Generating complete all-day activity plans with genetic algorithms
| dc.contributor.author | Charypar, David | |
| dc.contributor.author | Nagel, Kai | |
| dc.date.accessioned | 2019-03-28T14:29:22Z | |
| dc.date.available | 2019-03-28T14:29:22Z | |
| dc.date.issued | 2005 | |
| dc.description.abstract | Activity-based demand generation contructs complete all-day activity plans for each member of a population, and derives transportation demand from the fact that consecutive activities at different locations need to be connected by travel. Besides many other advantages, activity-based demand generation also fits well into the paradigm of multi-agent simulation, where each traveler is kept as an individual throughout the whole modeling process. In this paper, we present a new approach to the problem, which uses genetic algorithms (GA). Our GA keeps, for each member of the population, several instances of possible all-day activity plans in memory. Those plans are modified by mutation and crossover, while 'bad' instances are eventually discarded. Any GA needs a fitness function to evaluate the performance of each instance. For all-day activity plans, it makes sense to use a utility function to obtain such fitness. In consequence, a significant part of the paper is spent discussing such a utility function. In addition, the paper shows the performance of the algorithm to a few selected problems, including very busy and rather non-busy days. | en |
| dc.identifier.eissn | 1572-9435 | |
| dc.identifier.issn | 0049-4488 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/9258 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-8335 | |
| 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 | activity generation | en |
| dc.subject.other | genetic algorithms | en |
| dc.subject.other | location choice | en |
| dc.subject.other | multi-agent traffic simulation | en |
| dc.subject.other | utility functions | en |
| dc.title | Generating complete all-day activity plans with genetic algorithms | en |
| dc.type | Article | en |
| dc.type.version | acceptedVersion | en |
| dcterms.bibliographicCitation.doi | 10.1007/s11116-004-8287-y | |
| dcterms.bibliographicCitation.issue | 4 | |
| dcterms.bibliographicCitation.journaltitle | Transportation | en |
| dcterms.bibliographicCitation.originalpublishername | Springer | en |
| dcterms.bibliographicCitation.originalpublisherplace | Dordrecht [u.a.] | de |
| dcterms.bibliographicCitation.pageend | 397 | |
| dcterms.bibliographicCitation.pagestart | 369 | |
| dcterms.bibliographicCitation.volume | 32 | |
| 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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