Bogotá’s traffic prediction system using graph neural networks

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dc.contributor.advisorMartínez Vasquez, David Alejandro
dc.contributor.advisorMateus Rojas , Armando
dc.contributor.authorGarcía Pérez, Juan Camilo
dc.contributor.corporatenameUniversidad Santo Tomás
dc.contributor.cvlachttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001560096
dc.contributor.cvlachttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000680630
dc.contributor.cvlachttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001816224
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dc.contributor.googlescholarhttps://scholar.google.com/citations?user=1az5o_IAAAAJ&hl=es&oi=ao
dc.contributor.googlescholarhttps://scholar.google.com/citations?user=vNvgsOwAAAAJ&hl=es&oi=ao
dc.contributor.orcidhttps://orcid.org/0000-0001-9750-2653
dc.contributor.orcidhttps://orcid.org/0000-0002-2399-4859
dc.contributor.orcidhttps://orcid.org/0000-0001-6973-9477
dc.date.accessioned2026-07-17T16:30:13Z
dc.date.available2026-07-17T16:30:13Z
dc.date.issued2025-05
dc.descriptionLa congestión vehicular en zonas urbanas como Bogotá representa un desafío significativo que afecta la calidad de vida de los ciudadanos, la eficiencia económica y el medio ambiente. Los largos tiempos de desplazamiento, la contaminación atmosférica y el estrés asociado al tráfico son problemas apremiantes que requieren soluciones innovadoras para fomentar una movilidad urbana sostenible. Si bien existen metodologías para abordar estas problemáticas, a menudo no logran captar la complejidad inherente de los entornos urbanos, en particular la interconexión entre el flujo vehicular y la densidad poblacional.
dc.description.abstractVehicle congestion in urban areas like Bogotá represents a significant challenge affecting citizens’ quality of life, economic efficiency, and the environment. Long commute times, air pollution, and traffic-related stress are pressing issues requiring innovative solutions to promote sustainable urban mobility. While methodologies exist to address these problems, they often fail to capture the inherent complexity of urban environments, particularly the inter connected factors of traffic flow and population density.
dc.description.degreelevelPregradospa
dc.description.degreenameIngeniero Electronicospa
dc.description.domainhttp://unidadinvestigacion.usta.edu.co
dc.format.mimetypeapplication/pdf
dc.identifier.citationGarcia Perez, J. C. (2025). Bogotá’s traffic prediction system using graph neural networks. [Trabajo de Grado, Universidad Santo Tomás]. Repositorio Institucional.
dc.identifier.instnameinstname:Universidad Santo Tomásspa
dc.identifier.reponamereponame:Repositorio Institucional Universidad Santo Tomásspa
dc.identifier.repourlrepourl:https://repository.usta.edu.cospa
dc.identifier.urihttp://hdl.handle.net/11634/73259
dc.language.isospa
dc.publisherUniversidad Santo Tomásspa
dc.publisher.branchCRAI-USTA Bogotá
dc.publisher.facultyFacultad de Ingeniería Electrónicaspa
dc.publisher.programPregrado Ingeniería Electrónicaspa
dc.relation.referencesBing Yu, Haoteng Yin, and Zhanxing Zhu. “Spatio-Temporal Graph Convolutional Net works: A Deep Learning Framework for Traffic Forecasting”. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI) (2018), pp. 3634–3640. DOI: 10.24963/ijcai.2018/505. URL: https://arxiv.org/abs/1709.04875.
dc.relation.referencesClaudia Dangond-Gibsone et al. “Del transporte a la movilidad urbana en Bogotá: Más que un problema de vías y automotores”. In: (2013). URL: http://hdl.handle.net/ 10554/44690.
dc.relation.referencesPNUD. Sustainable Development Goals. 2023. URL: https://www.undp.org/es/ sustainable-development-goals/.
