Gaining insights into road traffic data through genetic improvement

Anikó Ekárt, Alina Patelli, Victoria Lush, Elisabeth Ilie-Zudor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationGECCO '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
DOIs
Publication statusPublished - 15 Jul 2017
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

Fingerprint

Highway accidents
Data mining

Bibliographical 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

Cite this

Ekárt, A., Patelli, A., Lush, V., & Ilie-Zudor, E. (2017). Gaining insights into road traffic data through genetic improvement. In GECCO '17: proceedings of the Genetic and Evolutionary Computation Conference (pp. 1511-1512). New York, NY (US): ACM. https://doi.org/10.1145/3067695.3082523
Ekárt, Anikó ; Patelli, Alina ; Lush, Victoria ; Ilie-Zudor, Elisabeth. / Gaining insights into road traffic data through genetic improvement. GECCO '17: proceedings of the Genetic and Evolutionary Computation Conference. New York, NY (US) : ACM, 2017. pp. 1511-1512
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Ekárt, A, Patelli, A, Lush, V & Ilie-Zudor, E 2017, Gaining insights into road traffic data through genetic improvement. in GECCO '17: proceedings of the Genetic and Evolutionary Computation Conference. ACM, New York, NY (US), pp. 1511-1512, Genetic and Evolutionary Computation Conference, GECCO '17, Berlin, Germany, 15/07/17. https://doi.org/10.1145/3067695.3082523

Gaining insights into road traffic data through genetic improvement. / Ekárt, Anikó; Patelli, Alina; Lush, Victoria; Ilie-Zudor, Elisabeth.

GECCO '17: proceedings of the Genetic and Evolutionary Computation Conference. New York, NY (US) : ACM, 2017. p. 1511-1512.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Ekárt A, Patelli A, Lush V, Ilie-Zudor E. Gaining insights into road traffic data through genetic improvement. In GECCO '17: proceedings of the Genetic and Evolutionary Computation Conference. New York, NY (US): ACM. 2017. p. 1511-1512 https://doi.org/10.1145/3067695.3082523