Gaining insights into road traffic data through genetic improvement

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Abstract

We argue that Genetic Improvement can be successfully used for enhancing road traffc data mining. This would support the relevant decision makers with extending the existing network of devices that sense and control city traffc, with the end goal of improving vehicle Flow and reducing the frequency of road accidents. Our position results from a set of preliminary observations emerging from the analysis of open access road trafic data collected in real time by the Birmingham City Council.

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  • Road traffic data through genetic improvement

    Rights statement: © 2017 Copyright held by the owner/author(s). Publication rights licensed to ACM. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.

    Final published version, 396 KB, PDF-document

Details

Publication date1 Jul 2017
Publication titleGECCO '17: proceedings of the Genetic and Evolutionary Computation Conference
Place of PublicationNew York, NY (US)
PublisherACM
Pages1511-1512
Number of pages2
ISBN (Electronic)978-1-4503-4939-0
ISBN (Print)978-1-4503-4920-8
Original languageEnglish
EventGenetic and Evolutionary Computation Conference, GECCO '17 - Berlin, Germany
Duration: 15 Jul 201719 Jul 2017

Conference

ConferenceGenetic and Evolutionary Computation Conference, GECCO '17
CountryGermany
CityBerlin
Period15/07/1719/07/17

Bibliographic note

-© 2017 Copyright held by the owner/author(s). Publication rights licensed to ACM. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. Funding H2020 (691829).

    Keywords

  • data mining, genetic Improvement, symbolic regression

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