Desarrollo de un programa de Eco-Driving a nivel operativo con monitoreo a bordo OBD II en el Área Metropolitana de Bucaramanga

dc.contributor.advisorMaradey Lázaro, Jessica Gissella
dc.contributor.advisorHuertas Cardozo, José Ignacio
dc.contributor.apolounabMaradey Lázaro, Jessica Gissella [jessica-gissella-maradey-lázaro]spa
dc.contributor.authorAngulo Sanchez, Laura Valentina
dc.contributor.cvlacAngulo Sanchez, Laura Valentina [1004924368]spa
dc.contributor.cvlacMaradey Lázaro, Jessica Gissella [0000040553]spa
dc.contributor.cvlacHuertas Cardozo, José Ignacio [0000057398]spa
dc.contributor.googlescholarHuertas Cardozo, José Ignacio [es&oi=ao]spa
dc.contributor.orcidMaradey Lázaro, Jessica Gissella [0000-0003-2319-1965]spa
dc.contributor.orcidHuertas Cardozo, José Ignacio [0000-0003-4508-6453]spa
dc.coverage.campusUNAB Campus Bucaramangaspa
dc.coverage.spatialBucaramanga (Santander, Colombia)spa
dc.date.accessioned2024-01-18T15:27:33Z
dc.date.available2024-01-18T15:27:33Z
dc.date.issued2024-01-17
dc.degree.nameIngeniero Mecatrónicospa
dc.description.abstractLas estrategias de Eco-Driving buscan controlar y reducir el consumo innecesario de combustible mediante la toma de decisiones antes y durante la conducción. Para desarrollar las estrategias primero se realizó una campaña de monitoreo en condiciones reales de carreta para una muestra de 6 vehículos durante 6 meses, el objetivo de la campaña fue recolectar datos de variables de manejo como la velocidad, aceleración, RPM, consumo de combustible, entre otros, mediante un dispositivo OBD II. Para analizar los estilos de conducción, se implementaron dos modelos de aprendizaje no supervisado: Kmeans y DBSCAN. Utilizando las métricas del coeficiente de silueta y el índice de Davies Boulton, se determinó que el método de mayor calidad para la clasificación es Kmeans cuando se itera con las variables de velocidad promedio y aceleración positiva máxima. Los resultados indicaron la existencia de 3 grupos o clústeres que corresponden a los estilos calmado, normal y agresivo. A partir de esta clasificación, se identificaron las características distintivas de cada estilo, lo que permitió plantear estrategias de Ecodriving a nivel operativo, es decir relacionadas directamente con el comportamiento del conductor en carretera. Finalmente, para estimar el ahorro potencial derivado de estas estrategias, se entrenaron cinco modelos de regresión con aprendizaje supervisado. Cada modelo se evaluó utilizando índices de error como MSE, RMSE y R2, Los resultados indicaron que el modelo óptimo para la estimación es Random forest debido a que tiene un RMSE de 4,99 L/100Km siendo el valor más bajo de error y un coeficiente de determinación de 0.91 siendo el valor más alto obtenido, lo cual es indicador de una alta precisión del modelo entrenado. Según los resultados, se concluyó que el Ecodriving posibilita una reducción de hasta el 30% en el consumo de combustible. Esto se traduce en un ahorro económico para el conductor y una disminución en la producción de emisiones contaminantes.spa
dc.description.abstractenglishEco-Driving strategies seek to control and reduce unnecessary fuel consumption by making decisions before and during driving. To develop the strategies, a monitoring campaign was first carried out in real road conditions for a sample of 6 vehicles for 6 months, the goal of the campaign was to collect data on driving variables such as speed, acceleration, RPM, fuel consumption, among others, using an OBD II device. To analyze driving styles, two unsupervised learning models were implemented: Kmeans and DBSCAN. Using the metrics of the silhouette coefficient and the Davies Boulton index, it was determined that the highest quality method for classification is Kmeans when iterated with the variables of average velocity and maximum positive acceleration. The results indicated the existence of 3 groups or clusters