Conference paper Open Access

Application of Artificial Intelligence Techniques to Traffic Prediction and Route Planning, the vision of TIMON project

E. Osaba; P. Lopez-Garcia; E. Onieva; A.D. Masegosa; L. Serrano; H. Landaluce


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.5281/zenodo.894076</identifier>
  <creators>
    <creator>
      <creatorName>E. Osaba</creatorName>
      <affiliation>Deusto</affiliation>
    </creator>
    <creator>
      <creatorName>P. Lopez-Garcia</creatorName>
      <affiliation>Deusto</affiliation>
    </creator>
    <creator>
      <creatorName>E. Onieva</creatorName>
      <affiliation>Deusto</affiliation>
    </creator>
    <creator>
      <creatorName>A.D. Masegosa</creatorName>
      <affiliation>Deusto</affiliation>
    </creator>
    <creator>
      <creatorName>L. Serrano</creatorName>
      <affiliation>Deusto</affiliation>
    </creator>
    <creator>
      <creatorName>H. Landaluce</creatorName>
      <affiliation>Deusto</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Application of Artificial Intelligence Techniques to Traffic Prediction and Route Planning, the vision of TIMON project</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2017</publicationYear>
  <subjects>
    <subject>Artificial Intelligence</subject>
    <subject>Traffic Congestion</subject>
    <subject>Route Planning</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2017-06-22</date>
  </dates>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/894076</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.894075</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by-sa/4.0/legalcode">Creative Commons Attribution Share Alike 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;TIMON is an European research project under the Horizon 2020 programme. The main objective of&lt;br&gt;
this project is to provide real-time services through a web based platform and a mobile APP for drivers,&lt;br&gt;
Vulnerable Road Users (VRUs) and businesses. These services will contribute to increasing drivers&lt;br&gt;
and VRUs assistance and safety. To provide these services, one of the core technologies developed&lt;br&gt;
inside TIMON will be the design and development of Artificial Intelligence (AI) techniques for traffic&lt;br&gt;
prediction and route planning. The DeustoTech-Mobility research group is in charge of this part of the&lt;br&gt;
project. The objective of this technical paper is to describe the approach followed in TIMON to&lt;br&gt;
develop traffic congestion prediction and route planning services based on AI techniques and the&lt;br&gt;
progress done so far. Additionally, the deployment and the result obtained in the first test done is also&lt;br&gt;
detailed in this study.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/636220/">636220</awardNumber>
      <awardTitle>Enhanced real time services for an optimized multimodal mobility relying on cooperative networks and open data</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
72
278
views
downloads
All versions This version
Views 7272
Downloads 278278
Data volume 92.8 MB92.8 MB
Unique views 6969
Unique downloads 249249

Share

Cite as