期刊
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
卷 36, 期 6, 页码 538-550出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.compenvurbsys.2012.08.005
关键词
Kernel regression; Missing data; Spatio-temporal; Transport; Journey time; Imputation
类别
资金
- EPSRC [EP/G023212/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/G023212/1] Funding Source: researchfish
As more and more real time spatio-temporal datasets become available at increasing spatial and temporal resolutions, the provision of high quality, predictive information about spatio-temporal processes becomes an increasingly feasible goal. However, many sensor networks that collect spatio-temporal information are prone to failure, resulting in missing data. To complicate matters, the missing data is often not missing at random, and is characterised by long periods where no data is observed. The performance of traditional univariate forecasting methods such as ARIMA models decreases with the length of the missing data period because they do not have access to local temporal information. However, if spatio-temporal autocorrelation is present in a space-time series then spatio-temporal approaches have the potential to offer better forecasts. In this paper, a non-parametric spatio-temporal kernel regression model is developed to forecast the future unit journey time values of road links in central London. UK, under the assumption of sensor malfunction. Only the current traffic patterns of the upstream and downstream neighbouring links are used to inform the forecasts. The model performance is compared with another form of non-parametric regression, K-nearest neighbours, which is also effective in forecasting under missing data. The methods show promising forecasting performance, particularly in periods of high congestion. (C) 2012 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据