M., & Suganthan, P. N. (2022). Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115. https://doi.org/10.1016/j.engappai.2022.105151 Geurts, K., Wets, G., Brijs, T., & Vanhoof, K. (2003). Profiling of High-Frequency Accident Locations by Use of Association Rules. Transportation Research Record, 1840(03), 123–130. https://doi.org/10.3141/1840-14 Ghosh, B., Asif, M. T., & Dauwels, J. (2017). Bayesian prediction of the duration of non-recurring road incidents. IEEE Region 10 Annual International Conference, Proceedings/TENCON, 87–90. https://doi.org/10.1109/TENCON.2016.7847964 Governmen, Australian. (2019). Australia’s Regional Open Data Census - Open data - Traffic Accidents. Retrieved July 15, 2019, from http://australia.census.okfn.org/dataset/traffic-accidents Government", "Colombian. (2019). datos.gov.co - Colombia - Open data catalog- Road accidents. Retrieved July 15, 2019, from https://www.datos.gov.co/browse?q=Registro nacional de accidentes de transito&sortBy=relevance “Government US.” (2019). data.gov - United States - Open data catalog - Traffic accidents. Retrieved July 15, 2019, from https://catalog.data.gov/dataset?q=traffic+accidents&sort=views_recent+desc&tags=crash&as_sfid=AAAAAAXHjZkDY7gFA5iMx_28NUE0FLt7GCD6A_wjSzainkj_rspLB-fqUew5h3LiHfKwq25Q1jllDf64k8tuEJ03xVdCKo4_qW6HRpHe_XBlCPYQhLUOwC0CkWT-WHXEHYKSTII%3D&as_fid=be93db12e7584b Gu, Y., Qian, Z. (Sean), & Chen, F. (2016). From Twitter to detector: Real-time traffic incident detection using social media data. Transportation Research Part C: Emerging Technologies, 67, 321–342. https://doi.org/10.1016/j.trc.2016.02.011 Gutierrez-Osorio, C., Gonzalez, F. A., & Pedraza, C. A. (2022). Deep Learning Ensemble Model for the Prediction of Traffic Accidents Using Social Media Data. Computers, 11(9), 126. Gutierrez-Osorio, C., & Pedraza, C. (2020). Modern data sources and techniques for analysis and forecast of road accidents: A review. Journal of Traffic and Transportation Engineering (English Edition), 7(4), 432–446. https://doi.org/10.1016/j.jtte.2020.05.002 Gutierrez-Osorio, C., & Pedraza, C. A. (2019a). Characterizing road accidents in urban areas of Bogota (Colombia): A data science approach. Proceedings of the 2nd Latin American Conference on Intelligent Transportation Systems, ITS LATAM 2019, 1–6. https://doi.org/10.1109/ITSLATAM.2019.8721334 Halim, Z., Kalsoom, R., Bashir, S., & Abbas, G. (2016). Artificial intelligence techniques for driving safety and vehicle crash prediction. Artificial Intelligence Review, 46(3), 351–387. https://doi.org/10.1007/s10462-016-9467-9 Hashmienejad, S. H., & Hossein, S. M. (2017). Traffic accident severity prediction using a novel multi-objective genetic algorithm. International Journal of Crashworthiness, 0(0), 1–16. https://doi.org/10.1080/13588265.2016.1275431 Janssen, M., Charalabidis, Y., & Zuiderwijk, A. (2012). Benefits, Adoption Barriers and Myths of Open Data and Open Government. Information Systems Management, 29(4), 258–268. https://doi.org/10.1080/10580530.2012.716740 Kaplan, S., & Prato, C. G. (2013). Cyclist-motorist crash patterns in Denmark: a latent class clustering approach. Traffic Injury Prevention, 14(7), 725–733. https://doi.org/10.1080/15389588.2012.759654 Kononen, D. W., Flannagan, C. A. C., & Wang, S. C. (2011). Identification and validation of a logistic regression model for predicting serious injuries associated with motor vehicle crashes. Accident Analysis and Prevention, 43(1), 112–122. https://doi.org/10.1016/j.aap.2010.07.018 Kumar, S., & Toshniwal, D. (2016). A data mining approach to characterize road accident locations. Journal of Modern Transportation, 24(1). https://doi.org/10.1007/s40534-016-0095-5 Kumar, Sachin, & Toshniwal, D. (2015). A data mining framework to analyze road accident data. Journal of Big Data, 2(1), 26. https://doi.org/10.1186/s40537-015-0035-y Kumar, Sachin, & Toshniwal, D. (2016). Analysis of hourly road accident counts using hierarchical clustering and cophenetic correlation coefficient (CPCC). Journal of Big Data, 3(1), 1–11. https://doi.org/10.1186/s40537-016-0046-3 Kunt, M. M., Aghayan, I., & Noii, N. (2011). Prediction for traffic accident severity: comparing the artificial neural network, genetic algorithm, combined