(Created page with " == Abstract == Solving traffic congestion problems in intersections is a complex logistic problem that usually consists in building infrastructures, such as bridges, tunnels...")
 
 
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Solving traffic congestion problems in intersections is a complex logistic problem that usually consists in building infrastructures, such as bridges, tunnels, or roundabouts, which represent the costliest solutions. Concerning the case of roundabouts, many parameters influence its performance, e.g. geometry and size. Thus, other less costlier solutions should be pondered. This paper introduces a low-cost approach to traffic intersections, by using pre-signals; and conducts simulation experiments to verify if this approach could be used to improve the performance of traffic intersections. In this sense, an agent-based traffic simulation model was developed that applies the object modelling paradigm of Simio to model the individual behaviour of vehicles. The simulation experiments results indicate that the flow of vehicles can be increased up to 20%, reducing the average queue sizes and crossing time per vehicle and saving the fuel consumed up to 64%. (undefined) info:eu-repo/semantics/publishedVersion
 
Solving traffic congestion problems in intersections is a complex logistic problem that usually consists in building infrastructures, such as bridges, tunnels, or roundabouts, which represent the costliest solutions. Concerning the case of roundabouts, many parameters influence its performance, e.g. geometry and size. Thus, other less costlier solutions should be pondered. This paper introduces a low-cost approach to traffic intersections, by using pre-signals; and conducts simulation experiments to verify if this approach could be used to improve the performance of traffic intersections. In this sense, an agent-based traffic simulation model was developed that applies the object modelling paradigm of Simio to model the individual behaviour of vehicles. The simulation experiments results indicate that the flow of vehicles can be increased up to 20%, reducing the average queue sizes and crossing time per vehicle and saving the fuel consumed up to 64%. (undefined) info:eu-repo/semantics/publishedVersion
 
Document type: Part of book or chapter of book
 
 
== Full document ==
 
<pdf>Media:Draft_Content_618012227-beopen14222-3965-document.pdf</pdf>
 
  
  
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Latest revision as of 14:26, 21 January 2021

Abstract

Solving traffic congestion problems in intersections is a complex logistic problem that usually consists in building infrastructures, such as bridges, tunnels, or roundabouts, which represent the costliest solutions. Concerning the case of roundabouts, many parameters influence its performance, e.g. geometry and size. Thus, other less costlier solutions should be pondered. This paper introduces a low-cost approach to traffic intersections, by using pre-signals; and conducts simulation experiments to verify if this approach could be used to improve the performance of traffic intersections. In this sense, an agent-based traffic simulation model was developed that applies the object modelling paradigm of Simio to model the individual behaviour of vehicles. The simulation experiments results indicate that the flow of vehicles can be increased up to 20%, reducing the average queue sizes and crossing time per vehicle and saving the fuel consumed up to 64%. (undefined) info:eu-repo/semantics/publishedVersion


Original document

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

http://dx.doi.org/10.1007/978-3-319-68496-3_36 under the license http://www.springer.com/tdm
https://dblp.uni-trier.de/db/conf/iccl2/iccl2017.html#VieiraDPO17,
https://www.scipedia.com/public/Vieira_et_al_2017a,
https://repositorium.sdum.uminho.pt/bitstream/1822/52992/1/458547_Book_Final.pdf,
https://repositorium.sdum.uminho.pt/handle/1822/52992,
https://rd.springer.com/chapter/10.1007/978-3-319-68496-3_36,
https://core.ac.uk/display/154276261,
https://academic.microsoft.com/#/detail/2760586557
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Published on 01/01/2017

Volume 2017, 2017
DOI: 10.1007/978-3-319-68496-3_36
Licence: CC BY-NC-SA license

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