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Short term traffic speed prediction on a large road network
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Kortsiktig trafikhastighet förutsägelse på ett stort vägnät (Swedish)
Abstract [en]

Traffic flow speed prediction has been an important element in the application of intelligent transportation system (ITS). The timely and accurate traffic flow speed prediction can be utilized to support the control, management, and improvement of traffic conditions. In this project, we investigate the short term traffic flow speed prediction on a large highway network. To eliminate the vagueness, we first give a formal mathematical definition of traffic flow speed prediction problem on a road network. In the last decades, traffic flow prediction research has been advancing from the theoretically well established parametric methods to nonparametric data-driven algorithms, like the deep neural networks. In this research, we give a detailed review of the state-of-art prediction models appeared in the literature.However, we find that the road networks are rather small in most of the literature, usually hundreds of road segments. The highway network in our project is much larger, consists of more than eighty thousand road segments, which makes it almost impossible to use the models in the literature directly. Therefore, in this research, we employ the time series clustering method to divide the road network into different disjoint regions. After that, several prediction models include historical average (HA), univariate and vector Autoregressive Integrated Moving Average model (ARIMA), support vector regression (SVR), Gaussian process regression (GPR), Stacked Autoencoders (SAEs), long short-term memory neural networks (LSTM) are selected to do the prediction on each region. We give a performance analysis of selected models at the end of the thesis.

Abstract [sv]

Trafikflöde förutsägelse är ett viktigt element i intelligenta transportsystem (ITS). Den läglig och exakta trafikflödes hastighet förutsägelse kan utnyttjas för att stödja kontrollen, hanteringen och förbättringen av trafikförhållandena. I det här projektet undersöker vi korttidsprognosens hastighetsprediktion på ett stort motorvägsnät. För att eliminera vaghet, vi först en formell matematisk definition av trafikflödeshastighetsprognosproblem på ett vägnät. Under de senaste årtiondena har prognosis för trafik flödeshastighet frodas från de teoretiskt väl etablerade parametriska metoderna till icke-parametriska data-driven algoritmer, som de djupa neurala nätverken. I den här undersökningen ger vi en detaljerad granskning av de modernaste prediksionsmodellerna i litteraturen.Vi finner dock att vägnätet är ganska litet i de flesta av litteraturen, vanligtvis hundratals vägsegment. Motorvägsnätverket i vårt projekt är mycket större, består av mer än 80 tusen vägsegment, vilket gör det nästan omöjligt att direkt använda modellerna i litteraturen. Därför använder vi i tidsserien klustermetoden för att dela upp vägnätet i olika åtskilja regioner. Därefter innehåller flera prediktionsmodeller historisk medelvärde (HA), univariate och vector Autoregressive Integrated Moving Average-modellen (ARIMA), stödvektorregression (SVR), Gaussian processregression (GPR), Staplade Autoenkodare (SAEs) neurala nätverk (LSTM) väljs för att göra förutsägelsen för varje region. Vi ger en prestationsanalys av utvalda modeller i slutet av avhandlingen.

Place, publisher, year, edition, pages
2019.
Series
TRITA-SCI-GRU ; 2019:086
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-252345OAI: oai:DiVA.org:kth-252345DiVA, id: diva2:1319934
External cooperation
The Chinese University of Hong Kong
Subject / course
Mathematical Statistics
Educational program
Master of Science - Applied and Computational Mathematics
Supervisors
Examiners
Available from: 2019-06-04 Created: 2019-06-03 Last updated: 2019-06-04Bibliographically approved

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