scholarly journals The Sequence-to-Sequence Architecture with An Embedded Module for Long-Term Traffic Speed Forecasting with Missing Data

2021 ◽  
Author(s):  
Ge Zheng ◽  
Wei Koong Chai ◽  
Vasilis Katos
Keyword(s):  
Missing Data ◽  
Sequence Architecture ◽  
Traffic Speed

scholarly journals Deep-Learning-Based Real-Time Road Traffic Prediction Using Long-Term Evolution Access Data

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5327 ◽  
Author(s):  
Byoungsuk Ji ◽  
Ellen J. Hong
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Road Traffic ◽  
Learning Model ◽  
Long Term Evolution ◽  
The Road ◽  
Term Evolution ◽  
Traffic Speed ◽  
Access Data

In this paper, we propose a method for deep-learning-based real-time road traffic predictions using long-term evolution (LTE) access data. The proposed system generates a road traffic speed learning model based on road speed data and historical LTE data collected from a plurality of base stations located within a predetermined radius from the road. Real-time LTE data were the input for the generated learning model in order to predict the real-time speed of traffic. Since the system was developed using a time-series-based road traffic speed learning model based on LTE data from the past, it is possible for it to be used for a road where the environment has changed. Moreover, even on roads where the collection of traffic data is invalid, such as a radio shadow area, it is possible to directly enter real-time wireless communications data into the traffic speed learning model to predict the traffic speed on the road in real time, and in turn, raise the accuracy of real-time road traffic predictions.

scholarly journals Concurrent Spatiotemporal Daily Land Use Regression Modeling and Missing Data Imputation of Fine Particulate Matter Using Distributed Space Time Expectation Maximization

2018 ◽  
Author(s):  
Seyed Mahmood Taghavi-Shahri ◽  
Alessandro Fassò ◽  
Behzad Mahaki ◽  
Heresh Amini
Keyword(s):  
Land Use ◽  
Particulate Matter ◽  
Missing Data ◽  
Exposure Assessment ◽  
Expectation Maximization ◽  
Space Time ◽  
Data Imputation ◽  
Land Use Regression ◽  
Missing Data Imputation

AbstractGraphical AbstractLand use regression (LUR) has been widely applied in epidemiologic research for exposure assessment. In this study, for the first time, we aimed to develop a spatiotemporal LUR model using Distributed Space Time Expectation Maximization (D-STEM). This spatiotemporal LUR model examined with daily particulate matter ≤ 2.5 μm (PM2.5) within the megacity of Tehran, capital of Iran. Moreover, D-STEM missing data imputation was compared with mean substitution in each monitoring station, as it is equivalent to ignoring of missing data, which is common in LUR studies that employ regulatory monitoring stations’ data. The amount of missing data was 28% of the total number of observations, in Tehran in 2015. The annual mean of PM2.5 concentrations was 33 μg/m3. Spatiotemporal R-squared of the D-STEM final daily LUR model was 78%, and leave-one-out cross-validation (LOOCV) R-squared was 66%. Spatial R-squared and LOOCV R-squared were 89% and 72%, respectively. Temporal R-squared and LOOCV R-squared were 99.5% and 99.3%, respectively. Mean absolute error decreased 26% in imputation of missing data by using the D-STEM final LUR model instead of mean substitution. This study reveals competence of the D-STEM software in spatiotemporal missing data imputation, estimation of temporal trend, and mapping of small scale (20 × 20 meters) within-city spatial variations, in the LUR context. The estimated PM2.5 concentrations maps could be used in future studies on short- and/or long-term health effects. Overall, we suggest using D-STEM capabilities in increasing LUR studies that employ data of regulatory network monitoring stations.Highlights-First Land Use Regression using D-STEM, a recently introduced statistical software-Assess D-STEM in spatiotemporal modeling, mapping, and missing data imputation-Estimate high resolution (20×20 m) daily maps for exposure assessment in a megacity-Provide both short- and long-term exposure assessment for epidemiological studies

Missing Data in Long-term Follow-up of Patients with Eating Disorders Using the Body Attitude Test

2012 ◽  
Vol 21 (3) ◽  
pp. 224-229 ◽  
Author(s):  
Candida Geerdens ◽  
Johan Vanderlinden ◽  
Guido Pieters ◽  
Amber De Herdt ◽  
Michel Probst
Keyword(s):  
Eating Disorders ◽  
Missing Data ◽  
The Body ◽  
Body Attitude ◽  
Attitude Test ◽  
Long Term Follow Up ◽  
Body Attitude Test

