Survey on traffic prediction in smart cities
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 …
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
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 …
(6G) networks, providing ubiquitous, continuous, and scalable services. Satellites emerge as …
Spatial-temporal aware inductive graph neural network for C-ITS data recovery
With the prevalence of Intelligent Transportation Systems (ITS), massive sensors are
deployed on roadside, vehicles, and infrastructures. One key challenge is imputing several …
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
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 …
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 …
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 …
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
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 …
model than an individual linear or nonlinear model for forecasting time series data …
Unidirectional and bidirectional LSTM models for short‐term traffic prediction
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 …
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 …
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
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 …
information systems. With the development of intelligent navigation and autonomous driving …