genetic algorithm and pattern search methods. Transport, 26(4), 353–366. https://doi.org/10.3846/16484142.2011.635465 Li, P., Abdel-Aty, M., & Yuan, J. (2020). Real-time crash risk prediction on arterials based on LSTM-CNN. Accident Analysis and Prevention, 135(July 2019), 105371. https://doi.org/10.1016/j.aap.2019.105371 Lin, L., Wang, Q., & Sadek, A. W. (2015). A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction. Transportation Research Part C: Emerging Technologies, 55(June), 444–459. https://doi.org/10.1016/j.trc.2015.03.015 Moghaddam, F. R., Afandizadeh, S., & Ziyadi, M. (2011). Prediction of accident severity using artificial neural networks. International Journal of Civil Engineering, 9(1), 41–49. Mohamed, M. G., Saunier, N., Miranda-Moreno, L. F., & Ukkusuri, S. V. (2013). A clustering regression approach: A comprehensive injury severity analysis of pedestrian-vehicle crashes in New York, US and Montreal, Canada. Safety Science, 54, 27–37. https://doi.org/10.1016/j.ssci.2012.11.001 Moriya, K., Matsushima, S., & Yamanishi, K. (2018). Traffic Risk Mining From Heterogeneous Road Statistics. IEEE Transactions on Intelligent Transportation Systems, 19(11), 3662–3675. https://doi.org/10.1109/TITS.2018.2856533 Pandhare, K. R., & Shah, M. A. (2017). Real time road traffic event detection using Twitter and spark. Proceedings of the International Conference on Inventive Communication and Computational Technologies, ICICCT 2017, (Icicct), 445–449. https://doi.org/10.1109/ICICCT.2017.7975237 Park, S., Kim, S., & Ha, Y. (2016). Highway traffic accident prediction using VDS big data analysis. The Journal of Supercomputing, 72(7), 2815–2831. https://doi.org/10.1007/s11227-016-1624-z Parnami, A., Bavi, P., Papanikolaou, D., Akella, S., Lee, M., & Krishnan, S. (2018). Deep Learning Based Urban Analytics Platform: Applications to Traffic Flow Modeling and Prediction. ACM SIGKDD Workshop on Mining Urban Data (MUD3). Pérez-Espinosa, A., Reyes-Cabello, A. L., Quiroz-Fabián, J., & Bravo-Grajales, E. (2018). Trafico CDMX system: Using big data to improve the mobility in Mexico City. ACM International Conference Proceeding Series, (January), 13–17. https://doi.org/10.1145/3277104.3277114 Ren, H., Song, Y., Wang, J., Hu, Y., & Lei, J. (2017). A Deep Learning Approach to the Citywide Traffic Accident Risk Prediction. Retrieved from http://arxiv.org/abs/1710.09543 Roshandel, S., Zheng, Z., & Washington, S. (2015). Impact of real-time traffic characteristics on freeway crash occurrence: Systematic review and meta-analysis. Accident Analysis and Prevention, 79, 198–211. https://doi.org/10.1016/j.aap.2015.03.013 Salas, A., Georgakis, P., & Petalas, Y. (2018). Incident detection using data from social media. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2018-March, 751–755. https://doi.org/10.1109/ITSC.2017.8317967 Scott-Parker, B., & Oviedo-Trespalacios, O. (2017). Young driver risky behaviour and predictors of crash risk in Australia, New Zealand and Colombia: Same but different? Accident Analysis and Prevention, 99, 30–38. https://doi.org/10.1016/j.aap.2016.11.001 Shi, Q., & Abdel-Aty, M. (2015). Big Data applications in real-time traffic operation and safety monitoring and improvement on urban expressways. Transportation Research Part C, 58, 380–394. https://doi.org/10.1016/j.trc.2015.02.022 Sinnott, R. O., & Yin, S. (2015). Accident Black Spot Identification and Verification through Social Media. Proceedings - 2015 IEEE International Conference on Data Science and Data Intensive Systems; 8th IEEE International Conference Cyber, Physical and Social Computing; 11th IEEE International Conference on Green Computing and Communications and 8th IEEE Inte, 17–24. https://doi.org/10.1109/DSDIS.2015.34 Suat-Rojas, N., Gutierrez-Osorio, C., & Pedraza, C. (2022). Extraction and Analysis of Social Networks Data to Detect Traffic Accidents. Information (Switzerland), 13(1). https://doi.org/10.3390/info13010026 Taamneh, M., Alkheder, S., & Taamneh, S. (2017). Data-mining techniques for traffic accident modeling and prediction in the United Arab Emirates. Journal of Transportation Safety & Security, 9(2), 146–166. https://doi.org/10.1080/19439962.2016.1152338 Tiwari, P., Dao, H., & Nguyen, G. N. (2017). Performance