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3D Speed Maps and Mean Observations Vectors for Short-Term Urban Traffic Prediction
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning. (Urban Mobility Group)ORCID iD: 0000-0002-8499-0843
Department of Science and Technology,Linköping University.
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning. (Urban Mobility Group)ORCID iD: 0000-0002-4106-3126
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning. (Urban Mobility Group)
2019 (English)In: TRB Annual Meeting Online, Washington DC, US, 2019, p. 1-20Conference paper, Published paper (Refereed)
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
Washington DC, US, 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
National Category
Transport Systems and Logistics
Research subject
Transport Science
Identifiers
URN: urn:nbn:se:kth:diva-250647OAI: oai:DiVA.org:kth-250647DiVA, id: diva2:1312964
Conference
Transportation research board annual meeting (TRB)
Note

QC 20190502

Available from: 2019-05-01 Created: 2019-05-01 Last updated: 2019-08-27Bibliographically approved
In thesis
1. Short-Term Traffic Prediction in Large-Scale Urban Networks
Open this publication in new window or tab >>Short-Term Traffic Prediction in Large-Scale Urban Networks
2019 (English)Licentiate thesis, comprehensive summary (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
Stockholm: KTH Royal Institute of Technology, 2019. p. 21
Series
TRITA-ABE-DLT ; 1915
Keywords
travel time prediction, short-term travel time prediction, traffic prediction, clustering, partitioning, spatio-temporal partitioning, large-scale prediction, PPCA, 3D speed map
National Category
Transport Systems and Logistics
Research subject
Transport Science
Identifiers
urn:nbn:se:kth:diva-250650 (URN)978-91-7873-224-1 (ISBN)
Presentation
2019-05-31, B2, Brinellvägen 23, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20190531

Available from: 2019-05-02 Created: 2019-05-01 Last updated: 2019-05-02Bibliographically approved

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Cebecauer, MatejJenelius, Erik

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