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Urban network travel time prediction based on a probabilistic principal component analysis model of probe data
KTH, School of Architecture and the Built Environment (ABE), Transport Science.
KTH, School of Architecture and the Built Environment (ABE), Transport Science. Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115 USA.
2018 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 19, no 2, p. 436-445Article in journal (Refereed) Published
Abstract [en]

This paper proposes a network travel time prediction methodology based on probe data. The model is intended as a tool for traffic management, trip planning, and online vehicle routing, and is designed to be efficient and scalable in calibration and real-time prediction; flexible to changes in network, data, and model extensions; and robust against noisy and missing data. A multivariate probabilistic principal component analysis (PPCA) model is proposed. Spatio-temporal correlations are inferred from historical data based on MLE and an efficient EM algorithm for handling missing data. Prediction is performed in real time by computing the expected distribution of link travel times in future time intervals, conditional on recent current-day observations. A generalization of the methodology partitions the network and applies a distinct PPCA model to each subnetwork. The methodology is applied to the network of downtown Shenzhen, China, using taxi probe data. The model captures variability over months and weekdays as well as other factors. Prediction with PPCA outperforms k-nearest neighbors prediction for horizons from 15 to 45 min, and a hybrid method of PPCA and local smoothing provides the highest accuracy.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. Vol. 19, no 2, p. 436-445
Keywords [en]
Travel time prediction, PPCA, probe data
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:kth:diva-219362DOI: 10.1109/TITS.2017.2703652ISI: 000424060200011Scopus ID: 2-s2.0-85020405032OAI: oai:DiVA.org:kth-219362DiVA, id: diva2:1162467
Funder
Swedish Transport AdministrationTrenOp, Transport Research Environment with Novel Perspectives
Note

QC 20171212

Available from: 2017-12-04 Created: 2017-12-04 Last updated: 2018-02-22Bibliographically approved

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CiteExportLink to record
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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