evaluation of lazy, decision tree classifier and multilayer perceptron on traffic accident analysis. Informatica (Slovenia), 41(1), 39–46. “United Kingdom Government.” (2019). data.gov.uk - United Kingdom - Open data catalog - traffic accidents. Retrieved July 15, 2019, from https://data.gov.uk/search?q=traffic+accidents Veljković, N., Bogdanović-Dinić, S., & Stoimenov, L. (2014). Benchmarking open government: An open data perspective. Government Information Quarterly, 31(2), 278–290. https://doi.org/10.1016/j.giq.2013.10.011 Wenqi, L., Dongyu, L., & Menghua, Y. (2017). A model of traffic accident prediction based on convolutional neural network. 2017 2nd IEEE International Conference on Intelligent Transportation Engineering, ICITE 2017, 198–202. https://doi.org/10.1109/ICITE.2017.8056908 Wen, Q., Sun, L., Yang, F., Song, X., Gao, J., Wang, X., & Xu, H. (2020). Time series data augmentation for deep learning: A survey. arXiv preprint arXiv:2002.12478. Wirth, R., & Hipp, J. (2000). CRISP-DM : Towards a Standard Process Model for Data Mining. Proceedings of the 4th International Conference on the Practical Application of Knowledge Discovery and Data Mining, (24959), 29–39. https://doi.org/10.1.1.198.5133 World Health Organization. (2015). Global Status Report on Road. In Foreign Affairs. Retrieved from https://www.who.int/publications/i/item/global-status-report-on-road-safety-2018 Xiong, X., Chen, L., & Liang, J. (2017). A new framework of vehicle collision prediction by combining SVM and HMM. IEEE Transactions on Intelligent Transportation Systems, 19(3), 699-710. You, J., Wang, J., & Guo, J. (2017). Real-time crash prediction on freeways using data mining and emerging techniques. Journal of Modern Transportation, 25(2), 116–123. https://doi.org/10.1007/s40534-017-0129-7 Yu, L., Du, B., Hu, X., Sun, L., Han, L., & Lv, W. (2020). Deep spatio-temporal graph convolutional network for traffic accident prediction. Neurocomputing, 423, 135–147. https://doi.org/10.1016/j.neucom.2020.09.043 Yuan, J., & Abdel-Aty, M. (2018). Approach-level real-time crash risk analysis for signalized intersections. Accident Analysis and Prevention, 119, 274–289. https://doi.org/10.1016/j.aap.2018.07.031 Zhang, Z., He, Q., Gao, J., & Ni, M. (2018). A deep learning approach for detecting traffic accidents from social media data. Transportation Research Part C: Emerging Technologies, 86(November 2017), 580–596. https://doi.org/10.1016/j.trc.2017.11.027 Zheng, M., Li, T., Zhu, R., Chen, J., Ma, Z., Tang, M., … Wang, Z. (2019). Traffic accident’s severity prediction: A deep-learning approach-based CNN network. IEEE Access, 7, 39897–39910. https://doi.org/10.1109/ACCESS.2019.2903319 Zhou, Z., Wang, Y., Xie, X., Chen, L., & Liu, H. (2020). RiskOracle: A minute-level citywide traffic accident forecasting framework. ArXiv, (December 2019). https://doi.org/10.1609/aaai.v34i01.5480 |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.rights.license.spa.fl_str_mv |
Atribución-NoComercial 4.0 Internacional |
| dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/licenses/by-nc/4.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
Atribución-NoComercial 4.0 Internacional http://creativecommons.org/licenses/by-nc/4.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.spa.fl_str_mv |
84 páginas |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.publisher.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.publisher.program.spa.fl_str_mv |
Bogotá - Ingeniería - Doctorado en Ingeniería - Sistemas y Computación |
| 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 |
| bitstream.url.fl_str_mv |
https://repositorio.unal.edu.co/bitstream/unal/84616/1/license.txt https://repositorio.unal.edu.co/bitstream/unal/84616/2/71775136.2023.pdf https://repositorio.unal.edu.co/bitstream/unal/84616/3/71775136.2023.pdf.jpg |
| bitstream.checksum.fl_str_mv |
eb34b1cf90b7e1103fc9dfd26be24b4a 9defd0e6c0e11287fae25f8b9e303390 68799d20cae9931903b31e74317c56de |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| repository.mail.fl_str_mv |
repositorio_nal@unal.edu.co |
| _version_ |
1814089870850654208 |
| spelling |
Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Pedraza Bonilla, César Augustocba8a7e9dda8064a0b4e4fc53dc636d4González Osorio, Fabio Augusto35912f60905ba6e179208c70e6024e80Gutierrez Osorio, Camilo Albeiro18c54b3751620bb549bb8e22bc300e83Plas Programming languages And Systemshttps://orcid.org/0000-0002-9113-1369https://www.scopus.com/authid/detail.uri?authorId=57209267130https://www.researchgate.net/profile/Camilo-Gutierrez-Osoriohttps://scholar.google.com/citations?user=E4ICanMAAAAJ&hl=en2023-08-30T14:31:31Z2023-08-30T14:31:31Z2023-08-29https://repositorio.unal.edu.co/handle/unal/84616Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, planosLos accidentes de tránsito son una gran preocupación a nivel mundial, ya que tienen un impacto significativo en la seguridad, la salud y el bienestar de las personas, por lo que constituyen un importante campo de investigación sobre el uso de técnicas y algoritmos de última generación para analizarlos y predecirlos. El estudio de los accidentes de tráfico se ha realizado a partir de la información publicada por las entidades de tráfico, pero gracias a la ubicuidad y disponibilidad de las redes sociales es posible disponer de información detallada y en tiempo real de los accidentes de tráfico, lo que permite realizar estudios detallados que incluyen eventos de accidentalidad vial no registrados. El objetivo de esta tesis es proponer un modelo predictivo para estimar la probabilidad de accidentes de tránsito en un área determinada mediante la integración de información proveniente de entidades oficiales y redes sociales relacionadas con accidentes viales y eventos de infraestructura vial. El modelo diseñado fue un modelo de aprendizaje profundo, compuesto por unidades recurrentes cerradas y redes neuronales convolucionales. Los resultados obtenidos se compararon con resultados publicados por otros investigadores y muestran resultados prometedores, lo que indica que, en el contexto del problema, el modelo de aprendizaje profundo propuesto supera a otros modelos de aprendizaje profundo disponibles en la literatura. La información proporcionada por el modelo puede ser valiosa para que las agencias de control de tráfico planifiquen actividades de prevención de accidentes de tráfico. (Texto tomado de la fuente)Traffic accidents are a major global concern as they have a significant impact on safety, health, and well-being. Therefore, it is an important area of research to analyze and predict accidents using state-of-the-art techniques and algorithms. Traditionally, the study of traffic accidents has been conducted using information from traffic entities and road police forces. However, with the rise of social media platforms, it's now possible to access detailed and real-time information about road accidents in a specific region, which allows for more comprehensive studies, even including unrecorded road accident events. This thesis aims to develop a predictive model that estimates the probability of road accidents in a specific area by combining information from official entities and social media related to road accidents and road infrastructure events. The proposed model is an ensemble deep learning model made up of Gated Recurrent Units and Convolutional Neural Networks. The results were compared with other published research and the outcomes are promising, indicating that the proposed ensemble deep learning model is more effective than other deep learning models reported in literature. The information provided by the model could be valuable for traffic control agencies to plan road accident prevention activities.DoctoradoPh.D. en Ingeniería – Sistemas y ComputaciónSistemas Inteligentes de Transporte ITS84 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ingeniería - Doctorado en Ingeniería - Sistemas y ComputaciónFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computaciónAccidentes de tránsitoTráfico de carreterasTraffic accidents - researchRoad trafficMachine learningDeep learningtraffic accident risk predictiontraffic accidentsRoad accident forecast by using predictive modeling techniquesPronóstico de accidentes de tráfico mediante el uso de técnicas de modelado predictivoTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDAhmed, M. M., & Abdel-Aty, M. A. (2012). The viability of using automatic vehicle