Search: onr:"swepub:oai:DiVA.org:kth-250647" > 3D Speed Maps and M...
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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