corresponding to the styles calm, normal and aggressive. Based on this classification, the distinctive characteristics of each style were identified, which made it possible to propose Ecodriving strategies at the operational level, i.e. directly related to the behaviour of the driver on the road. Finally, to estimate the potential savings derived from these strategies, five regression models with supervised learning were trained. Each model was evaluated using error indices such as MSE, RMSE and R2. The results indicated that the optimal model for estimation is Random forest because it has a RMSE of 4.99 L/100Km being the lowest error value and a coefficient of determination of 0.91 being the highest value obtained, which is an indicator of a high precision of the trained model. According to the results, it was concluded that Ecodriving allows a reduction of up to 30% in fuel consumption. This translates into economic savings for the driver and a reduction in the production of pollutant emissions.spa
dc.description.degreelevelPregradospa
dc.description.learningmodalityModalidad Presencialspa
dc.description.tableofcontents1.INTRODUCCIÓN ............................................................................................ 15 1.1 DEFINICIÓN DEL PROBLEMA ................................................................ 15 1.2 JUSTIFICACIÓN ........................................................................................... 15 2. OBJETIVOS ..................................................................................................... 19 2.1 OBJETIVO GENERAL .................................................................................. 19 2.2 OBJETIVOS ESPECÍFICOS ........................................................................ 19 3. ESTADO DEL ARTE ...................................................................................... 20 4. MARCO TEORICO ......................................................................................... 25 4.2 ESTILOS DE CONDUCCIÓN ....................................................................... 25 4.2.1 Factores que influyen en los estilos de conducción ............................... 25 4.2.2 Tipos de estilos de conducción .............................................................. 25 4.2.3 Tipos de clasificadores de estilos de conducción .................................. 26 4.1 ECO-DRIVING .............................................................................................. 27 4.1.1 Categorías de las guías de Eco-Driving: ................................................ 28 5. METODOLOGÍA ................................................................................................ 31 6. DESARROLLO .................................................................................................. 32 6.1 SELECCIÓN Y CARACTERIZACIÓN TÉCNICA DE LOS VEHÍCULOS ...... 32 6.2 CARACTERIZACIÓN DE LOS CONDUCTORES ........................................ 33 6.3 CAMPAÑA DE MONITOREO ....................................................................... 34 6.3.1 Selección de la ruta ............................................................................... 34 6.3.2 Selección de las variables ..................................................................... 35 6.3.3 Sistema de monitoreo a bordo ............................................................... 35 6.4 REGISTRO DE DATOS ................................................................................ 38 6.4.1 Adquisicion y transmisión de los datos .................................................. 39 6.4.2 Almacenamiento y envío de datos ......................................................... 42 6.5 ELIMINACIÓN DE DATOS ATÍPICOS ......................................................... 43 6.5.1 Filtrado ................................................................................................... 