A functional data approach to missing value imputation and outlier detection for traffic flow data

Chiou, Jeng‐Min; Zhang, Yi Chen; Chen, Wan-Hui; Chang, Chiung‐Wen

doi:10.1080/21680566.2014.892847

Cited by 51 publications

(38 citation statements)

References 33 publications

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“…The choice of

ℳ

is usually carried out by maximizing the proportion of the total variability explained by the principal components. According to this criterion, the optimal choice is to retain the first M components, that is,

ℳ = {1, \dots, M}

. However, this is not the only possible choice.…”

Section: Methodsmentioning

confidence: 99%

Control charts for monitoring ship operating conditions and CO₂ emissions based on scalar‐on‐function regression

Capezza

Lepore

Menafoglio

et al. 2020

Appl Stoch Models Bus & Ind

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To respond to the compelling air pollution programs, shipping companies are nowadays setting‐up on their fleets modern multisensor systems that stream massive amounts of observational data, which can be considered as varying over a continuous domain. Motivated by this context, a novel procedure is proposed, which extends classical multivariate techniques to the monitoring of multivariate functional data and a scalar quality characteristic related to them. The proposed procedure is shown to be also applicable in real time and is illustrated by means of a real‐case study in the maritime field on the continuous monitoring of operating conditions (ie, the multivariate functional data) and total CO2 emissions (ie, the scalar quality characteristic) at each voyage of a cruise ship. The real‐time monitoring is particularly helpful for promptly supporting managerial decision making by indicating if and when an anomaly occurs during the navigation.

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“…The choice of

ℳ

ℳ = {1, \dots, M}

. However, this is not the only possible choice.…”

Section: Methodsmentioning

confidence: 99%

Control charts for monitoring ship operating conditions and CO₂ emissions based on scalar‐on‐function regression

Capezza

Lepore

Menafoglio

et al. 2020

Appl Stoch Models Bus & Ind

View full text Add to dashboard Cite

show abstract

“…. , y i+n2,j ) (4) where t i,j is the trend (tendency) value at the ith sample time point on the jth day, f (x) is a certain smooth mapping function, n1 and n2 indicate the length of the smoothing window.…”

Section: A the Long-term Trendmentioning

confidence: 99%

Trend Modeling for Traffic Time Series Analysis: An Integrated Study

Li¹,

Zhang³

et al. 2015

IEEE Trans. Intell. Transport. Syst.

131

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This paper discusses the trend modeling for traffic time series. First, we recount two types of definitions for a long-term trend that appeared in previous studies and illustrate their intrinsic differences. We show that, by assuming an implicit temporal connection among the time series observed at different days/locations, the PCA trend brings several advantages to traffic time series analysis. We also describe and define the so-called short-term trend that cannot be characterized by existing definitions. Second, we sequentially review the role that trend modeling plays in four major problems in traffic time series analysis: abnormal data detection, data compression, missing data imputation, and traffic prediction. The relations between these problems are revealed, and the benefit of detrending is explained. For the first three problems, we summarize our findings in the last ten years and try to provide an integrated framework for future study. For traffic prediction problem, we present a new explanation on why prediction accuracy can be improved at data points representing the short-term trends if the traffic information from multiple sensors can be appropriately used. This finding indicates that the trend modeling is not only a technique to specify the temporal pattern but is also related to the spatial relation of traffic time series.

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“…Matrix and tensor completion methods are growing increasingly popular for traffic data imputation and prediction [16], [17], [18], [19]. Such methods have been shown to provide a reasonably accurate and computationally efficient framework for imputing traffic datasets, though the focus has been primarily on minimizing large-scale reconstruction error arXiv:1812.08739v2 [stat.ML] 8 Jun 2019 rather than quantifying uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation

Rodrigues

Henrickson

Pereira

2019

IEEE Trans. Intell. Transport. Syst.

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Traffic speed data imputation is a fundamental challenge for data-driven transport analysis. In recent years, with the ubiquity of GPS-enabled devices and the widespread use of crowdsourcing alternatives for the collection of traffic data, transportation professionals increasingly look to such usergenerated data for many analysis, planning, and decision support applications. However, due to the mechanics of the data collection process, crowdsourced traffic data such as probe-vehicle data is highly prone to missing observations, making accurate imputation crucial for the success of any application that makes use of that type of data.In this article, we propose the use of multi-output Gaussian processes (GPs) to model the complex spatial and temporal patterns in crowdsourced traffic data. While the Bayesian nonparametric formalism of GPs allows us to model observation uncertainty, the multi-output extension based on convolution processes effectively enables us to capture complex spatial dependencies between nearby road segments. Using 6 months of crowdsourced traffic speed data or "probe vehicle data" for several locations in Copenhagen, the proposed approach is empirically shown to significantly outperform popular state-ofthe-art imputation methods.

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A functional data approach to missing value imputation and outlier detection for traffic flow data

Cited by 51 publications

References 33 publications

Control charts for monitoring ship operating conditions and CO₂ emissions based on scalar‐on‐function regression

Control charts for monitoring ship operating conditions and CO₂ emissions based on scalar‐on‐function regression

Trend Modeling for Traffic Time Series Analysis: An Integrated Study

Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation

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A functional data approach to missing value imputation and outlier detection for traffic flow data

Cited by 51 publications

References 33 publications

Control charts for monitoring ship operating conditions and CO2 emissions based on scalar‐on‐function regression

Control charts for monitoring ship operating conditions and CO2 emissions based on scalar‐on‐function regression

Trend Modeling for Traffic Time Series Analysis: An Integrated Study

Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation

Contact Info

Product

Resources

About

Control charts for monitoring ship operating conditions and CO₂ emissions based on scalar‐on‐function regression

Control charts for monitoring ship operating conditions and CO₂ emissions based on scalar‐on‐function regression