dc.relation.referencesPablo Alvarez Lopez et al. “Microscopic Traffic Simulation using SUMO”. In: The 21st IEEE International Conference on Intelligent Transportation Systems. 2018. URL: https:// elib.dlr.de/124092/
dc.relation.referencesLing Zhao et al. “T-GCN: A Temporal Graph Convolutional Network for Traffic Predic tion”. In: IEEE Transactions on Intelligent Transportation Systems 21.9 (2020), pp. 3848–3858. DOI: 10.1109/TITS.2019.2935152
dc.relation.referencesYaroslav Shamlitskiy et al. “Transport Stream Optimization Based on Neural Network Learning Algorithms”. In: Transportation Research Procedia 68 (2023), pp. 417–425. DOI: 10.1016/j.trpro.2023.02.056
dc.relation.referencesPetar Veliˇckovi´c. “Everything is connected: Graph neural networks”. In: Current Opinion in Structural Biology 79 (2023), p. 102538. DOI: 10.1016/j.sbi.2023.102538
dc.relation.referencesEleni I. Vlahogianni. “Computational Intelligence and Optimization for Transportation BigData:ChallengesandOpportunities”.In:EngineeringandAppliedSciencesOptimization (2015), pp. 107–128.
dc.relation.referencesMohammed S. Ahmed and Allen R. Cook. Analysis of Freeway Traffic Time-Series Data by Using Box-Jenkins Techniques. Tech. rep. Transportation Research Board, 1979
dc.relation.referencesBilly M. Williams and Lester A. Hoel. “Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results”. In: Journal of Transportation Engineering 129.6 (2003), pp. 664–672
dc.relation.referencesYuhan Jia, Jianping Wu, and Yiman Du. “Traffic Speed Prediction Using Deep Learning Method”.In:IEEETransactionsonIntelligent Transportation Systems (ITSC) (2016), pp. 1217 1222
dc.relation.referencesWenhao Huang et al. “Deep Architecture for Traffic Flow Prediction: Deep Belief Net works with Multitask Learning”. In: IEEE Transactions on Intelligent Transportation Systems 15.5 (2014), pp. 2191–2201
dc.relation.referencesYisheng Lv et al. “Traffic Flow Prediction with Big Data: A Deep Learning Approach”. In: IEEE Transactions on Intelligent Transportation Systems 16.2 (2015), pp. 865–873
dc.relation.referencesQuanjun Chen et al. “Learning Deep Representation from Big and Heterogeneous Data for Traffic Accident Inference”. In: AAAI Conference on Artificial Intelligence. 2016, pp. 338 344
dc.relation.referencesLingfei Wu et al. Graph Neural Networks: Foundations, Frontiers, and Applications. Springer, 2022.
dc.relation.referencesThomas N. Kipf and Max Welling. “Semi-Supervised Classification with Graph Convo lutional Networks”. In: International Conference on Learning Representations (ICLR) (2017)
dc.relation.referencesSepp Hochreiter and Jürgen Schmidhuber. “Long Short-Term Memory”. In: Neural Com putation 9.8 (1997), pp. 1735–1780.
dc.relation.referencesYuankaiWuandHuachunTan.“Short-TermTrafficFlowForecastingwithSpatial-Temporal Correlation in a Hybrid Deep Learning Framework”. In: arXiv preprint arXiv:1612.01022 (2016)
dc.relation.referencesXingjian Shi et al. “Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting”. In: Advances in Neural Information Processing Systems (NIPS). 2015, pp. 802–810.
dc.relation.referencesYao MaandJiliang Tang. Deep Learning on Graphs. Cambridge University Press, 2021
dc.relation.referencesClaudio Stamile, Aldo Marzullo, and Enrico Deusebio. Graph Machine Learning. Packt Publishing, 2021
dc.relation.referencesMichaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. “Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering”. In: Advances in Neural Infor mation Processing Systems (NIPS). 2016, pp. 3844–3852.
dc.relation.referencesYoungjooSeoetal.“Structured Sequence ModelingwithGraphConvolutionalRecurrent Networks”. In: arXiv preprint arXiv:1612.07659 (2016)
dc.relation.referencesJonas Gehring et al. “Convolutional Sequence to Sequence Learning”. In: arXiv preprint arXiv:1705.03122 (2017).
dc.relation.referencesYaguangLietal.“DiffusionConvolutionalRecurrentNeuralNetwork:Data-DrivenTraf fic Forecasting”. In: International Conference on Learning Representations (ICLR) (2018).
dc.relation.referencesIlya Sutskever, Oriol Vinyals, and Quoc V. Le. “Sequence to Sequence Learning with Neural Networks”. In: Advances in Neural Information Processing Systems (NIPS). 2014, pp. 3104–3112.