43 6.5.2 Edición ................................................................................................... 44 6.5.3 Adicción ................................................................................................. 45 6.6 METODOLOGÍA PARA LA SELECCIÓN DE PARÁMETROS CARACTERÍSTICOS .......................................................................................... 46 6.6.1 Segmentación ........................................................................................ 48 6.6.2 Calculo de CPS ..................................................................................... 49 6.6.3 Filtrado según correlación de las variables ............................................ 50 6.6.4 Escalado de datos ................................................................................. 51 6.6.5 Aplicación del modelo de selección de parámetros ............................... 51 6.6.6 Selección de características según importancia .................................... 52 6.7 METODOLOGÍA PARA LA CLASIFICACIÓN DE ESTILOS DE CONDUCCIÓN ................................................................................................... 53 6.7.1 Selección de las características para la clasificación............................. 54 6.7.2 Normalización de los datos .................................................................... 56 6.7.3 Construcción del algoritmo usando modelo DBSCAN ........................... 56 6.7.4 Construcción del algoritmo usando modelo K-means ............................ 59 6.7.5 Evaluación de los modelos .................................................................... 62 6.8 METODOLOGÍA PARA LA CUANTIFICACIÓN DEL CONSUMO DE COMBUSTIBLE .................................................................................................. 64 6.8.1 Construcción del modelo de predicción del consumo de combustible ... 65 6.9 POTENCIA ESPECÍFICA DEL VEHÍCULO (VSP) ....................................... 69 6.9.1 Proceso de obtención de obtención del VSP ......................................... 71 6.10 DISTRIBUCIÓN DE FERCUENCIA DE VELOCIDAD ACELERACIÓN (SAFD) ................................................................................................................ 71 6.10.1 Proceso de obtención del diagrama SAFD .......................................... 72 7. ANÁLISIS DE RESULTADOS ........................................................................... 73 7.1 BASE DE DATOS PROYECTO ACTUAL ..................................................... 73 7.2 BASE DE DATOS CONCATENADA ............................................................ 74 7.3 ANÁLISIS DE LA POTENCIA ESPECÍFICA DEL VEHIÍCULO .................... 75 7.4 ANÁLISIS DE LOS DIAGRAMAS SAFD ...................................................... 77 7.4.1 Análisis de la variable velocidad en los diagramas SAFD ..................... 77 7.4.2 Análisis de la variable aceleración en los diagramas SAFD .................. 78 7.4.3 Análisis general de los diagramas SAFD ............................................... 79 7.6 CLASIFICACIÓN DE ESTILOS DE CONDUCCIÓN ..................................... 79 7.6.1 Estilos de conducción de la base de datos concatenada....................... 80 7.6.2 Correlación de los estilos de conducción con el consumo de combustible ........................................................................................................................ 83 7.6.3 Correlación del consumo de combustible y los estilos de conducción para cada conductor ....................................................................................... 84 7.6.4 Correlación de variables con los estilos de conducción ......................... 85 7.7 ESTRATEGIAS DE ECODRIVING ............................................................... 87 7.7.1 Cuantificación del ahorro de las estrategias de Ecodriving .................... 