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dc.contributor.authoramelec, viloriaspa
dc.contributor.authorPineda, Omarspa
dc.contributor.authorVarela Izquierdo, Noelspa
dc.contributor.authorDiaz Martínez, Jorge Luisspa
dc.date.accessioned2021-03-17T17:13:10Z
dc.date.available2021-03-17T17:13:10Z
dc.date.issued2020-07-19
dc.identifier.issn18650929spa
dc.identifier.urihttps://hdl.handle.net/11323/8037spa
dc.description.abstractWith the excessive growth of modern cities, great problems are generated in citizen administration. One of these problems is the control of vehicle flow during peak hours. This paper proposes a solution to the problem of vehicle control through a proactive approach based on Machine Learning. Through this solution, a traffic control system learns about traffic flow in order to prevent future problems of long queues at traffic lights. The architecture of the traffic system is based on the principles of Autonomous Computing with the aim of changing the traffic light timers automatically. A simulation of the roads in an intelligent city and a Weka-based tool were created to validate this approach.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoeng
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universalspa
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/spa
dc.sourceCommunications in Computer and Information Sciencespa
dc.subjectMachine Learningspa
dc.subjectProactive controlspa
dc.subjectTrafficspa
dc.subjectSmart citiesspa
dc.subjectAutonomous Computingspa
dc.titleOptimization of driving efficiency for pre-determined routes: proactive vehicle traffic controlspa
dc.typePre-Publicaciónspa
dc.source.urlhttps://link.springer.com/chapter/10.1007/978-981-15-6648-6_7spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1007/978-981-15-6648-6_7spa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.publisher.programRetractedspa
dc.relation.references1. Mittal, A., Ostojic, M., Mahmassani, H.S.: Active traffic signal control for mixed vehicular traffic in connected environments: self-identifying platoon strategy, (No. 19-05931 (2019)spa
dc.relation.references2. Fang, J., Ye, H., Easa, S.M.: Modified traffic flow model with connected vehicle microscopic data for proactive variable speed limit control. J. Adv. Transp. 2019, 18 (2019)spa
dc.relation.references4. Lum, C., Koper, C.S., Wu, X., Johnson, W., Stoltz, M.: Examining the empirical realities of proactive policing through systematic observations and computer-aided dispatch data. Police Q. (2020). https://doi.org/10.1177/1098611119896081spa
dc.relation.references5. Ferenchak, N.N., Marshall, W.E.: Equity analysis of proactively-vs. reactively-identified traffic safety issues. Transp. Res. Record 2673(7), 596–606 (2019)spa
dc.relation.references6. Xie, K., Ozbay, K., Yang, H., Li, C.: Mining automatically extracted vehicle trajectory data for proactive safety analytics. Transp. Res. Part C: Emerg. Technol. 106, 61–72 (2019)spa
dc.relation.references7. Azari, A., Papapetrou, P., Denic, S., Peters, G.: User traffic prediction for proactive resource management: learning-powered approaches. arXiv preprint arXiv:1906.00951 (2019)spa
dc.relation.references8. Gillani, R., Nasir, A.: Proactive control of hybrid electric vehicles for maximum fuel efficiency. In: 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST), pp. 396–401. IEEE (2019)spa
dc.relation.references9. Bui, D.P., et al.: The use of proactive risk management to reduce emergency service vehicle crashes among firefighters. J. Saf. Res. 71, 103–109 (2019)spa
dc.relation.references10. Batkovic, I., Zanon, M., Ali, M., & Falcone, P. Real-time constrained trajectory planning and vehicle control for proactive autonomous driving with road users. In: 2019 18th European Control Conference (ECC), pp. 256–262. IEEE (2019)spa
dc.relation.references11. Lee, D., Tak, S., Choi, S., Yeo, H.: Development of risk predictive collision avoidance system and its impact on traffic and vehicular safety. Transp. Res. Record 2673(7), 454–465 (2019)spa
dc.relation.references12. Fuentes, A.: Proactive decision support tools for national park and non-traditional agencies in solving traffic-related problems. Doctoral dissertation, Virginia Tech (2019)spa
dc.relation.references13. Kathuria, A., Vedagiri, P.: Evaluating pedestrian vehicle interaction dynamics at un-signalized intersections: a proactive approach for safety analysis. Accid. Anal. Prev. 134, 105316 (2020)spa
dc.relation.references14. Hu, Y., Chen, C., He, T., He, J., Guan, X., Yang, B.: Proactive power management scheme for hybrid electric storage system in EVs: an MPC method. IEEE Trans. Intell. Transp. Syst. (2019)spa