MISSING DATA AND DROPOUTS IN A LONG-TERM, DOUBLE-BLIND SCHIZOPHRENIA STUDY COMPARING ZIPRASIDONE AND HALOPERIDOL

2008 ◽  
Vol 102 (1-3) ◽  
pp. 174
Author(s):  
Steven Potkin ◽  
Cynthia Siu ◽  
Elizabeth Pappadopulos
Keyword(s):  
Missing Data ◽  
Double Blind

scholarly journals BASIC STAGES OF SCIENTIFIC THOUGHT DEVELOPMENT ON PARAMETERS PREDICTION OF HIGHWAY SYSTEMS

2018 ◽  
Vol 2018 (7) ◽  
pp. 63-69
Author(s):  
Татьяна Самисько ◽  
Tat'yana Samis'ko ◽  
Марат Курмаев ◽  
Marat Kurmaev
Keyword(s):  
Traffic Safety ◽  
Efficient Solutions ◽  
Traffic Intensity ◽  
Traffic System ◽  
Term Prediction ◽  
Traffic Speed ◽  
The Republic ◽  
Quantitative Changes ◽  
Long Term Prediction

In this work there is considered a parameter prediction of highway systems and methods used at the account of traffic intensity. A comprehensive model of the transport system structural diagram of the Republic and the interconnec-tion between its separate elements with the notation of organization environment of a traffic system with its constituents is shown. It allows solving problems of traffic organization and ensuring traffic safety at mini-mum costs scientifically substantiated. On the basis of the study of qualitative and quantitative changes between a control system and a controlled one for traffic system efficient functioning there are defined advisable and efficient solutions. The characteristic of traffic system parameters is shown which is presented by three groups including a charac-teristic of highway states, structure and transport ve-hicle speed, traffic intensity and the amount of research works. There are emphasized and described typical peculiarities of methods used at the prediction of traffic intensity, traffic speed, and at the elimination of exist-ing drawbacks. The analysis of considered methods, parameters, models and assessments on a long-term prediction of highway systems allowed defining advantages and dis-advantages of existing methods, revealing a basic tool of all methods – a diagram of extrapolation, offering a suitable method with the purpose of current drawback elimination, presenting a model (formula) for a long-term prediction of a traffic speed, defining the most promising method for a long-term forecast of design loads. The authors of the paper have offered a model for a long-term prediction of a traffic speed, there is considered and characterized an attitude in the system of “man-motor car- traffic environment” in the course of its evolution.

scholarly journals DINEOF reconstruction for creating long-term cloud-free sea surface temperature data records: A Case study in Lombok Strait, Indonesia

2021 ◽  
Vol 893 (1) ◽  
pp. 012069
Author(s):  
Yochi Okta Andrawina ◽  
Ratu Almira Kismawardhani ◽  
Hasti Amrih Rejeki
Keyword(s):  
Missing Data ◽  
Climate Variability ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Satellite Image ◽  
Empirical Orthogonal Functions ◽  
Sea Surface ◽  
Series Data ◽  
Data Record

Abstract A long-term reliable sea surface temperature (SST) satellite data record is requisite resources for monitoring to understand climate variability. Creating a long-term data record especially for climate variability requires a combination of multiple satellite products. Consequently, missing data issues are inevitable. Hence, DINEOF (Data Interpolating Empirical Orthogonal Functions) has been applied to attain a complete and coherent multi-sensor SST data record with EOF-based technique by reconstructing the missing data. Unfortunately, the technique can lead to large discontinuities in the data reconstruction due to images depiction within long time series data. For that reason, filtering the temporal covariance matrix had been applied to reduce the spurious variability and more realistic reconstructions are obtained. However, this approach has not yet tested in tropical region with higher evaporation which cause incomplete satellite image coverage. Therefore, the objective of this research is to reconstruct SST of Lombok strait with data gaps up to 58.16% in one year. It is successfully reconstructed until the last iteration of 42 optimal EOF modes with the convergence achieved up to 0.9806×10-3, including previous set-aside data for internal cross-validation. The results highlight that the DINEOF method can effectively reconstruct SST data in Lombok Strait.

Twitter Based Capital Market Analysis Using Cloud Statistics

2021 ◽  
pp. 726-733
Author(s):  
Sangeeta Gupta ◽  
Rajanikanth Aluvalu
Keyword(s):  
Missing Data ◽  
Social Networking ◽  
Missing Values ◽  
Social Networking Site ◽  
Unstructured Data ◽  
Modern World ◽  
Cloud Environment ◽  
Statistical Measure ◽  
Smart Working

People in the modern world are attracted towards smart working and earning environments rather than having a long-term perception. The goal of this work is to address the challenge of providing better inputs to the customers interested to investing in the share market to earn better returns on investments. The Twitter social networking site is chosen to develop the proposed environment as a majority of the customers tweet about their opinions. A huge set of data across various companies that take inputs from Twitter are processed and stored in the cloud environment for efficient analysis and assessment. A statistical measure is used to signal the worth of investing in a particular stock based on the outcomes obtained. Also, rather than ignoring the missing values and unstructured data, the proposed work analyzes every single entity to enable the customers to take worthy decisions. Tweets in the range of 1 to 100,000 are taken to perform analysis and it is observed from the results that for a maximum of 100,000 tweets, the number of missing is identified as 2,524 and the statistical measure to fill in the missing values is calculated based on the particular missing data record, the count of all data records, and the total number of records. If the outcome of the measure is obtained as a negative, then proceeding with an investment is not recommended. The findings of this work will help the share market investors to earn better profits.

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