Show simple item record

dc.creatoramelec, viloria
dc.creatorPineda, Omar
dc.creatorVarela Izquierdo, Noel
dc.creatorDiaz Martínez, Jorge Luis
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
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universal*
dc.sourceCommunications in Computer and Information Sciencespa
dc.subjectMachine Learningspa
dc.subjectProactive controlspa
dc.subjectSmart citiesspa
dc.subjectAutonomous Computingspa
dc.titleOptimization of driving efficiency for pre-determined routes: proactive vehicle traffic controlspa
dcterms.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
dcterms.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
dcterms.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).
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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).
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.references34. Chaubey, N.: Security analysis of Vehicular Ad Hoc Networks (VANETs): a comprehensive study. Int. J. Secur. Appl. 10, 261–274 (2016)spa
dcterms.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

Files in this item


This item appears in the following Collection(s)

Show simple item record

CC0 1.0 Universal
Except where otherwise noted, this item's license is described as CC0 1.0 Universal