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Integrated framework for real-time urban network travel time prediction on sparse probe data
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering. KTH Royal Institute of Technology.
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering. KTH Royal Institute of Technology.
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering. KTH Royal Institute of Technology.
2018 (English)In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 12, no 1, p. 66-74Article in journal (Refereed) Published
Abstract [en]

The study presents the methodology and system architecture of an integrated urban road network travel time prediction framework based on low-frequency probe vehicle data. Intended applications include real-time network traffic management, vehicle routing and information provision. The framework integrates methods for receiving a stream of probe vehicle data, map matching and path inference, link travel time estimation, calibration of prediction model parameters and network travel time prediction in real time. The system design satisfies three crucial aspects: computational efficiency of prediction, internal consistency between components and robustness against noisy and missing data. Prediction is based on a multivariate hybrid method of probabilistic principal component analysis, which captures global correlation patterns between links and time intervals, and local smoothing, which considers local correlations among neighbouring links. Computational experiments for the road network of Stockholm, Sweden and probe data from taxis show that the system provides high accuracy for both peak and off-peak traffic conditions. The computational efficiency of the framework makes it capable of real-time prediction for large-scale networks. For links with large speed variations between days, prediction significantly outperforms the historical mean. Furthermore, prediction is reliable also for links with high proportions of missing data.

Place, publisher, year, edition, pages
Institution of Engineering and Technology, 2018. Vol. 12, no 1, p. 66-74
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:kth:diva-219361DOI: 10.1049/iet-its.2017.0113Scopus ID: 2-s2.0-85041135912OAI: oai:DiVA.org:kth-219361DiVA, id: diva2:1162465
Note

QC 20180206

Available from: 2017-12-04 Created: 2017-12-04 Last updated: 2018-02-06Bibliographically approved

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  • apa
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Output format
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