88 8. CONCLUSIONES ........................................................................................... 91 9. RECOMENDACIONES................................................................................... 93 10. ANEXOS ...................................................................................................... 94 10.1 CÓDIGO PARA LA LIMPIEZA DE DATOS ATIPICOS .......................... 94 10.2 CÓDIGO PARA SELECCIÓN DE PARÁMETROS CARACTERÍSTICOS 95 10.3 CODIGO PARA LA CLASIFICACIÓN DE LOS ESTILOS DE CONDUCCIÓN ................................................................................................... 98 10.4 DASHBOARD PARA VISUALIZACIÓN DE LOS ESTILOS DE CONDUCCIÓN POR CONDUCTOR ................................................................ 103 10.5 CÓDIGO PARA LA ESTIMACIÓN/CUANTIFICACIÓN DEL CONSUMO DE COMBUSTIBLE .......................................................................................... 103 10.6 PROGRAMA CON ESTRATEGIAS DE ECO-DRIVING ...................... 110 BIBLIOGRAFÍA ................................................................................................... 111spa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameinstname:Universidad Autónoma de Bucaramanga - UNABspa
dc.identifier.reponamereponame:Repositorio Institucional UNABspa
dc.identifier.repourlrepourl:https://repository.unab.edu.cospa
dc.identifier.urihttp://hdl.handle.net/20.500.12749/23191
dc.language.isospaspa
dc.publisher.facultyFacultad Ingenieríaspa
dc.publisher.grantorUniversidad Autónoma de Bucaramanga UNABspa
dc.publisher.programPregrado Ingeniería Mecatrónicaspa
dc.publisher.programidIMK-1789
dc.relation.referencesAgencia Eureopea de Medio Ambiente, «Transporte,» 2020. [En línea].spa
dc.relation.referencesT. Colombo, G. Panzani, S. Savaresi y P. Paparo, «Absolute Driving Style Estimation for Ground Vehicles,» IEEE Conference on Control Technology and Applications (CCTA), 2017.spa
dc.relation.referencesÁrea metropolitana de Bucaramanga, «Balance mensual del control a fuentes moviles en Bucaramanga,Floridablanca,Piedecuesta y Giron:1350 pruebas realizadas;70% de los vehículos aprobados y 30% restante no cumplen la norma,» 2019. [En línea]. Available: https://www.amb.gov.co/balance-mensual-del-control-a-fuentes-moviles-en-bucaramanga-floridablanca-piedecuesta-y-giron-1350-pruebas-realizadas-70-de-los-vehiculos-aprobados-y-30-restante-no-cumple-la-norma/.spa
dc.relation.referencesP.Fafoutellis, E.G.Mantouka,E.I. Vlahogianni, «Eco-Driving and Its Impacts on Fuel Efficiency: An Overview of Technologies and Data-Driven Methods. Sustainability,» 2021.spa
dc.relation.referencesM. Sivak, B. Schoettle, «Eco-driving: Strategic, tactical, and operational decisions of the driver that influence vehicle fuel economy,» 2012.spa
dc.relation.referencesN.Xu,X. Li,Q. Liu,D. Zhao, «An Overview of Eco-Driving Theory, Capability Evaluation and Training Applications,» 2021.spa
dc.relation.referencesH.Wang, L.Fu,Y.Zhou,H.Li, «Modelling of the fuel consumption for passenger cars regarding,» Beijing, 2008.spa
dc.relation.referencesE. Choi. E. Kim, «Critical aggressive acceleration values and models for fuel consumption when starting and driving a passenger car running on LPG,» 2017.spa
dc.relation.referencesF. Saust, O. Bley, R. Kutzner, J. M. Wille, B. Friedrich and M. Maurer, «Exploitability of vehicle related sensor data in cooperative systems,» 13th International IEEE Conference on Intelligent Transportation Systems, 2010.spa
dc.relation.referencesA.Weber, A. Winckler, «Advanced traffic signal control algorithms, appendix A: Exploratory advanced research project BMW,» 2013.spa
dc.relation.referencesC. Rolim, P. Baptista, G. Duarte, T. Farias, «Impacts of On-board Devices and Training on Light Duty Vehicle Driving Behavior,» 2014.spa
dc.relation.referencesM. H.Almannaa, H. Chen, H. A. Rakha, A. Loulizi, and I. El-Shawarby, «Reducing Vehicle Fuel Consumption and Delay at Signalized Intersections: Controlled-Field Evaluation of Effectiveness of Infrastructure-to-Vehicle Communication,» 2017.spa
