Manzoni et al 2013a - Revision history

Scipediacontent at 16:59, 28 January 2021

2021-01-28T16:59:35Z

Scipediacontent: Scipediacontent moved page Draft Content 898984165 to Manzoni et al 2013a

2021-01-21T12:11:03Z

Scipediacontent moved page Draft Content 898984165 to Manzoni et al 2013a

Scipediacontent: Created page with " == Abstract == Â© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, includin..."

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== Abstract ==

Â© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works [EN] A critical issue, especially in urban areas, is the occurrence of traffic accidents, since it could generate traffic jams. Additionally, these traffic jams will negatively affect to the rescue process, increasing the emergency services arrival time, which can determine the difference between life or death for injured people involved in the accident. In this paper, we propose four different approaches addressing the traffic congestion problem, comparing them to obtain the best solution. Using V2I communications, we are able to accurately estimate the traffic density in a certain area, which represents a key parameter to perform efficient traffic redirection, thereby reducing the emergency services arrival time, and avoiding traffic jams when an accident occurs. Specifically, we propose two approaches based on the Dijkstra algorithm, and two approaches based on Evolution Strategies. Results indicate that the Density-Based Evolution Strategy system is the best one among all the proposed solutions, since it offers the lowest emergency services travel times. This work was partially supported by the Ministerio de Ciencia e InnovaciÃ³m , Spain, under Grant TIN2011-27543-C03-01, as well as by the FundaciÃ³n Universitaria Antonio Gargallo, the Obra Social de Ibercaja, the Government of Aragon, and the European Social Fund (T91 Research Group). Barrachina Villalba, J.; Garrido, P.; Fogue, M.; MartÃnez, FJ.; Cano EscribÃ¡, JC.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2013). Using evolution strategies to reduce emergency services arrival time in case of accident. En 2013 IEEE 25th International Conference on Tools with Artificial Intelligence. IEEE. 833-840. doi:10.1109/ICTAI.2013.127 S 833 840

== Original document ==

The different versions of the original document can be found in:

* [http://hdl.handle.net/10251/67557 http://hdl.handle.net/10251/67557]

* [https://riunet.upv.es/bitstream/10251/67557/1/paper_ICTAI_barrachina_v2.pdf https://riunet.upv.es/bitstream/10251/67557/1/paper_ICTAI_barrachina_v2.pdf]

* [http://xplorestaging.ieee.org/ielx7/6734837/6735210/06735338.pdf?arnumber=6735338 http://xplorestaging.ieee.org/ielx7/6734837/6735210/06735338.pdf?arnumber=6735338],
: [http://dx.doi.org/10.1109/ictai.2013.127 http://dx.doi.org/10.1109/ictai.2013.127]

* [https://dblp.uni-trier.de/db/conf/ictai/ictai2013.html#BarrachinaGFMCCM13 https://dblp.uni-trier.de/db/conf/ictai/ictai2013.html#BarrachinaGFMCCM13],
: [https://riunet.upv.es/handle/10251/67557 https://riunet.upv.es/handle/10251/67557],
: [https://www.computer.org/csdl/proceedings/ictai/2013/2972/00/2971a833.pdf https://www.computer.org/csdl/proceedings/ictai/2013/2972/00/2971a833.pdf],
: [https://academic.microsoft.com/#/detail/2094339176 https://academic.microsoft.com/#/detail/2094339176]

← Older revision		Revision as of 16:59, 28 January 2021
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	== Abstract ==		== Abstract ==

−	Â© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works [EN] A critical issue, especially in urban areas, is the occurrence of traffic accidents, since it could generate traffic jams. Additionally, these traffic jams will negatively affect to the rescue process, increasing the emergency services arrival time, which can determine the difference between life or death for injured people involved in the accident. In this paper, we propose four different approaches addressing the traffic congestion problem, comparing them to obtain the best solution. Using V2I communications, we are able to accurately estimate the traffic density in a certain area, which represents a key parameter to perform efficient traffic redirection, thereby reducing the emergency services arrival time, and avoiding traffic jams when an accident occurs. Specifically, we propose two approaches based on the Dijkstra algorithm, and two approaches based on Evolution Strategies. Results indicate that the Density-Based Evolution Strategy system is the best one among all the proposed solutions, since it offers the lowest emergency services travel times. This work was partially supported by the Ministerio de Ciencia e ~~InnovaciÃ³m~~ , Spain, under Grant TIN2011-27543-C03-01, as well as by the ~~FundaciÃ³n~~ Universitaria Antonio Gargallo, the Obra Social de Ibercaja, the Government of Aragon, and the European Social Fund (T91 Research Group). Barrachina Villalba, J.; Garrido, P.; Fogue, M.; ~~MartÃnez~~, FJ.; Cano ~~EscribÃ¡~~, JC.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2013). Using evolution strategies to reduce emergency services arrival time in case of accident. En 2013 IEEE 25th International Conference on Tools with Artificial Intelligence. IEEE. 833-840. doi:10.1109/ICTAI.2013.127 S 833 840	+	© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works [EN] A critical issue, especially in urban areas, is the occurrence of traffic accidents, since it could generate traffic jams. Additionally, these traffic jams will negatively affect to the rescue process, increasing the emergency services arrival time, which can determine the difference between life or death for injured people involved in the accident. In this paper, we propose four different approaches addressing the traffic congestion problem, comparing them to obtain the best solution. Using V2I communications, we are able to accurately estimate the traffic density in a certain area, which represents a key parameter to perform efficient traffic redirection, thereby reducing the emergency services arrival time, and avoiding traffic jams when an accident occurs. Specifically, we propose two approaches based on the Dijkstra algorithm, and two approaches based on Evolution Strategies. Results indicate that the Density-Based Evolution Strategy system is the best one among all the proposed solutions, since it offers the lowest emergency services travel times. This work was partially supported by the Ministerio de Ciencia e Innovacióm , Spain, under Grant TIN2011-27543-C03-01, as well as by the Fundación Universitaria Antonio Gargallo, the Obra Social de Ibercaja, the Government of Aragon, and the European Social Fund (T91 Research Group). Barrachina Villalba, J.; Garrido, P.; Fogue, M.; Martínez, FJ.; Cano Escribá, JC.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2013). Using evolution strategies to reduce emergency services arrival time in case of accident. En 2013 IEEE 25th International Conference on Tools with Artificial Intelligence. IEEE. 833-840. doi:10.1109/ICTAI.2013.127 S 833 840

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