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.

Publication DOI: https://doi.org/10.1145/3067695.3082523
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Computer Science
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
College of Engineering & Physical Sciences
Additional Information: -© 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).
Event Title: Genetic and Evolutionary Computation Conference, GECCO '17
Event Type: Other
Event Dates: 2017-07-15 - 2017-07-19
Uncontrolled Keywords: data mining,genetic Improvement,symbolic regression,Software,Computational Theory and Mathematics,Computer Science Applications
ISBN: 978-1-4503-4920-8, 978-1-4503-4939-0
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2017-07-15
Accepted Date: 2017-07-01
Authors: Ekárt, Anikó (ORCID Profile 0000-0001-6967-5397)
Patelli, Alina (ORCID Profile 0000-0002-8945-6711)
Lush, Victoria (ORCID Profile 0000-0003-3248-9608)
Ilie-Zudor, Elisabeth

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