A review of traffic congestion prediction using artificial intelligence
M Akhtar, S Moridpour - Journal of Advanced Transportation, 2021 - Wiley Online Library
In recent years, traffic congestion prediction has led to a growing research area, especially
of machine learning of artificial intelligence (AI). With the introduction of big data by …
of machine learning of artificial intelligence (AI). With the introduction of big data by …
A brief overview of machine learning methods for short-term traffic forecasting and future directions
Short-term traffic forecasting is a vital part of intelligent transportation systems. Recently, the
combination of unprecedented data availability and the repaid development of machine …
combination of unprecedented data availability and the repaid development of machine …
Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Spatiotemporal forecasting has various applications in neuroscience, climate and
transportation domain. Traffic forecasting is one canonical example of such learning task …
transportation domain. Traffic forecasting is one canonical example of such learning task …
Efficient wind power prediction using machine learning methods: A comparative study
Wind power represents a promising source of renewable energies. Precise forecasting of
wind power generation is crucial to mitigate the challenges of balancing supply and demand …
wind power generation is crucial to mitigate the challenges of balancing supply and demand …
A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models
This study aims to develop an assumption-free data-driven model to accurately forecast
COVID-19 spread. Towards this end, we firstly employed Bayesian optimization to tune the …
COVID-19 spread. Towards this end, we firstly employed Bayesian optimization to tune the …
Short-term traffic forecasting: Where we are and where we're going
EI Vlahogianni, MG Karlaftis, JC Golias - Transportation Research Part C …, 2014 - Elsevier
Since the early 1980s, short-term traffic forecasting has been an integral part of most
Intelligent Transportation Systems (ITS) research and applications; most effort has gone into …
Intelligent Transportation Systems (ITS) research and applications; most effort has gone into …
Variational graph neural networks for road traffic prediction in intelligent transportation systems
As one of the most important applications of industrial Internet of Things, intelligent
transportation system aims to improve the efficiency and safety of transportation networks. In …
transportation system aims to improve the efficiency and safety of transportation networks. In …
An autoencoder and LSTM-based traffic flow prediction method
Smart cities can effectively improve the quality of urban life. Intelligent Transportation System
(ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow …
(ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow …
Data-driven transfer learning framework for estimating on-ramp and off-ramp traffic flows
To develop the most appropriate control strategy and monitor, maintain, and evaluate the
traffic performance of the freeway weaving areas, state and local Departments of …
traffic performance of the freeway weaving areas, state and local Departments of …
Macroscopic traffic flow modeling with physics regularized Gaussian process: A new insight into machine learning applications in transportation
Despite the wide implementation of machine learning (ML) technique in traffic flow modeling
recently, those data-driven approaches often fall short of accuracy in the cases with a small …
recently, those data-driven approaches often fall short of accuracy in the cases with a small …