RT Journal Article
T1 On the imputation of missing data for road traffic forecasting: New insights and novel techniques: 												New insights and novel techniques
A1 Laña, Ibai
A1 Olabarrieta, Ignacio (Iñaki)
A1 Vélez, Manuel
A1 Del Ser, Javier
AB Vehicle flow forecasting is of crucial importance for the management of road traffic in complex urban networks, as well as a useful input for route planning algorithms. In general traffic predictive models rely on data gathered by different types of sensors placed on roads, which occasionally produce faulty readings due to several causes, such as malfunctioning hardware or transmission errors. Filling in those gaps is relevant for constructing accurate forecasting models, a task which is engaged by diverse strategies, from a simple null value imputation to complex spatio-temporal context imputation models. This work elaborates on two machine learning approaches to update missing data with no gap length restrictions: a spatial context sensing model based on the information provided by surrounding sensors, and an automated clustering analysis tool that seeks optimal pattern clusters in order to impute values. Their performance is assessed and compared to other common techniques and different missing data generation models over real data captured from the city of Madrid (Spain). The newly presented methods are found to be fairly superior when portions of missing data are large or very abundant, as occurs in most practical cases.
SN 0968-090X
YR 2018
FD 2018-05
LA eng
NO Laña , I , Olabarrieta , I , Vélez , M & Del Ser , J 2018 , ' On the imputation of missing data for road traffic forecasting: New insights and novel techniques : New insights and novel techniques ' , Transportation Research Part C: Emerging Technologies , vol. 90 , pp. 18-33 . https://doi.org/10.1016/j.trc.2018.02.021
NO Publisher Copyright: © 2018 Elsevier Ltd
DS TECNALIA Publications
RD 1 jul 2024