Gaining insights into road traffic data through genetic improvement

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

doi:10.1145/3067695.3082523

Gaining insights into road traffic data through genetic improvement

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

Hungarian Academy of Sciences

Research output: Chapter in Book/Published conference output › Conference publication

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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.

Original language	English
Title of host publication	GECCO '17: proceedings of the Genetic and Evolutionary Computation Conference
Place of Publication	New York, NY (US)
Publisher	ACM
Pages	1511-1512
Number of pages	2
ISBN (Electronic)	978-1-4503-4939-0
ISBN (Print)	978-1-4503-4920-8
DOIs	https://doi.org/10.1145/3067695.3082523
Publication status	Published - 15 Jul 2017
Event	Genetic and Evolutionary Computation Conference, GECCO '17 - Berlin, Germany Duration: 15 Jul 2017 → 19 Jul 2017

Conference

Conference	Genetic and Evolutionary Computation Conference, GECCO '17
Country/Territory	Germany
City	Berlin
Period	15/07/17 → 19/07/17

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 [email protected].

Funding H2020 (691829).

Keywords

data mining
genetic Improvement
symbolic regression

Access to Document

10.1145/3067695.3082523

Road traffic data through genetic improvement
© 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 [email protected].
Final published version, 396 KB

Cite this

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title = "Gaining insights into road traffic data through genetic improvement",

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.",

keywords = "data mining, genetic Improvement, symbolic regression",

author = "Anik{\'o} Ek{\'a}rt and Alina Patelli and Victoria Lush and Elisabeth Ilie-Zudor",

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ER -

Gaining insights into road traffic data through genetic improvement

Abstract

Conference

Bibliographical note

Keywords

Access to Document

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