identification data for real-time crash prediction. IEEE Transactions on Intelligent Transportation Systems, 13(2), 459–468. https://doi.org/10.1109/TITS.2011.2171052Alkheder, S., Taamneh, M., & Taamneh, S. (2017). Severity Prediction of Traffic Accident Using an Artificial Neural Network. Journal of Forecasting, 36(1). https://doi.org/10.1002/for.2425Amin-Naseri, M., Chakraborty, P., Sharma, A., Gilbert, S. B., & Hong, M. (2018). Evaluating the Reliability, Coverage, and Added Value of Crowdsourced Traffic Incident Reports from Waze. Transportation Research Record. https://doi.org/10.1177/0361198118790619Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J. Z., & Wang, R. (2016). TrafficWatch: Real-Time Traffic Incident Detection and Monitoring Using Social Media. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9651, 540–551. https://doi.org/10.1007/978-3-319-31753-3Bao, J., Liu, P., & Ukkusuri, S. V. (2019). A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data. Accident Analysis and Prevention, 122(November 2018), 239–254. https://doi.org/10.1016/j.aap.2018.10.015Bao, J., Liu, P., Yu, H., & Xu, C. (2017). Incorporating twitter-based human activity information in spatial analysis of crashes in urban areas. Accident Analysis and Prevention, 106(July), 358–369. https://doi.org/10.1016/j.aap.2017.06.012Bengio, Y., Courville, A., & Vincent, P. (2013). Representation Learning: A Review and New Perspectives. Pattern Analysis and Machine Intelligence, IEEE Transactions On, 35(8), 1798–1828. https://doi.org/10.1109/TPAMI.2013.50Bocarejo, J. P., Velasquez, J. M., Díaz, C. A., & Tafur, L. E. (2012). Impact of bus rapid transit systems on road safety: Lessons from Bogotá, Colombia. In Transportation Research Record (Vol. 2317). https://doi.org/10.3141/2317-01Bonilla, J. A. (2019). The More Stringent, the Better? Rationing Car Use in Bogotá with Moderate and Drastic Restrictions. World Bank Economic Review, 33(2), 516–534. https://doi.org/10.1093/wber/lhw053Branco, P., Ribeiro, R. P., Torgo, L., Krawczyk, B., & Moniz, N. (2017). SMOGN: a Pre-processing Approach for Imbalanced Regression. Proceedings of Machine Learning Research, 74, 36–50.Cao, G., Michelini, J., Grigoriadis, K., Ebrahimi, B., & Franchek, M. A. (2015a). Cluster-based correlation of severe braking events with time and location. 2015 10th System of Systems Engineering Conference, SoSE 2015, 2450(June), 187–192. https://doi.org/10.1109/SYSOSE.2015.7151986Castro, Y., & Kim, Y. J. (2016). Data mining on road safety: factor assessment on vehicle accidents using classification models. International Journal of Crashworthiness, 21(2), 104–111. https://doi.org/10.1080/13588265.2015.1122278Chen, C., Fan, X., Zheng, C., Xiao, L., Cheng, M., & Wang, C. (2018). SDCAE: Stack Denoising Convolutional Autoencoder Model for Accident Risk Prediction Via Traffic Big Data. Proceedings - 2018 6th International Conference on Advanced Cloud and Big Data, CBD 2018, 328–333. https://doi.org/10.1109/CBD.2018.00065Chen, Q., Song, X., Yamada, H., & Shibasaki, R. (2016). Learning deep representation from big and heterogeneous data for traffic accident inference. 30th AAAI Conference on Artificial Intelligence, AAAI 2016.Chen, Y., Lv, Y., Wang, X., & Wang, F. Y. (2018). A convolutional neural network for traffic information sensing from social media text. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2018-March, 1–6. https://doi.org/10.1109/ITSC.2017.8317650Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. 1–9. Retrieved from http://arxiv.org/abs/1412.3555Çodur, M. Y., & Tortum, A. (2015). An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey. PROMET - Traffic&Transportation, 27(3), 217–225. https://doi.org/10.7307/ptt.v27i3.1551D’Andrea, E., Ducange, P., Lazzerini, B., & Marcelloni, F. (2015). Real-Time Detection of Traffic from Twitter Stream Analysis. IEEE Transactions on Intelligent Transportation Systems, 16(4), 2269–2283. https://doi.org/10.1109/TITS.2015.2404431Dabiri, S., & Heaslip, K. (2019). Developing a Twitter-based traffic event detection model using deep learning architectures. Expert Systems with Applications, 118, 425–439. https://doi.org/10.1016/j.eswa.2018.10.017Delen, D., Sharda, R., & Bessonov, M. (2006). Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks. Accident Analysis and Prevention, 38(3), 434–444. https://doi.org/10.1016/j.aap.2005.06.024Fan, X., He, B., Wang, C., Li, J., Cheng, M., Huang, H., & Liu, X. (2015). Big data analytics and visualization with spatio-temporal correlations for traffic accidents. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9529). https://doi.org/10.1007/978-3-319-27122-4_18Fawcett, L., Thorpe, N., Matthews, J., & Kremer, K. (2017). A novel Bayesian hierarchical model for road safety hotspot prediction. Accident Analysis and Prevention, 99, 262–271. https://doi.org/10.1016/j.aap.2016.11.021Ganaie, M. A., Hu, M., Malik, A. K., Tanveer, M., & Suganthan, P. N. (2022). Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115. https://doi.org/10.1016/j.engappai.2022.105151Geurts, K., Wets, G., Brijs, T., & Vanhoof, K. (2003). Profiling of High-Frequency Accident Locations by Use of Association Rules. Transportation Research Record, 1840(03), 123–130. https://doi.org/10.3141/1840-14Ghosh, B., Asif, M. T., & Dauwels, J. (2017). Bayesian prediction of the duration of non-recurring road incidents. IEEE Region 10 Annual International Conference, Proceedings/TENCON, 87–90. https://doi.org/10.1109/TENCON.2016.7847964Governmen, Australian. (2019). Australia’s Regional Open Data Census - Open data - Traffic Accidents. Retrieved July 15, 2019, from http://australia.census.okfn.org/dataset/traffic-accidentsGovernment", "Colombian. (2019). datos.gov.co - Colombia - Open data catalog- Road accidents. Retrieved July 15, 2019, from https://www.datos.gov.co/browse?q=Registro nacional de accidentes de transito&sortBy=relevance“Government US.” (2019). data.gov - United States - Open data catalog - Traffic accidents. Retrieved July 15, 2019, from https://catalog.data.gov/dataset?q=traffic+accidents&sort=views_recent+desc&tags=crash&as_sfid=AAAAAAXHjZkDY7gFA5iMx_28NUE0FLt7GCD6A_wjSzainkj_rspLB-fqUew5h3LiHfKwq25Q1jllDf64k8tuEJ03xVdCKo4_qW6HRpHe_XBlCPYQhLUOwC0CkWT-WHXEHYKSTII%3D&as_fid=be93db12e7584bGu, Y., Qian, Z. (Sean), & Chen, F. (2016). From Twitter to detector: Real-time traffic incident detection using social media data. Transportation Research Part C: Emerging Technologies, 67, 321–342. https://doi.org/10.1016/j.trc.2016.02.011Gutierrez-Osorio, C., Gonzalez, F. A., & Pedraza, C. A. (2022). Deep Learning Ensemble Model for the Prediction of Traffic Accidents Using Social Media Data. Computers, 11(9), 126.Gutierrez-Osorio, C., & Pedraza, C. (2020). Modern data sources and techniques for analysis and forecast of road accidents: A review. Journal of Traffic and Transportation Engineering (English Edition), 7(4), 432–446. https://doi.org/10.1016/j.jtte.2020.05.002Gutierrez-Osorio, C., & Pedraza, C. A. (2019a). Characterizing road accidents in urban areas of Bogota (Colombia): A data science approach. Proceedings of the 2nd Latin American Conference on Intelligent Transportation Systems, ITS LATAM 2019, 1–6. https://doi.org/10.1109/ITSLATAM.2019.8721334Halim, Z., Kalsoom, R., Bashir, S., & Abbas, G. (2016). Artificial intelligence techniques for driving safety and vehicle crash prediction. Artificial Intelligence Review, 46(3), 351–387. https://doi.org/10.1007/s10462-016-9467-9Hashmienejad, S. H., & Hossein, S. M. (2017). Traffic accident severity prediction using a novel multi-objective genetic algorithm. International Journal of Crashworthiness, 0(0), 1–16. https://doi.org/10.1080/13588265.2016.1275431Janssen, M., Charalabidis, Y., & Zuiderwijk, A. (2012). Benefits, Adoption Barriers and Myths of Open Data and Open Government. Information Systems Management, 29(4), 258–268. https://doi.org/10.1080/10580530.2012.716740Kaplan, S., & Prato, C. G. (2013). Cyclist-motorist crash patterns in Denmark: a latent class clustering approach. Traffic Injury Prevention, 14(7), 725–733. https://doi.org/10.1080/15389588.2012.759654Kononen, D. W., Flannagan, C. A. C., & Wang, S. C. (2011). Identification and validation of a logistic regression model for predicting serious injuries associated with motor vehicle crashes. Accident Analysis and Prevention, 43(1), 112–122. https://doi.org/10.1016/j.aap.2010.07.018Kumar, S., & Toshniwal, D. (2016). A data mining approach to characterize