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3D Speed Maps and Mean Observations Vectors for Short-Term Urban Traffic Prediction
KTH, Sweden.ORCID iD: 0000-0002-8499-0843
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-5961-5136
KTH, Sweden.ORCID iD: 0000-0002-4106-3126
KTH, Sweden.
2019 (English)In: TRB Annual Meeting Online, Washington DC, US, 2019, 2019, p. 1-20Conference paper, Oral presentation only (Other academic)
Abstract [en]

City-wide travel time prediction in real-time is an important enabler for efficient use of the road network. It can be used in traveler information to enable more efficient routing of individual vehicles as well as decision support for traffic management applications such as directed information campaigns or incident management. 3D speed maps have been shown to be a promising methodology for revealing day-to-day regularities of city-level travel times and possibly also for short-term prediction. In this paper, we aim to further evaluate and benchmark the use of 3D speed maps for short-term travel time prediction and to enable scenario-based evaluation of traffic management actions we also evaluate the framework for traffic flow prediction. The 3D speed map methodology is adapted to short-term prediction and benchmarked against historical mean as well as against Probabilistic Principal Component Analysis (PPCA). The benchmarking and analysis are made using one year of travel time and traffic flow data for the city of Stockholm, Sweden. The result of the case study shows very promising results of the 3D speed map methodology for short-term prediction of both travel times and traffic flows. The modified version of the 3D speed map prediction outperforms the historical mean prediction as well as the PPCA method. Further work includes an extended evaluation of the method for different conditions in terms of underlying sensor infrastructure, preprocessing and spatio-temporal aggregation as well as benchmarking against other prediction methods.

Place, publisher, year, edition, pages
2019. p. 1-20
Keywords [en]
3D speed map, short-term prediction, travel time prediction, traffic prediction, large-scale prediction, clustering, partitioning, spatio-temporal partitioning, Transport Systems and Logistics, Transportteknik och logistik
Identifiers
URN: urn:nbn:se:liu:diva-178639OAI: oai:DiVA.org:liu-178639DiVA, id: diva2:1587598
Conference
Transportation research board annual meeting (TRB)
Available from: 2021-08-25 Created: 2021-08-25 Last updated: 2021-08-25

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Gundlegård, David

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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