3D Speed Maps and Mean Observations Vectors for Short-Term Urban Traffic Prediction

↓ Direkt till sidans innehåll
↓ Direkt till sidans sekundära innehåll (sidomenyn)

Search: WFRF:(Jenelius Erik 1980 ) > 3D Speed Maps and M...

3D Speed Maps and Mean Observations Vectors for Short-Term Urban Traffic Prediction

Cebecauer, Matej (author): KTH,Transportplanering,Urban Mobility Group,KTH, Sweden

Gundlegård, David, 1978- (author): Linköpings universitet,Kommunikations- och transportsystem,Tekniska fakulteten

Jenelius, Erik, 1980- (author): KTH,Transportplanering,Urban Mobility Group,KTH, Sweden

Burghout, Wilco (author): KTH,Transportplanering,Urban Mobility Group,KTH, Sweden

show less...

(creator_code:org_t)

Washington DC, US, 2019
2019
English.
In: TRB Annual Meeting Online. - Washington DC, US. ; , s. 1-20, s. 1-20

Related links:: https://urn.kb.se/re...; show more...; https://urn.kb.se/re...; show less...

Conference paper (peer-reviewed)

Abstract Subject headings

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.

Subject headings

TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Transportteknik och logistik (hsv//swe)
ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Transport Systems and Logistics (hsv//eng)

Keyword

3D speed map
short-term prediction
travel time prediction
traffic prediction
large-scale prediction
clustering
partitioning
spatio-temporal partitioning
Transportvetenskap
Transport Science

Publication and Content Type

ref (subject category)
kon (subject category)

To the university's database

Find more in SwePub

By the author/editor: Cebecauer, Matej; Gundlegård, Davi ...; Jenelius, Erik, ...; Burghout, Wilco

About the subject

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Civil Engineerin ...; and Transport System ...

Articles in the publication: TRB Annual Meeti ...

By the university: Royal Institute of Technology; Linköping University

Search outside SwePub

Extend your search to:: Google; Google Book Search; Google Scholar

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

LIBRIS.kb.se