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3D Speed Maps and Mean Observations Vectors for Short-Term Urban Traffic Prediction
KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Byggvetenskap, Transportplanering. (Urban Mobility Group)ORCID-id: 0000-0002-8499-0843
Department of Science and Technology,Linköping University.
KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Byggvetenskap, Transportplanering. (Urban Mobility Group)ORCID-id: 0000-0002-4106-3126
KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Byggvetenskap, Transportplanering. (Urban Mobility Group)
2019 (engelsk)Inngår i: TRB Annual Meeting Online, Washington DC, US, 2019, s. 1-20Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Washington DC, US, 2019. s. 1-20
Emneord [en]
3D speed map, short-term prediction, travel time prediction, traffic prediction, large-scale prediction, clustering, partitioning, spatio-temporal partitioning
HSV kategori
Forskningsprogram
Transportvetenskap
Identifikatorer
URN: urn:nbn:se:kth:diva-250647OAI: oai:DiVA.org:kth-250647DiVA, id: diva2:1312964
Konferanse
Transportation research board annual meeting (TRB)
Merknad

QC 20190502

Tilgjengelig fra: 2019-05-01 Laget: 2019-05-01 Sist oppdatert: 2019-08-27bibliografisk kontrollert
Inngår i avhandling
1. Short-Term Traffic Prediction in Large-Scale Urban Networks
Åpne denne publikasjonen i ny fane eller vindu >>Short-Term Traffic Prediction in Large-Scale Urban Networks
2019 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Stockholm: KTH Royal Institute of Technology, 2019. s. 21
Serie
TRITA-ABE-DLT ; 1915
Emneord
travel time prediction, short-term travel time prediction, traffic prediction, clustering, partitioning, spatio-temporal partitioning, large-scale prediction, PPCA, 3D speed map
HSV kategori
Forskningsprogram
Transportvetenskap
Identifikatorer
urn:nbn:se:kth:diva-250650 (URN)978-91-7873-224-1 (ISBN)
Presentation
2019-05-31, B2, Brinellvägen 23, Stockholm, 13:00 (engelsk)
Opponent
Veileder
Merknad

QC 20190531

Tilgjengelig fra: 2019-05-02 Laget: 2019-05-01 Sist oppdatert: 2019-05-02bibliografisk kontrollert

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