Missing data problem widely exists in many traffic information systems, which brings great trouble to further studies. To solve this problem, this paper proposes a Bayesian Principal Component Analysis (BPCA) based missing value imputing method to impute the incomplete traffic flow volume data collected in Beijing. Intuitively, this method takes an appropriate tradeoff between the historical and periodic information when imputing missing data. Experiments prove that the proposed method provides significant better imputing performance than two other frequently used imputing methods: historical imputing and spline imputing.
A BPCA based missing value imputing method for traffic flow volume data
2008 IEEE Intelligent Vehicles Symposium ; 985-990
2008-06-01
259789 byte
Conference paper
Electronic Resource
English
A BPCA Based Missing Value Imputing Method for Traffic Flow Volume Data
British Library Conference Proceedings | 2008
|Efficient missing data imputing for traffic flow by considering temporal and spatial dependence
Online Contents | 2013
|Improving traffic time‐series predictability by imputing continuous non‐random missing data
Wiley | 2023
|Improving traffic time‐series predictability by imputing continuous non‐random missing data
DOAJ | 2023
|Method for Imputing Missing Data using Online Calibration for Urban Freeway Control
Transportation Research Record | 2018
|