road accident locations. Journal of Modern Transportation, 24(1). https://doi.org/10.1007/s40534-016-0095-5Kumar, Sachin, & Toshniwal, D. (2015). A data mining framework to analyze road accident data. Journal of Big Data, 2(1), 26. https://doi.org/10.1186/s40537-015-0035-yKumar, Sachin, & Toshniwal, D. (2016). Analysis of hourly road accident counts using hierarchical clustering and cophenetic correlation coefficient (CPCC). Journal of Big Data, 3(1), 1–11. https://doi.org/10.1186/s40537-016-0046-3Kunt, M. M., Aghayan, I., & Noii, N. (2011). Prediction for traffic accident severity: comparing the artificial neural network, genetic algorithm, combined genetic algorithm and pattern search methods. Transport, 26(4), 353–366. https://doi.org/10.3846/16484142.2011.635465Li, P., Abdel-Aty, M., & Yuan, J. (2020). Real-time crash risk prediction on arterials based on LSTM-CNN. Accident Analysis and Prevention, 135(July 2019), 105371. https://doi.org/10.1016/j.aap.2019.105371Lin, L., Wang, Q., & Sadek, A. W. (2015). A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction. Transportation Research Part C: Emerging Technologies, 55(June), 444–459. https://doi.org/10.1016/j.trc.2015.03.015Moghaddam, F. R., Afandizadeh, S., & Ziyadi, M. (2011). Prediction of accident severity using artificial neural networks. International Journal of Civil Engineering, 9(1), 41–49.Mohamed, M. G., Saunier, N., Miranda-Moreno, L. F., & Ukkusuri, S. V. (2013). A clustering regression approach: A comprehensive injury severity analysis of pedestrian-vehicle crashes in New York, US and Montreal, Canada. Safety Science, 54, 27–37. https://doi.org/10.1016/j.ssci.2012.11.001Moriya, K., Matsushima, S., & Yamanishi, K. (2018). Traffic Risk Mining From Heterogeneous Road Statistics. IEEE Transactions on Intelligent Transportation Systems, 19(11), 3662–3675. https://doi.org/10.1109/TITS.2018.2856533Pandhare, K. R., & Shah, M. A. (2017). Real time road traffic event detection using Twitter and spark. Proceedings of the International Conference on Inventive Communication and Computational Technologies, ICICCT 2017, (Icicct), 445–449. https://doi.org/10.1109/ICICCT.2017.7975237Park, S., Kim, S., & Ha, Y. (2016). Highway traffic accident prediction using VDS big data analysis. The Journal of Supercomputing, 72(7), 2815–2831. https://doi.org/10.1007/s11227-016-1624-zParnami, A., Bavi, P., Papanikolaou, D., Akella, S., Lee, M., & Krishnan, S. (2018). Deep Learning Based Urban Analytics Platform: Applications to Traffic Flow Modeling and Prediction. ACM SIGKDD Workshop on Mining Urban Data (MUD3).Pérez-Espinosa, A., Reyes-Cabello, A. L., Quiroz-Fabián, J., & Bravo-Grajales, E. (2018). Trafico CDMX system: Using big data to improve the mobility in Mexico City. ACM International Conference Proceeding Series, (January), 13–17. https://doi.org/10.1145/3277104.3277114Ren, H., Song, Y., Wang, J., Hu, Y., & Lei, J. (2017). A Deep Learning Approach to the Citywide Traffic Accident Risk Prediction. Retrieved from http://arxiv.org/abs/1710.09543Roshandel, S., Zheng, Z., & Washington, S. (2015). Impact of real-time traffic characteristics on freeway crash occurrence: Systematic review and meta-analysis. Accident Analysis and Prevention, 79, 198–211. https://doi.org/10.1016/j.aap.2015.03.013Salas, A., Georgakis, P., & Petalas, Y. (2018). Incident detection using data from social media. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2018-March, 751–755. https://doi.org/10.1109/ITSC.2017.8317967Scott-Parker, B., & Oviedo-Trespalacios, O. (2017). Young driver risky behaviour and predictors of crash risk in Australia, New Zealand and Colombia: Same but different? Accident Analysis and Prevention, 99, 30–38. https://doi.org/10.1016/j.aap.2016.11.001Shi, Q., & Abdel-Aty, M. (2015). Big Data applications in real-time traffic operation and safety monitoring and improvement on urban expressways. Transportation Research Part C, 58, 380–394. https://doi.org/10.1016/j.trc.2015.02.022Sinnott, R. O., & Yin, S. (2015). Accident Black Spot Identification and Verification through Social Media. Proceedings - 2015 IEEE International Conference on Data Science and Data Intensive Systems; 8th IEEE International Conference Cyber, Physical and Social