Journal ArticleDOI
Missing data imputation for traffic flow based on combination of fuzzy neural network and rough set theory
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Current traffic flow analysis and modeling are important key steps for intelligent transportation system (ITS) accuracy and modeling and are one of the most critical issues in the appl...About:
This article is published in Journal of Intelligent Transportation Systems.The article was published on 2021-09-03. It has received 39 citations till now. The article focuses on the topics: Imputation (statistics) & Missing data.read more
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A multi-view bidirectional spatiotemporal graph network for urban traffic flow imputation
TL;DR: A novel multi-view bidirectional spatiotemporal graph network called Multi-BiSTGN is proposed to impute urban traffic data with complex missing patterns and outperformed ten existing baselines under different missing types and missing rates.
Journal ArticleDOI
A distribution model for shared parking in residential zones that considers the utilization rate and the walking distance
TL;DR: A double-objective model is proposed that considers both the utilizing rate and the walking distance of parking spaces in a central business district of Harbin and demonstrates that the proposed model increases the occupying rates of parking lots in residential zones while decreasing the walk distance.
Journal ArticleDOI
Missing value imputation affects the performance of machine learning: A review and analysis of the literature (2010–2021)
Md. Kamrul Hasan,Md. Ashraful Alam,Shidhartho Roy,Aishwariya Dutta,Md. Tasnim Jawad,Sunanda Das +5 more
TL;DR: In this article, the authors conduct a rigorous review and analysis of the state-of-the-art Missing Value Imputation (MVI) methods in the literature published in the last decade and select 191 articles for review using the well-known Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) technique.
Journal ArticleDOI
Stabilizing mixed cooperative adaptive cruise control traffic flow to balance capacity using car-following model
Yanyan Qin,Hao Wang +1 more
TL;DR: In this paper, an analytical framework on string stability of mixed traffic is proposed for understanding traffic flow dynamics, while its analytical study for mixed traffic has been deficient, and the authors focus on the analytical framework for string stability in mixed traffic.
Journal ArticleDOI
Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns
Tong Nie,Guoyang Qin,Jian Sun +2 more
TL;DR: Wang et al. as discussed by the authors proposed an innovative nonconvex truncated Schatten p-norm for tensors (TSpN) to approximate tensor rank and impute missing spatio-temporal traffic data under the LRTC framework.
References
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Book
A practical guide to splines
TL;DR: This book presents those parts of the theory which are especially useful in calculations and stresses the representation of splines as linear combinations of B-splines as well as specific approximation methods, interpolation, smoothing and least-squares approximation, the solution of an ordinary differential equation by collocation, curve fitting, and surface fitting.
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ALS issues in clinical trials. Missing data.
John L.P. Thompson,Gilberto Levy +1 more
TL;DR: The importance of missing data in RCTs is emphasized, and how the problem can be handled in an unbiased way by imputation procedures is discussed, and some recommendations for trial design and conduct are made that are tailored to R CTs for ALS.
Journal ArticleDOI
Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results
Billy M. Williams,Lester A Hoel +1 more
TL;DR: The theoretical basis for modeling univariate traffic condition data streams as seasonal autoregressive integrated moving average processes as well as empirical results using actual intelligent transportation system data are presented and found to be consistent with the theoretical hypothesis.
Journal ArticleDOI
Forecasting seasonals and trends by exponentially weighted moving averages
TL;DR: In this article, a systematic development of the forecasting expressions for exponential weighted moving averages is presented. But the methods for series with no trend, or additive or multiplicative trend are examined.
Journal Article
Analysis of freeway traffic time-series data by using box-jenkins techniques
Mohammed S Ahmed,Allen R Cook +1 more
TL;DR: The ARIMA models were found to be more accurate in representing freeway time-series data, in terms of mean absolute error and mean square error, than moving-average, double-exponential smoothing, and Trigg and Leach adaptive models.