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http://dx.doi.org/10.5351/KJAS.2021.34.5.773

Functional regression approach to traffic analysis  

Lee, Injoo (Department of Statistics, Kangwon National University)
Lee, Young K. (Department of Statistics, Kangwon National University)
Publication Information
The Korean Journal of Applied Statistics / v.34, no.5, 2021 , pp. 773-794 More about this Journal
Abstract
Prediction of vehicle traffic volume is very important in planning municipal administration. It may help promote social and economic interests and also prevent traffic congestion costs. Traffic volume as a time-varying trajectory is considered as functional data. In this paper we study three functional regression models that can be used to predict an unseen trajectory of traffic volume based on already observed trajectories. We apply the methods to highway tollgate traffic volume data collected at some tollgates in Seoul, Chuncheon and Gangneung. We compare the prediction errors of the three models to find the best one for each of the three tollgate traffic volumes.
Keywords
auto-covariance; cross-covariance; functional principal components; functional singular components; smooth backfitting;
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1 Choo S, Lee SM, Park YI, and Yun J (2007). A study on methods for constructing weekend origin/destination travel, The Korea Transport Institute.
2 Muller HG and Yao F (2008). Functional additive models, Journal of the American Statistical Association, 103, 1534-1544.   DOI
3 Sohn C and Kim GH (2014). Influences of weather on the inbound traffic volume of a tourist destination, The Korea spatial planning review, 99-111.
4 Jeon JM and Park BU (2020). Additive regression with Hilbertian responses, The Annals of Statistics, 48, 2671-2697.
5 Mammen E, Linton O, and Nielsen JP (1999). The existence and asymptotic properties of a backfitting projection algorithm under weak conditions, The Annals of Statistics, 27, 1443-1490.   DOI
6 Kato T (1995). Perturbation theory for linear operators. Berlin: Springer-Verlag.
7 An SY (2017). A study on the optimal traffic flow by highway section. Master's thesis, Inje University.
8 Hsing T and Eubank R (2015). Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators. Wiley.
9 Hunt JD, McMillan P, Stefan K, and Atkins D (2005). Nature of weekend travel by urban households, 2005 Annual Conference of the Transportation Association of Canada.
10 Park BU, Chen CJ, Tao W, and Muller HG (2018). Singular additive models for function to function regression, Statistica Sinica, 28, 2497-2520.
11 Park MJ (2015). Traffic prediction technology development using big data, Monthly KOTI Magagine on Transport, 42-46.
12 Yang W, Muller HG, and Stadtmuller U (2011). Functional singular component analysis, Journal of the Royal Statistical Society: Series B Statistical Methodology, 73, 303-324.   DOI
13 Yoon SY, Lee CY, Kim HJ, Yook DH, and Kim SR (2017). A study on usability of big data to enhance reliability of regional travel demand forecasting, Korea Research Institute for Human Settlements.