dc.relation.references15. Silva, R., Couturier, C., Ernst, T., Bonnin, J.M.: Proactive decision making for ITS communication. In: Global Advancements in Connected and Intelligent Mobility: Emerging Research and Opportunities, pp. 197–226. IGI Global (2020)spa
dc.relation.references16. Formosa, N., Quddus, M., Ison, S., Abdel-Aty, M., Yuan, J.: Predicting real-time traffic conflicts using deep learning. Accid. Anal. Prev. 136, 105429 (2020)spa
dc.relation.references17. Zahid, M., Chen, Y., Jamal, A., Memon, M.Q.: Short term traffic state prediction via hyperparameter optimization based classifiers. Sensors 20(3), 685 (2020)spa
dc.relation.references18. Viloria, A., Acuña, G.C., Franco, D.J.A., Hernández-Palma, H., Fuentes, J.P., Rambal, E.P.: Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019)spa
dc.relation.references19. Paranjothi, A., Khan, M.S., Patan, R., Parizi, R.M., Atiquzzaman, M.: VANETomo: a congestion identification and control scheme in connected vehicles using network tomography. Comput. Commun. 151, 275–289 (2020)spa
dc.relation.references20. Zahed, M.I.A., Ahmad, I., Habibi, D., Phung, Q.V., Mowla, M.M.: Proactive content caching using surplus renewable energy: a win–win solution for both network service and energy providers. Future Gener. Comput. Syst. 105, 210–221 (2020)spa
dc.relation.references21. Perez, R., Vásquez, C., Viloria, A.: An intelligent strategy for faults location in distribution networks with distributed generation. J. Intell. Fuzzy Syst. 36(2), 1627–1637 (2019)spa
dc.relation.references22. Viloria, A., Robayo, P.V.: Virtual network level of application composed IP networks connected with systems-(NETS Peer-to-Peer). Indian J. Sci. Technol. 9, 46 (2016)spa
dc.relation.references23. Liu, J., Khattak, A.: Informed decision-making by integrating historical on-road driving performance data in high-resolution maps for connected and automated vehicles. J. Intell. Transp. Syst. 24(1), 11–23 (2020)spa
dc.relation.references24. Rivoirard, L., Wahl, M., Sondi, P.: Multipoint relaying versus chain-branch-leaf clustering performance in optimized link state routing-based vehicular ad hoc networks. IEEE Trans. Intell. Transp. Syst. 21, 1034–1043 (2019)spa
dc.relation.references25. Ramezani, M., Ye, E.: Lane density optimisation of automated vehicles for highway congestion control. Transportmetrica B: Transp. Dyn. 7(1), 1096–1116 (2019)spa
dc.relation.references26. Rahman, M., et al.: A review of sensing and communication, human factors, and controller aspects for information-aware connected and automated vehicles. IEEE Trans. Intell. Transp. Syst. 21(1), 7–29 (2020)spa
dc.relation.references27. de Souza, A.M., Braun, T., Botega, L.C., Cabral, R., Garcia, I.C., Villas, L.A.: Better safe than sorry: a vehicular traffic re-routing based on traffic conditions and public safety issues. J. Internet Serv. Appl. 10(1), 17 (2019)spa
dc.relation.references28. Vijayaraghavan, V., Rian Leevinson, J.: Intelligent traffic management systems for next generation IoV in smart city scenario. In: Mahmood, Z. (ed.) Connected Vehicles in the Internet of Things, pp. 123–141. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-36167-9_6spa
dc.relation.references29. Wu, Y., Tan, H., Peng, J., Zhang, H., He, H.: Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus. Appl. Energy 247, 454–466 (2019)spa
dc.relation.references30. Chen, X., He, X., Xiong, C., Zhu, Z., Zhang, L.: A bayesian stochastic kriging optimization model dealing with heteroscedastic simulation noise for freeway traffic management. Transp. Sci. 53(2), 545–565 (2019)spa
dc.relation.references32. Balouchzahi, N.M., Rajaei, M.: Efficient traffic information dissemination and vehicle navigation for lower travel time in urban scenario using vehicular networks. Wirel. Personal Commun. 106(2), 633–649 (2019)spa
dc.relation.references33. Xu, H., Liu, J., Qian, C., Huang, H., Qiao, C.: Reducing controller response time with hybrid routing in software defined networks. Comput. Netw. 164, 106891 (2019)spa
dc.relation.references34. Chaubey, N.: Security analysis of Vehicular Ad Hoc Networks (VANETs): a comprehensive study. Int. J. Secur. Appl. 10, 261–274 (2016)spa
dc.relation.references35. Chaubey, N.K., Yadav, D.: A taxonomy of sybil attacks in Vehicular Ad-Hoc Network (VANET). In: Rao, R., Jain, V., Kaiwartya, O., Singh, N. (eds.) IoT and Cloud Computing Advancements in Vehicular Ad-Hoc Networks, pp. 174–190. IGI Global, Hershey (2020).spa
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dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa


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