Large-Scale Multi-Agent Simulations for Transportation Applications
| dc.contributor.author | Balmer, Michael | |
| dc.contributor.author | Nagel, Kai | |
| dc.contributor.author | Raney, Bryan | |
| dc.date.accessioned | 2019-03-28T14:29:26Z | |
| dc.date.available | 2019-03-28T14:29:26Z | |
| dc.date.issued | 2004 | |
| dc.description.abstract | In many transportation simulation applications including intelligent transportation systems (ITS), behavioral responses of individual travelers are important. This implies that simulating individual travelers directly may be useful. Such a microscopic simulation, consisting of many intelligent particles (= agents), is an example of a multi-agent simulation. For ITS applications, it would be useful to simulate large metropolitan areas, with ten million travelers or more. Indeed, when using parallel computing and efficient implementations, multi-agent simulations of transportation systems of that size are feasible, with computational speeds of up to 300 times faster than real time. It is also possible to efficiently implement the simulation of day-to-day agent-based learning, and it is possible to make this implementation modular and essentially “plug-and-play.” Unfortunately, these techniques are not immediately applicable for within-day replanning, which would be paramount for ITS. Alternative techniques, which allow within-day replanning also for large scenarios, are discussed. | en |
| dc.identifier.eissn | 1547-2442 | |
| dc.identifier.issn | 1547-2450 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/9260 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-8337 | |
| dc.language.iso | en | |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
| dc.subject.ddc | 380 Handel, Kommunikation, Verkehr | de |
| dc.subject.other | multi-agent simulation | en |
| dc.subject.other | transportation planning | en |
| dc.subject.other | transportation application | en |
| dc.subject.other | parallel computation | en |
| dc.subject.other | route planning | en |
| dc.subject.other | traffic simulation | en |
| dc.subject.other | agent learning | en |
| dc.subject.other | activity planning | en |
| dc.title | Large-Scale Multi-Agent Simulations for Transportation Applications | en |
| dc.type | Article | en |
| dc.type.version | acceptedVersion | en |
| dcterms.bibliographicCitation.doi | 10.1080/15472450490523892 | |
| dcterms.bibliographicCitation.issue | 4 | |
| dcterms.bibliographicCitation.journaltitle | Journal of intelligent transportation systems : technology planning and operations | en |
| dcterms.bibliographicCitation.originalpublishername | Taylor & Francis | en |
| dcterms.bibliographicCitation.originalpublisherplace | London [u.a.] | de |
| dcterms.bibliographicCitation.pageend | 221 | |
| dcterms.bibliographicCitation.pagestart | 205 | |
| dcterms.bibliographicCitation.volume | 8 | |
| tub.accessrights.dnb | domain | |
| tub.affiliation | Fak. 5 Verkehrs- und Maschinensysteme::Inst. Land- und Seeverkehr (ILS)::FG Verkehrssystemplanung und Verkehrstelematik | de |
| tub.affiliation.faculty | Fak. 5 Verkehrs- und Maschinensysteme | de |
| tub.affiliation.group | FG Verkehrssystemplanung und Verkehrstelematik | de |
| tub.affiliation.institute | Inst. Land- und Seeverkehr (ILS) | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin | de |
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