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Urban network travel time prediction based on a probabilistic principal component analysis model of probe data
KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Transportvetenskap.
KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Transportvetenskap. Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115 USA.
2018 (engelsk)Inngår i: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 19, nr 2, s. 436-445Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

This paper proposes a network travel time prediction methodology based on probe data. The model is intended as a tool for traffic management, trip planning, and online vehicle routing, and is designed to be efficient and scalable in calibration and real-time prediction; flexible to changes in network, data, and model extensions; and robust against noisy and missing data. A multivariate probabilistic principal component analysis (PPCA) model is proposed. Spatio-temporal correlations are inferred from historical data based on MLE and an efficient EM algorithm for handling missing data. Prediction is performed in real time by computing the expected distribution of link travel times in future time intervals, conditional on recent current-day observations. A generalization of the methodology partitions the network and applies a distinct PPCA model to each subnetwork. The methodology is applied to the network of downtown Shenzhen, China, using taxi probe data. The model captures variability over months and weekdays as well as other factors. Prediction with PPCA outperforms k-nearest neighbors prediction for horizons from 15 to 45 min, and a hybrid method of PPCA and local smoothing provides the highest accuracy.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2018. Vol. 19, nr 2, s. 436-445
Emneord [en]
Travel time prediction, PPCA, probe data
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-219362DOI: 10.1109/TITS.2017.2703652ISI: 000424060200011Scopus ID: 2-s2.0-85020405032OAI: oai:DiVA.org:kth-219362DiVA, id: diva2:1162467
Forskningsfinansiär
Swedish Transport AdministrationTrenOp, Transport Research Environment with Novel Perspectives
Merknad

QC 20171212

Tilgjengelig fra: 2017-12-04 Laget: 2017-12-04 Sist oppdatert: 2018-02-22bibliografisk kontrollert

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