dc.relation.referencesDavid I. Shuman et al. “The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains”. In: IEEE Signal Processing Magazine 30.3 (2013), pp. 83–98.
dc.relation.referencesDavid K. Hammond, Pierre Vandergheynst, and Rémi Gribonval. “Wavelets on Graphs via Spectral Graph Theory”. In: Applied and Computational Harmonic Analysis 30.2 (2011), pp. 129–150
dc.relation.referencesJoan Bruna et al. “Spectral Networks and Locally Connected Networks on Graphs”. In: arXiv preprint arXiv:1312.6203 (2013)
dc.relation.referencesMathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov. “Learning Convolutional Neural Networks for Graphs”. In: International Conference on Machine Learning (ICML). 2016, pp. 2014–2023
dc.relation.referencesYexin Li et al. “Traffic Prediction in a Bike-Sharing System”. In: ACM SIGSPATIAL. 2015, p. 33.
dc.relation.referencesChao Chen et al. “Freeway Performance Measurement System: Mining Loop Detector Data”. In: Transportation Research Record 1748 (2001), pp. 96–102.
dc.relation.referencesDiederik P. Kingma and Jimmy Ba. “Adam: A Method for Stochastic Optimization”. In: arXiv preprint arXiv:1412.6980 (2014). Published as a conference paper at ICLR 2015. URL: https://arxiv.org/abs/1412.6980
dc.relation.referencesJunbo Zhang, Yu Zheng, and Dekang Qi. “Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction”. In: IEEE Transactions on Intelligent Transportation Sys tems 22.1 (2021), pp. 252–265. DOI: 10.1109/TITS.2020.3049170.
dc.relation.referencesSecretaría de Movilidad de Bogotá. Datos de Movilidad en Bogotá. 2024. URL: https:// datos.movilidadbogota.gov.co/.
dc.relation.referencesOpenDataSoft. Tráfico y Movilidad Urbana. Accessed: April 3, 2025. 2024. URL: https: //transport.opendatasoft.com/pages/trafico/?flg=es.
dc.relation.referencesDLR SUMO. OSM Web Wizard- Road Type Selection. Accessed: April 3, 2025. 2024. URL: https://sumo.dlr.de/docs/Tutorials/OSMWebWizard.html#road-type_ selection.
dc.relation.referencesEnvironmental Protection Agency. Fuel Economy in Urban Driving Conditions. Technical Report EPA-420-R-22-003. 2022
dc.relation.referencesWorldHealthOrganization. Transportation Noise and Stress Indicators in Urban Populations. WHO Regional Office for Europe, 2021.
dc.relation.referencesK.MüllerandJ.Schmidt.“EmissionReductionsThroughAdaptiveSignalControl:Zurich Case Study”. In: Proceedings of the 23rd International IEEE Conference on Intelligent Trans portation Systems. 2020, pp. 1–6. DOI: 10.1109/ITSC45102.2020.9294556
dc.rightsAttribution-NonCommercial-NoDerivs 2.5 Colombiaen
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2
dc.rights.localAbierto (Texto Completo)spa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/
dc.subject.keywordMachine Learning
dc.subject.keywordDeep Learning
dc.subject.keywordArtificial Intelligence
dc.subject.keywordGraph Neural Networks
dc.subject.keywordTraffic Prediction
dc.subject.keywordTraffic Forecasting
dc.subject.lembIngeniería Electrónica
dc.subject.lembTransporte urbano
dc.subject.lembContaminación del aire
dc.subject.proposalGraph Neural Networks
dc.subject.proposalMachine Learning
dc.subject.proposalTraffic Prediction
dc.subject.proposalBogotá
dc.subject.proposalTraffic Forecasting
dc.titleBogotá’s traffic prediction system using graph neural networks
dc.typebachelor thesis
dc.type.categoryApropiación Social y Circulación del Conocimiento: Consultoría científico-tecnológica
dc.type.coarhttp://purl.org/coar/resource_type/c_7a1f
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.driveinfo:eu-repo/semantics/bachelorThesis
dc.type.localTrabajo de gradospa
dc.type.versioninfo:eu-repo/semantics/acceptedVersion

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