Computing; 11th IEEE International Conference on Green Computing and Communications and 8th IEEE Inte, 17–24. https://doi.org/10.1109/DSDIS.2015.34Suat-Rojas, N., Gutierrez-Osorio, C., & Pedraza, C. (2022). Extraction and Analysis of Social Networks Data to Detect Traffic Accidents. Information (Switzerland), 13(1). https://doi.org/10.3390/info13010026Taamneh, M., Alkheder, S., & Taamneh, S. (2017). Data-mining techniques for traffic accident modeling and prediction in the United Arab Emirates. Journal of Transportation Safety & Security, 9(2), 146–166. https://doi.org/10.1080/19439962.2016.1152338Tiwari, P., Dao, H., & Nguyen, G. N. (2017). Performance evaluation of lazy, decision tree classifier and multilayer perceptron on traffic accident analysis. Informatica (Slovenia), 41(1), 39–46.“United Kingdom Government.” (2019). data.gov.uk - United Kingdom - Open data catalog - traffic accidents. Retrieved July 15, 2019, from https://data.gov.uk/search?q=traffic+accidentsVeljković, N., Bogdanović-Dinić, S., & Stoimenov, L. (2014). Benchmarking open government: An open data perspective. Government Information Quarterly, 31(2), 278–290. https://doi.org/10.1016/j.giq.2013.10.011Wenqi, L., Dongyu, L., & Menghua, Y. (2017). A model of traffic accident prediction based on convolutional neural network. 2017 2nd IEEE International Conference on Intelligent Transportation Engineering, ICITE 2017, 198–202. https://doi.org/10.1109/ICITE.2017.8056908Wen, Q., Sun, L., Yang, F., Song, X., Gao, J., Wang, X., & Xu, H. (2020). Time series data augmentation for deep learning: A survey. arXiv preprint arXiv:2002.12478.Wirth, R., & Hipp, J. (2000). CRISP-DM : Towards a Standard Process Model for Data Mining. Proceedings of the 4th International Conference on the Practical Application of Knowledge Discovery and Data Mining, (24959), 29–39. https://doi.org/10.1.1.198.5133World Health Organization. (2015). Global Status Report on Road. In Foreign Affairs. Retrieved from https://www.who.int/publications/i/item/global-status-report-on-road-safety-2018Xiong, X., Chen, L., & Liang, J. (2017). A new framework of vehicle collision prediction by combining SVM and HMM. IEEE Transactions on Intelligent Transportation Systems, 19(3), 699-710.You, J., Wang, J., & Guo, J. (2017). Real-time crash prediction on freeways using data mining and emerging techniques. Journal of Modern Transportation, 25(2), 116–123. https://doi.org/10.1007/s40534-017-0129-7Yu, L., Du, B., Hu, X., Sun, L., Han, L., & Lv, W. (2020). Deep spatio-temporal graph convolutional network for traffic accident prediction. Neurocomputing, 423, 135–147. https://doi.org/10.1016/j.neucom.2020.09.043Yuan, J., & Abdel-Aty, M. (2018). Approach-level real-time crash risk analysis for signalized intersections. Accident Analysis and Prevention, 119, 274–289. https://doi.org/10.1016/j.aap.2018.07.031Zhang, Z., He, Q., Gao, J., & Ni, M. (2018). A deep learning approach for detecting traffic accidents from social media data. Transportation Research Part C: Emerging Technologies, 86(November 2017), 580–596. https://doi.org/10.1016/j.trc.2017.11.027Zheng, M., Li, T., Zhu, R., Chen, J., Ma, Z., Tang, M., … Wang, Z. (2019). Traffic accident’s severity prediction: A deep-learning approach-based CNN network. IEEE Access, 7, 39897–39910. https://doi.org/10.1109/ACCESS.2019.2903319Zhou, Z., Wang, Y., Xie, X., Chen, L., & Liu, H. (2020). RiskOracle: A minute-level citywide traffic accident forecasting framework. ArXiv, (December 2019). https://doi.org/10.1609/aaai.v34i01.5480InvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/84616/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL71775136.2023.pdf71775136.2023.pdfTesis de Doctorado en Ingeniería - Sistemas y Computaciónapplication/pdf1370888https://repositorio.unal.edu.co/bitstream/unal/84616/2/71775136.2023.pdf9defd0e6c0e11287fae25f8b9e303390MD52THUMBNAIL71775136.2023.pdf.jpg71775136.2023.pdf.jpgGenerated Thumbnailimage/jpeg4692https://repositorio.unal.edu.co/bitstream/unal/84616/3/71775136.2023.pdf.jpg68799d20cae9931903b31e74317c56deMD53unal/84616oai:repositorio.unal.edu.co:unal/846162024-08-12 23:12:22.304Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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 |