ORBi: Cools Mario - Investigating Effect of Holidays on Daily Traffic Counts: Time Series Approach

Reference : Investigating Effect of Holidays on Daily Traffic Counts: Time Series Approach


	Document type :	Scientific congresses and symposiums : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Civil engineering Business & economic sciences : Special economic topics (health, labor, transportation…)
	To cite this reference:	http://hdl.handle.net/2268/133001

Title :	Investigating Effect of Holidays on Daily Traffic Counts: Time Series Approach
Language :	English
Author, co-author :	Cools, Mario [Universiteit Hasselt - UH > > > >]
	Moons, Elke [Universiteit Hasselt - UH > > > >]
	Wets, Geert [Universiteit Hasselt - UH > > > >]
Publication date :	2007
Main document title :	Proceedings of the 86th Annual Meeting of the Transportation Research Board (CD-ROM)
Publisher :	Transportation Research Board of the National Academies
On invitation :	No
Audience :	International
City :	Washington
Country :	DC
Event name :	86th Annual Meeting of the Transportation Research Board
Event date :	21-01-2007 to 25-01-2007
Event organizer :	Transportation Research Board of the National Academies
Event place (city) :	Washington
Event country :	DC
Abstract :	[en] In this paper, different modeling philosophies are explored in order to explain and forecast daily traffic counts. The main objectives of this study are the analysis of the impact of holidays on daily traffic, and the forecasting of future traffic counts. Data coming from single inductive loop detectors, collected in 2003, 2004 and 2005, were used for the analysis. The different models that were investigated showed that the variation in daily traffic counts could be explained by weekly cycles. The Box-Tiao modeling approach was applied to quantify the effect of holidays on daily traffic. The results showed that traffic counts were significantly lower for holiday periods. When the different modeling techniques were compared with respect to forecasting with a large forecast horizon, Box-Tiao modeling clearly outperformed the other modeling strategies. Simultaneous modeling of both the underlying reasons of travel, and revealed traffic patterns, certainly is a challenge for further research.
Target :	Researchers ; Professionals ; Students ; General public ; Others
Permalink :	http://hdl.handle.net/2268/133001

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