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Profit prediction using ARIMA, SARIMA and LSTM models in time series forecasting: A comparison
Time series forecasting using historical data is significantly important nowadays. Many fields
such as finance, industries, healthcare, and meteorology use it. Profit analysis using …
such as finance, industries, healthcare, and meteorology use it. Profit analysis using …
Privacy-preserving generation and publication of synthetic trajectory microdata: A comprehensive survey
The generation of trajectory data has increased dramatically with the advent and
widespread use of GPS-enabled devices. This rich source of data provides invaluable …
widespread use of GPS-enabled devices. This rich source of data provides invaluable …
A systematic survey on big data and artificial intelligence algorithms for intelligent transportation system
Rapid urbanization and globalization have resulted in intolerable congestion and traffic,
necessitating the investigation of Intelligent Transportation Systems (ITS). ITS employs …
necessitating the investigation of Intelligent Transportation Systems (ITS). ITS employs …
Time series big data: a survey on data stream frameworks, analysis and algorithms
Big data has a substantial role nowadays, and its importance has significantly increased
over the last decade. Big data's biggest advantages are providing knowledge, supporting …
over the last decade. Big data's biggest advantages are providing knowledge, supporting …
Aveiro Tech City Living Lab: a communication, sensing, and computing platform for city environments
This article presents the deployment and experimentation architecture of the Aveiro Tech
City Living Lab (ATCLL) in Aveiro, Portugal. This platform comprises a large number of …
City Living Lab (ATCLL) in Aveiro, Portugal. This platform comprises a large number of …
[HTML][HTML] Hybrid graph convolution neural network and branch-and-bound optimization for traffic flow forecasting
In this study, we combine graph optimization and prediction in a single pipeline to
investigate an innovative convolutional graph-based neural network for urban traffic flow …
investigate an innovative convolutional graph-based neural network for urban traffic flow …
[HTML][HTML] Road traffic noise monitoring in a Smart City: Sensor and Model-Based approach
This paper aims to propose a novel Road Traffic Noise Model (RTNM), capable of
dynamically assessing road traffic noise levels from reliable data (hourly traffic volumes and …
dynamically assessing road traffic noise levels from reliable data (hourly traffic volumes and …
SARIMA modelling approach for forecasting of traffic accidents
To achieve greater sustainability of the traffic system, the trend of traffic accidents in road
traffic was analysed. Injuries from traffic accidents are among the leading factors in the …
traffic was analysed. Injuries from traffic accidents are among the leading factors in the …
[HTML][HTML] The Application of Machine Learning and Deep Learning in Intelligent Transportation: A Scientometric Analysis and Qualitative Review of Research Trends
Machine learning (ML) and deep learning (DL) have become very popular in the research
community for addressing complex issues in intelligent transportation. This has resulted in …
community for addressing complex issues in intelligent transportation. This has resulted in …
Traffic congestion forecasting using multilayered deep neural network
This study proposes a multilayered deep neural network (MLDNN) and a congestion index
(CI) based on traffic density factor to forecast traffic congestion directly. Data were collected …
(CI) based on traffic density factor to forecast traffic congestion directly. Data were collected …