Predicting Motorway Traffic Performance by Data Fusion of Local Sensor Data and Electronic Toll Collection Data

Heilmann, B; El Faouzi, N-E; de Mouzon, O; Hainitz, N; Koller, H; Bauer, D; Antoniou, C

dc.contributor.author	Heilmann, B	en
dc.contributor.author	El Faouzi, N-E	en
dc.contributor.author	de Mouzon, O	en
dc.contributor.author	Hainitz, N	en
dc.contributor.author	Koller, H	en
dc.contributor.author	Bauer, D	en
dc.contributor.author	Antoniou, C	en
dc.date.accessioned	2014-03-01T01:36:40Z
dc.date.available	2014-03-01T01:36:40Z
dc.date.issued	2011	en
dc.identifier.issn	1093-9687	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/21385
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Construction & Building Technology	en
dc.subject.classification	Engineering, Civil	en
dc.subject.other	Austria	en
dc.subject.other	Computational performance	en
dc.subject.other	Correlation analysis	en
dc.subject.other	Electronic toll collection	en
dc.subject.other	Electronic toll collection systems	en
dc.subject.other	Free flow	en
dc.subject.other	Heavy goods vehicles	en
dc.subject.other	Historical database	en
dc.subject.other	Linear Kalman filters	en
dc.subject.other	Local sensors	en
dc.subject.other	Macroscopic model	en
dc.subject.other	Memory length	en
dc.subject.other	Non-parametric	en
dc.subject.other	Prediction accuracy	en
dc.subject.other	Prediction horizon	en
dc.subject.other	Prediction methods	en
dc.subject.other	Speed-density	en
dc.subject.other	State-space models	en
dc.subject.other	Time interval	en
dc.subject.other	Traffic management centers	en
dc.subject.other	Traffic performance	en
dc.subject.other	Traffic state	en
dc.subject.other	Transition state	en
dc.subject.other	Automobiles	en
dc.subject.other	Forecasting	en
dc.subject.other	Highway traffic control	en
dc.subject.other	Information fusion	en
dc.subject.other	Pattern recognition	en
dc.subject.other	Toll collection	en
dc.subject.other	Toll highways	en
dc.subject.other	Sensor data fusion	en
dc.subject.other	correlation	en
dc.subject.other	database	en
dc.subject.other	Kalman filter	en
dc.subject.other	motorway	en
dc.subject.other	prediction	en
dc.subject.other	traffic management	en
dc.subject.other	Austria	en
dc.subject.other	GB virus C	en
dc.title	Predicting Motorway Traffic Performance by Data Fusion of Local Sensor Data and Electronic Toll Collection Data	en
heal.type	journalArticle	en
heal.identifier.primary	10.1111/j.1467-8667.2010.00696.x	en
heal.identifier.secondary	http://dx.doi.org/10.1111/j.1467-8667.2010.00696.x	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	This article proposes data fusion from different sources to improve estimation and prediction accuracy of traffic states on motorways. This is demonstrated in two case studies on an intraurban and an interurban motorway section in Austria. Data fusion in this case combines local detector data and speed data from the Electronic Toll Collection (ETC) system for heavy goods vehicles (HGV). A macroscopic model for open motorway sections has been used to estimate passenger car and HGV density, applying a standard state-space model and a linear Kalman filter. The resulting historical database of 4 months of speed-density patterns has been used as a basis for pattern recognition. A nonparametric kernel predictor with memory length of 9 and 18 hours has been used to predict HGV speed for a prediction horizon of 15 minutes to 2 hours. Results show good overall prediction accuracy. Correlation analysis showed little bias of predicted speed for free flow and congested time intervals, whereas transition states between free flow and congestion were frequently biased. Prediction accuracy can be improved by applying a combination of different prediction methods. On the other hand, computational performance of the prediction has to be further improved prior to implementation in a traffic management center. © 2010 Computer-Aided Civil and Infrastructure Engineering.	en
heal.publisher	WILEY-BLACKWELL	en
heal.journalName	Computer-Aided Civil and Infrastructure Engineering	en
dc.identifier.doi	10.1111/j.1467-8667.2010.00696.x	en
dc.identifier.isi	ISI:000292367900005	en
dc.identifier.volume	26	en
dc.identifier.issue	6	en
dc.identifier.spage	451	en
dc.identifier.epage	463	en