Optimization of driving efficiency for pre-determined routes: proactive vehicle traffic control

With 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...

Full description

Autores:
amelec, viloria
Pineda, Omar
Varela Izquierdo, Noel
Diaz Martínez, Jorge Luis
Tipo de recurso:
http://purl.org/coar/resource_type/c_816b
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/8037
Acceso en línea:
https://hdl.handle.net/11323/8037
https://doi.org/10.1007/978-981-15-6648-6_7
https://repositorio.cuc.edu.co/
Palabra clave:
Machine Learning
Proactive control
Traffic
Smart cities
Autonomous Computing
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_ebc666ec424c0dd33a4f9ea295d2313b
oai_identifier_str oai:repositorio.cuc.edu.co:11323/8037
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Optimization of driving efficiency for pre-determined routes: proactive vehicle traffic control
title Optimization of driving efficiency for pre-determined routes: proactive vehicle traffic control
spellingShingle Optimization of driving efficiency for pre-determined routes: proactive vehicle traffic control
Machine Learning
Proactive control
Traffic
Smart cities
Autonomous Computing
title_short Optimization of driving efficiency for pre-determined routes: proactive vehicle traffic control
title_full Optimization of driving efficiency for pre-determined routes: proactive vehicle traffic control
title_fullStr Optimization of driving efficiency for pre-determined routes: proactive vehicle traffic control
title_full_unstemmed Optimization of driving efficiency for pre-determined routes: proactive vehicle traffic control
title_sort Optimization of driving efficiency for pre-determined routes: proactive vehicle traffic control
dc.creator.fl_str_mv amelec, viloria
Pineda, Omar
Varela Izquierdo, Noel
Diaz Martínez, Jorge Luis
dc.contributor.author.spa.fl_str_mv amelec, viloria
Pineda, Omar
Varela Izquierdo, Noel
Diaz Martínez, Jorge Luis
dc.subject.spa.fl_str_mv Machine Learning
Proactive control
Traffic
Smart cities
Autonomous Computing
topic Machine Learning
Proactive control
Traffic
Smart cities
Autonomous Computing
description With 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.
publishDate 2020
dc.date.issued.none.fl_str_mv 2020-07-19
dc.date.accessioned.none.fl_str_mv 2021-03-17T17:13:10Z
dc.date.available.none.fl_str_mv 2021-03-17T17:13:10Z
dc.type.spa.fl_str_mv Pre-Publicación
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_816b
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dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
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status_str acceptedVersion
dc.identifier.issn.spa.fl_str_mv 18650929
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/8037
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1007/978-981-15-6648-6_7
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 18650929
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/8037
https://doi.org/10.1007/978-981-15-6648-6_7
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1. 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)
2. 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)
4. 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/10.1177/1098611119896081
5. Ferenchak, N.N., Marshall, W.E.: Equity analysis of proactively-vs. reactively-identified traffic safety issues. Transp. Res. Record 2673(7), 596–606 (2019)
6. 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)
7. Azari, A., Papapetrou, P., Denic, S., Peters, G.: User traffic prediction for proactive resource management: learning-powered approaches. arXiv preprint arXiv:1906.00951 (2019)
8. 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)
9. 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)
10. 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)
11. 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)
12. Fuentes, A.: Proactive decision support tools for national park and non-traditional agencies in solving traffic-related problems. Doctoral dissertation, Virginia Tech (2019)
13. 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)
14. 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)
15. 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)
16. 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)
17. Zahid, M., Chen, Y., Jamal, A., Memon, M.Q.: Short term traffic state prediction via hyperparameter optimization based classifiers. Sensors 20(3), 685 (2020)
18. 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)
19. 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)
20. 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)
21. 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)
22. 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)
23. 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)
24. 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)
25. Ramezani, M., Ye, E.: Lane density optimisation of automated vehicles for highway congestion control. Transportmetrica B: Transp. Dyn. 7(1), 1096–1116 (2019)
26. 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)
27. 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)
28. 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_6
29. 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)
30. 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)
32. 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)
33. 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)
34. Chaubey, N.: Security analysis of Vehicular Ad Hoc Networks (VANETs): a comprehensive study. Int. J. Secur. Appl. 10, 261–274 (2016)
35. 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).
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spelling amelec, viloriaPineda, OmarVarela Izquierdo, NoelDiaz Martínez, Jorge Luis2021-03-17T17:13:10Z2021-03-17T17:13:10Z2020-07-1918650929https://hdl.handle.net/11323/8037https://doi.org/10.1007/978-981-15-6648-6_7Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/With 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.amelec, viloria-will be generated-orcid-0000-0003-2673-6350-600Pineda, Omar-will be generated-orcid-0000-0002-8239-3906-600Varela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600Diaz Martínez, Jorge Luisapplication/pdfengCorporación Universidad de la CostaRetractedCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Communications in Computer and Information Sciencehttps://link.springer.com/chapter/10.1007/978-981-15-6648-6_7Machine LearningProactive controlTrafficSmart citiesAutonomous ComputingOptimization of driving efficiency for pre-determined routes: proactive vehicle traffic controlPre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersion1. 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)2. 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)4. 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/10.1177/10986111198960815. Ferenchak, N.N., Marshall, W.E.: Equity analysis of proactively-vs. reactively-identified traffic safety issues. Transp. Res. Record 2673(7), 596–606 (2019)6. 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)7. Azari, A., Papapetrou, P., Denic, S., Peters, G.: User traffic prediction for proactive resource management: learning-powered approaches. arXiv preprint arXiv:1906.00951 (2019)8. 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)9. 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)10. 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)11. 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)12. Fuentes, A.: Proactive decision support tools for national park and non-traditional agencies in solving traffic-related problems. Doctoral dissertation, Virginia Tech (2019)13. 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)14. 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)15. 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)16. 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)17. Zahid, M., Chen, Y., Jamal, A., Memon, M.Q.: Short term traffic state prediction via hyperparameter optimization based classifiers. Sensors 20(3), 685 (2020)18. 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)19. 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)20. 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)21. 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)22. 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)23. 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)24. 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)25. Ramezani, M., Ye, E.: Lane density optimisation of automated vehicles for highway congestion control. Transportmetrica B: Transp. Dyn. 7(1), 1096–1116 (2019)26. 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)27. 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)28. 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_629. 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)30. 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)32. 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)33. 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)34. Chaubey, N.: Security analysis of Vehicular Ad Hoc Networks (VANETs): a comprehensive study. Int. J. Secur. Appl. 10, 261–274 (2016)35. 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. 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