dc.relation.referencesP. Hao, G. Wu, K. Boriboonsomsin and M. J. Barth, «Eco-Approach and Departure (EAD) Application for Actuated Signals in Real-World TraffiC,» IEEE Transactions on Intelligent Transportation Systems, 2019.spa
dc.relation.referencesO. Teichert, A. Koch and A. Ongel, «Comparison of Eco-Driving Strategies for Different Traffic-Management Measures,» IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 2020.spa
dc.relation.referencesP.Arroyo-López, J. C. Velázquez-Martínez & K. M. Gámez-Pérez, «Past behavior as a predictor of eco-driving practices: The case of a sustained intervention in a Mexican transportation company,» 2021.spa
dc.relation.referencesJ. Caban, «The investigation of eco-driving possibilities in passenger car used in urban traffic,» 2021.spa
dc.relation.referencesD. Oswald et al, «Real-world Efficacy of a Haptic Accelerator Pedal-based Eco-driving System,» IEEE International Intelligent Transportation Systems Conference (ITSC), 2021.spa
dc.relation.referencesM-H.Yen,S-L. Tian,Y-T.Lin,C-W. Yang y C-C.Chen, «Combining a Universal OBD-II Module with Deep Learning to Develop an Eco-Driving Analysis System,» appl.sci, 2021.spa
dc.relation.referencesF.Kutzner,C.Kacperski,D.Schramm y M.Waenke, «How far can we get with eco driving tech?,» 2021.spa
dc.relation.referencesF. Martín Poó y R. D. Ledesma, «A Study on the Relationship Between Personality and driving styles,» 2013.spa
dc.relation.referencesJ. D. Molina y B. F. Acuña, «“Clasificación de estilos de conducción en el área metropolitana de Bucaramanga con monitoreo a bordo (OBDII) en condiciones reales de carretera,» 2022.spa
dc.relation.referencesJ. C. Ferreira, J. de Almeida and A. R. da Silva, «The Impact of Driving Styles on Fuel Consumption: A Data-Warehouse-and-Data-Mining-Based Discovery Process,» IEEE Transactions on Intelligent Transportation Systems, 2015.spa
dc.relation.referencesC. Lee y P. Öberg, «Classification of Road Type and Driving Style using OBD Data,» SAE 2015 World Congress & Exhibition, 2015.spa
dc.relation.referencesK.Sentoff,L.Aultman,B.Holmén, «Implications of driving style and road grade for accurate vehicle,» 2015.spa
dc.relation.referencesJ. E. Meseguer, C. K. Toh, C. T. Calafate, J. C. Cano and P. Manzoni, «Drivingstyles: a mobile platform for driving styles and fuel consumption characterization,» Journal of Communications and Networks, 2017.spa
dc.relation.referencesF. Martinelli, F. Mercaldo, A. Orlando, V. Nardone, A. Santone y A. Sangaiah, «Human behavior characterization for driving style recognition in vehicle system,» 2018.spa
dc.relation.referencesI. Feraud y J. Naranjo, «A Data-driven Approach to Estimate Driving Style,» ICCMS '19: Proceedings of the 11th International Conference on Computer Modeling and Simulation, 2019.spa
dc.relation.referencesJ. Fan, Y. Li, Y. Liu, Y. Zhang and C. Ma, «Analysis of taxi driving behavior and driving risk based on trajectory data,» IEEE Intelligent Vehicles Symposium (IV), 2019.spa
dc.relation.referencesB. Gao, K. Cai, T. Qu, Y. Hu and H. Chen, «Personalized Adaptive Cruise Control Based on Online Driving Style Recognition Technology and Model Predictive Control,» IEEE Transactions on Vehicular Technology, 2020.spa
dc.relation.referencesA. Mohammadnazar, R. Arvin y A. Khattak, «Classifying travelers’ driving style using basic safety messages generated by connected vehicles: Application of unsupervised machine learning,» 2021.spa
dc.relation.referencesM. Rafael, M. Sanchez, V. Mucino, J. Cervantes and A. Lozano, «Impact of driving styles on exhaust emissions and fuel economy from a heavy-duty truck: Laboratory tests,» Revista Internacional de Sistemas de Vehículos Pesados (IJHVS), 2006.spa
