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Understanding and predicting trends in urban freight transport
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
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2017 (English)In: Proceedings - 18th IEEE International Conference on Mobile Data Management, MDM 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, 124-133 p.Conference paper (Refereed)
Abstract [en]

Among different components of urban mobility, urban freight transport is usually considered as the least sustainable. Limited traffic infrastructures and increasing demands in dense urban regions lead to frequent delivery runs with smaller freight vehicles. This increases the traffic in urban areas and has negative impacts upon the quality of life in urban populations. Data driven optimizations are essential to better utilize existing urban transport infrastructures and to reduce the negative effects of freight deliveries for the cities. However, there is limited work and data driven research on urban delivery areas and freight transportation networks. In this paper, we collect and analyse data on urban freight deliveries and parking areas towards an optimized urban freight transportation system. Using a new check-in based mobile parking system for freight vehicles, we aim to understand and optimize freight distribution processes. We explore the relationship between areas' availability patterns and underlying traffic behaviour in order to understand the trends in urban freight transport. By applying the detected patterns we predict the availabilities of loading/unloading areas, and thus open up new possibilities for delivery route planning and better managing of freight transport infrastructures.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. 124-133 p.
Keyword [en]
Parking availability prediction, Smart cities, Smart mobility, Urban freight transport
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:kth:diva-212471DOI: 10.1109/MDM.2017.26Scopus ID: 2-s2.0-85026731337ISBN: 9781538639320 (print)OAI: oai:DiVA.org:kth-212471DiVA: diva2:1135175
Conference
18th IEEE International Conference on Mobile Data Management, MDM 2017, 29 May 2017 through 1 June 2017
Note

QC 20170822

Available from: 2017-08-22 Created: 2017-08-22 Last updated: 2017-08-22Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf