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 …
Nonstationary time series transformation methods: An experimental review
Data preprocessing is a crucial step for mining and learning from data, and one of its primary
activities is the transformation of data. This activity is very important in the context of time …
activities is the transformation of data. This activity is very important in the context of time …
Optimized graph convolution recurrent neural network for traffic prediction
Traffic prediction is a core problem in the intelligent transportation system and has broad
applications in the transportation management and planning, and the main challenge of this …
applications in the transportation management and planning, and the main challenge of this …
Empirical mode decomposition based deep learning for electricity demand forecasting
J Bedi, D Toshniwal - IEEE access, 2018 - ieeexplore.ieee.org
Electricity is of great significance for national economic, social, and technological activities,
such as material production, healthcare, and education. The nationwide electricity demand …
such as material production, healthcare, and education. The nationwide electricity demand …
A customized deep learning approach to integrate network-scale online traffic data imputation and prediction
Online data imputation and traffic prediction based on real-time data streams are essential
for the intelligent transportation systems, particularly online navigation applications based …
for the intelligent transportation systems, particularly online navigation applications based …
A novel STFSA-CNN-GRU hybrid model for short-term traffic speed prediction
C Ma, Y Zhao, G Dai, X Xu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Short-term traffic speed prediction is fundamental to intelligent transportation systems (ITS),
and the accuracy of the model largely determines the performance of real-time traffic control …
and the accuracy of the model largely determines the performance of real-time traffic control …
A novel short-term traffic prediction model based on SVD and ARIMA with blockchain in industrial internet of Things
With the construction and development of smart cities, accurate and real-time traffic
prediction plays a vital role in urban traffic. However, traffic data has the characteristics of …
prediction plays a vital role in urban traffic. However, traffic data has the characteristics of …
Vehicle speed prediction by two-level data driven models in vehicular networks
Vehicle speed prediction provides important information for many intelligent vehicular and
transportation applications. Accurate on-road vehicle speed prediction is challenging …
transportation applications. Accurate on-road vehicle speed prediction is challenging …
Truck traffic flow prediction based on LSTM and GRU methods with sampled GPS data
S Wang, J Zhao, C Shao, C Dong, C Yin - Ieee Access, 2020 - ieeexplore.ieee.org
Given the enormous traffic issues, such as congestion and crashes, resulting from the
conflicts between trucks and passenger cars, an accurate and reliable prediction of truck …
conflicts between trucks and passenger cars, an accurate and reliable prediction of truck …
Network-wide traffic flow estimation with insufficient volume detection and crowdsourcing data
With the rapid development of urbanization and modernization, it is increasingly crucial to
sense network-wide traffic. Network-wide traffic volume information is of great benefit for …
sense network-wide traffic. Network-wide traffic volume information is of great benefit for …