dc.relation.referencesC. Marina Martinez, M. Heucke, F. -Y. Wang, B. Gao and D. Cao, «Driving Style Recognition for Intelligent Vehicle Control and Advanced Driver Assistance: A Survey,» IEEE Transactions on Intelligent Transportation Systems, 2018.spa
dc.relation.referencesZ. Constantinescu, C. Marinoiu y M. Vladoiu, «Driving style analysis using data mining techniques,» International Journal of Computers Communications & Control, 2010.spa
dc.relation.referencesDe Zepeda, M. V. N., Meng, F., Su, J., Zeng, X.-J., & Wang, Q., Dynamic clustering analysis for driving styles identification, Engineering Applications of Artificial Intelligence, 2021.spa
dc.relation.referencesO. F. Ozgul, M. U. Cakir, M. Tan, M. F. Amasyali y H. T. Hayvaci, «Fully Unsupervised Framework for Scoring Driving Style,» 2018 International Conference on Intelligent Systems (IS), 2018.spa
dc.relation.referencesR. Yu, X. Long y J. Li, «Driving Style Analyses for Car-sharing Users Utilizing Low-frequency Trajectory Data,,» 5th International Conference on Transportation Information and Safety (ICTIS), 2019.spa
dc.relation.referencesH. Strömberg,I.C.Karlsson,O. Rexfelt, «Eco-driving:Drivers’ understanding of the concept and implications for future interventions,» Transport Policy, 2015.spa
dc.relation.referencesR.Tu,J.XU,T.Li y H.Chen, «Effective and Acceptable Eco-Driving Guidance for Human-Driving Vehicles: A Review,» Int. J. Environ. Res. Public Health, 2022.spa
dc.relation.referencesY. Yao, X. Zhao, C. Liu, J. Rong, Y. Zhang, Z. Dong y Y. Su, Vehicle Fuel Consumption Prediction Method Based on Driving Behavior Data Collected from Smartphones, 2020.spa
dc.relation.referencesA. G. Çapraz, P. Özel, M. Şevkli y Ö. F. Beyca, Fuel Consumption Models Applied to Automobiles Using Real-time Data: A Comparison of Statistical Models, Procedia Comput, 2016, p. pp. 774–781.spa
dc.relation.referencesS. Wickramanayake and H. M. N. Dilum Bandara, «"Fuel consumption prediction of fleet vehicles using Machine Learning: A comparative study,» de 2016 Moratuwa Engineering Research Conference (MERCon), Moratuwa, Sri Lanka, 2016.spa
dc.relation.referencesG. Li, «Machine learning in fuel consumption prediction of aircraft,» de Cogn. Inform. (ICCI), Beijing, China, 2010.spa
dc.relation.referencesF. Schockenhoff, H. Nehse y M. Lienkamp, «Maneuver-based objectification of user comfort affecting aspects of driving style of autonomous vehicle concepts,» Applied Sciences, 2020.spa
dc.relation.referencesP. Jardin, I. Moisidis, S. S. Zetina y S. Rinderknecht, «Rule-Based Driving Style Classification Using Acceleration Data Profiles,» 23rd International Conference on Intelligent Transportation Systems (ITSC), 2020.spa
dc.relation.referencesM. M. Bejani y M. Ghatee, «Convolutional neural network with adaptive regularization to classify driving styles on smartphones,» IEEE transactions on intelligent transportation systems, 2019.spa
dc.relation.referencesO. Shouno, «Deep unsupervised learning of a topological map of vehicle maneuvers for characterizing driving styles,» 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 2018.spa
dc.relation.referencesI. Del Campo, E. Asua, V. Martínez, Ó. Mata-Carballeira y J. Echanobe, «Driving style recognition based on ride comfort using a hybrid machine learning algorithm,» 21st International Conference on Intelligent Transportation Systems (ITSC), 2018.spa
dc.relation.referencesM. Brambilla, P. Mascetti y A. Mauri, «Comparison of different driving style analysis approaches based on trip segmentation over GPS information,» IEEE International Conference on Big Data (Big Data), 2017.spa
dc.relation.referencesD. Dörr, D. Grabengiesser y F. Gauterin, «Online driving style recognition using fuzzy logic,» IEEE International Conference on Intelligent Trasportation Systems, 2014.spa
dc.relation.referencesA. Aljaafreh, N. Alshabatat y Al-Din, «Driving style recognition using fuzzy logic,» International Conference on Vehicular Electronics and Safety, 2012spa
