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

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Fältnamn	Indikatorer	Metadata
000		03774naa a2200469 4500
001		oai:DiVA.org:kth-250647
003		SwePub
008		190501s2019 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
009		oai:DiVA.org:liu-178639
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-2506472 URI
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1786392 URI
040		a (SwePub)kthd (SwePub)liu
041		a engb eng
042		9 SwePub
072	7	a ref2 swepub-contenttype
072	7	a kon2 swepub-publicationtype
100	1	a Cebecauer, Mateju KTH,Transportplanering,Urban Mobility Group,KTH, Sweden4 aut0 (Swepub:kth)u1nb1u4p
245	1 0	a 3D Speed Maps and Mean Observations Vectors for Short-Term Urban Traffic Prediction
264	1	a Washington DC, US,c 2019
338		a print2 rdacarrier
500		a QC 20190502
520		a 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.
650	7	a TEKNIK OCH TEKNOLOGIERx Samhällsbyggnadsteknikx Transportteknik och logistik0 (SwePub)201052 hsv//swe
650	7	a ENGINEERING AND TECHNOLOGYx Civil Engineeringx Transport Systems and Logistics0 (SwePub)201052 hsv//eng
653		a 3D speed map
653		a short-term prediction
653		a travel time prediction
653		a traffic prediction
653		a large-scale prediction
653		a clustering
653		a partitioning
653		a spatio-temporal partitioning
653		a Transportvetenskap
653		a Transport Science
700	1	a Gundlegård, David,d 1978-u Linköpings universitet,Kommunikations- och transportsystem,Tekniska fakulteten4 aut0 (Swepub:liu)davgu33
700	1	a Jenelius, Erik,d 1980-u KTH,Transportplanering,Urban Mobility Group,KTH, Sweden4 aut0 (Swepub:kth)u1x5t81f
700	1	a Burghout, Wilcou KTH,Transportplanering,Urban Mobility Group,KTH, Sweden4 aut0 (Swepub:kth)u1x8efdz
710	2	a KTHb Transportplanering4 org
773	0	t TRB Annual Meeting Onlined Washington DC, USg , s. 1-20, s. 1-20q <1-20
773	0	t TRB Annual Meeting Online, Washington DC, US, 2019d Washington DC, USg , s. 1-20, s. 1-20q <1-20<1-20
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-250647
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-178639

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Av författaren/redakt...: Cebecauer, Matej; Gundlegård, Davi ...; Jenelius, Erik, ...; Burghout, Wilco

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TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Samhällsbyggnads ...; och Transportteknik ...

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Av lärosätet: Kungliga Tekniska Högskolan; Linköpings universitet

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