Survey on traffic prediction in smart cities

AM Nagy, V Simon - Pervasive and Mobile Computing, 2018 - Elsevier
The rapid development in machine learning and in the emergence of new data sources
makes it possible to examine and predict traffic conditions in smart cities more accurately …

Revolutionizing future connectivity: A contemporary survey on AI-empowered satellite-based non-terrestrial networks in 6G

S Mahboob, L Liu - IEEE Communications Surveys & Tutorials, 2024 - ieeexplore.ieee.org
Non-Terrestrial Networks (NTN) are expected to be a critical component of 6th Generation
(6G) networks, providing ubiquitous, continuous, and scalable services. Satellites emerge as …

Spatial-temporal aware inductive graph neural network for C-ITS data recovery

W Liang, Y Li, K Xie, D Zhang, KC Li… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
With the prevalence of Intelligent Transportation Systems (ITS), massive sensors are
deployed on roadside, vehicles, and infrastructures. One key challenge is imputing several …

[HTML][HTML] Long-term traffic flow forecasting using a hybrid CNN-BiLSTM model

M Méndez, MG Merayo, M Núñez - Engineering Applications of Artificial …, 2023 - Elsevier
The increase of road traffic in large cities during the last years has produced that long and
short-term traffic flow forecasting is a critical need for the authorities. The availability of good …

Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems

A Boukerche, Y Tao, P Sun - Computer networks, 2020 - Elsevier
In recent years, the Intelligent transportations system (ITS) has received considerable
attention, due to higher demands for road safety and efficiency in highly interconnected road …

Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification

J Guo, W Huang, BM Williams - Transportation Research Part C: Emerging …, 2014 - Elsevier
Short term traffic flow forecasting has received sustained attention for its ability to provide the
anticipatory traffic condition required for proactive traffic control and management. Recently …

A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data

CN Babu, BE Reddy - Applied Soft Computing, 2014 - Elsevier
A suitable combination of linear and nonlinear models provides a more accurate prediction
model than an individual linear or nonlinear model for forecasting time series data …

Unidirectional and bidirectional LSTM models for short‐term traffic prediction

RL Abduljabbar, H Dia, PW Tsai - Journal of Advanced …, 2021 - Wiley Online Library
This paper presents the development and evaluation of short‐term traffic prediction models
using unidirectional and bidirectional deep learning long short‐term memory (LSTM) neural …

Traffic flow prediction for road transportation networks with limited traffic data

A Abadi, T Rajabioun… - IEEE transactions on …, 2014 - ieeexplore.ieee.org
Obtaining accurate information about current and near-term future traffic flows of all links in a
traffic network has a wide range of applications, including traffic forecasting, vehicle …

Short-term prediction of lane-level traffic speeds: A fusion deep learning model

Y Gu, W Lu, L Qin, M Li, Z Shao - Transportation research part C: emerging …, 2019 - Elsevier
Accurate and robust short-term traffic prediction is an important part of advanced traveler
information systems. With the development of intelligent navigation and autonomous driving …