dc.relation.referencesG. Li, F. Zhu, X. Qu, B. Cheng, S. Li y P. Green, «Driving style classification based on driving operational pictures,» IEEE Access, 2019.spa
dc.relation.referencesA. Donkers, D. Yang y M. Viktorović, «Influence of driving style, infrastructure, weather and traffic on electric vehicle performance,» Transportation research part D: transport and environment, 2020.spa
dc.relation.referencesJ. Guo, Y. Jiang, Y. Yu y W. Liu, «A novel energy consumption prediction model with combination of road information and driving style of BEVs,» Sustainable Energy Technologies and Assessments, 2020.spa
dc.relation.referencesI. Silva y J. Eugenio Naranjo, «A systematic methodology to evaluate prediction models for driving style classification,» Sensors, 2020.spa
dc.relation.referencesJ. Cordero, J. Aguilar, K. Aguilar, D. Chávez y E. Puerto, «Recognition of the driving style in vehicle drivers,» Sensors, 2020.spa
dc.relation.referencesY. Liu, J. Wang, P. Zhao, D. Qin y Z. Chen, «Research on classification and recognition of driving styles based on feature engineering,» IEEE Access, 2019.spa
dc.relation.referencesR. Liessner, A. Dietermann, B. Bäker y K. Lüpkes, «Derivation of real-world driving cycles corresponding to traffic situation and driving style on the basis of Markov models and cluster analyses,» th Hybrid and Electric Vehicles Conference (HEVC 2016), 2016.spa
dc.relation.referencesV. Nikulin, «Driving style identification with unsupervised learning,» International Conference on Machine Learning and Data Mining in Pattern Recognition, 2016.spa
dc.relation.referencesK. Li, L. Jin, Y. Jiang, H. Xian y L. Gao, «Effects of driver behavior style differences and individual differences on driver sleepiness detection,» Advances in Mechanical Engineering, 2015.spa
dc.relation.referencesK. M. Sentoff, L. Aultman-Hall y B. A. Holmén, «Implications of driving style and road grade for accurate vehicle activity data and emissions estimates,» Transportation Research Part D: Transport and Environment, 2015.spa
dc.relation.referencesO. Derbel y R. Landry, «Driving style assessment based on the GPS data and fuzzy inference systems,» 12th International Multi-Conference on Systems, Signals & Devices, 2015.spa
dc.relation.referencesD. Johnson y M. Trivedi, «Driving style recognition using a smartphone as a sensor platform,» 14th International IEEE Conference on Intelligent Transportation Systems, 2011spa
dc.relation.referencesT. Felstead, M. McDonald y M. Fowkes, «Driving style extremes and potential vehicle emission effects,» Proceedings of the Institution of Civil Engineers-Transport, 2009.spa
dc.relation.referencesJ. Gallus, U. Kirchner, R. Vogt y T. Benter, «Impact of driving style and road grade on gaseous exhaust emissions of passenger vehicles measured by a Portable Emission Measurement System (PEMS),» Transportation Research Part D: Transport and Environment, 2017.spa
dc.relation.referencesY. Feng, S. Pickering, E. Chappell, P. Iravani y C. Brace, «A support vector clustering based approach for driving style classification,» International Journal of Machine Learning and Computing, 2019.spa
dc.relation.referencesW. Han, W. Wang, X. Li y J. Xi, «Statistical-based approach for driving style recognition using Bayesian probability with kernel density estimation,» IET Intelligent Transport Systems, 2019.spa
dc.relation.referencesY. Feng, S. Pickering, E. Chappell, P. Iravani y C. Brace, «Driving Style Modelling with Adaptive Neuro-Fuzzy Inference System and Real Driving Data,» Conference on Applied Human Factors and Ergonomics, 2018.spa
dc.relation.referencesG. Li, S. E. Li, B. Cheng y P. Green, «Estimation of driving style in naturalistic highway traffic using maneuver transition probabilities,» Transportation Research Part C: Emerging Technologies, 2017.spa
dc.relation.referencesM. Karaduman y H. Eren, «Deep learning based traffic direction sign detection and determining driving style,» 2017 International Conference on Computer Science and Engineering, 2017.spa
dc.relation.referencesF. Jiménez, J. C. Amarillo, J. E. Naranjo, F. Serradilla y A. Díaz, «Energy consumption estimation in electric vehicles considering driving style,,» IEEE 18th International Conference on Intelligent Transportation Systems, 2015.spa
dc.relation.referencesC. Deng, C. Wu, N. Lyu y Z. Huang, «Driving style recognition method using braking characteristics based on hidden Markov model,» PLOS ONE, 2017.spa
dc.relation.referencesY. Shi, N. Cui y Y. Du, «Energy Management Strategy based on Driving Style Recognition for Plug-in Hybrid Electric Bus,» 2020 39th Chinese Control Conference, 2020.spa
dc.relation.referencesMensing, F., Bideaux, E., Trigui, R., Ribet, J., & Jeanneret, B., «Mensing, F., Bideaux, E., Trigui, R., Ribet, J., & Jeanneret, B. (2014). Eco-driving: An economic or ecologic driving style? Transportation Research Part C: Emerging Technologies, 38, 110–121. doi:10.1016/j.trc.2013.10.013 ,» Transportation Research Part C: Emerging Technologies, 2014.spa
dc.relation.referencesMayakuntla, S. K., & Verma, A, «A novel methodology for construction of driving cycles for Indian cities,» Transportation Research Part D: Transport and Environment, 2018.spa
dc.relation.referencesH. Ullerichs, L. Poulsgaard y P. Sørensen, Clustering Based On Driving Style Using Hot Paths.spa
dc.relation.referencesS. J. Navarro & R. A., Desarrollo de un ciclo de conducción bajo condiciones reales en el área metropolitana de Bucaramanga, 2021spa
dc.relation.referencesY.Huanga, E. C. Y. Ng, J.L Zhou, N.C. Surawskia, E.F Chanb, & G. Hong, «Eco-driving technology for sustainable road transport: A review,» 2018.spa
dc.relation.referencesT. Colombo, G. Panzani, S. Savaresi y P. Paparo, «Absolute driving style estimation for ground vehicles,» IEEE conference on control technology and applications, 2017.spa
dc.relation.uriapolohttps://apolo.unab.edu.co/en/persons/jessica-gissella-maradey-l%C3%A1zarospa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.creativecommonsAtribución-NoComercial-SinDerivadas 2.5 Colombia*
dc.rights.localAbierto (Texto Completo)spa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/*
dc.subject.keywordsDriving stylesspa
dc.subject.keywordsEco-drivingspa
dc.subject.keywordsRandom forestspa
dc.subject.keywordsVehicle drivingspa
dc.subject.keywordsEnergetic resourcesspa
dc.subject.keywordsEnergy conservationspa
dc.subject.keywordsAutomobilesspa
dc.subject.lembMecatrónicaspa
dc.subject.lembConducción de vehículosspa
dc.subject.lembRecursos energéticosspa
dc.subject.lembConservación de la energíaspa
dc.subject.lembAutomóvilesspa
dc.subject.proposalEstilos de conducciónspa
dc.subject.proposalConducción ecológicaspa
dc.subject.proposalPCAspa
dc.subject.proposalKmeansspa
dc.subject.proposalRandom forestspa
dc.titleDesarrollo de un programa de Eco-Driving a nivel operativo con monitoreo a bordo OBD II en el Área Metropolitana de Bucaramangaspa
dc.title.translatedDevelopment of an Eco-Driving program at an operational level with on-board OBD II monitoring in the metropolitan area of Bucaramangaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_7a1f
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.driverinfo:eu-repo/semantics/bachelorThesis
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersion
dc.type.localTrabajo de Gradospa
dc.type.redcolhttp://purl.org/redcol/resource_type/TP

Archivos

Bloque original

Mostrando 1 - 2 de 2
Cargando...
Miniatura
Nombre:
Tesis.pdf
Tamaño:
4.36 MB
Formato:
Adobe Portable Document Format
Descripción:
Tesis
Cargando...
Miniatura
Nombre:
Licencia.pdf
Tamaño:
316.45 KB
Formato:
Adobe Portable Document Format
Descripción:
Licencia

Bloque de licencias

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
license.txt
Tamaño:
829 B
Formato:
Item-specific license agreed upon to submission
Descripción: