Traffic flow prediction models–A review of deep learning techniques
AA Kashyap, S Raviraj, A Devarakonda… - Cogent …, 2022 - Taylor & Francis
Traffic flow prediction is an essential part of the intelligent transport system. This is the
accurate estimation of traffic flow in a given region at a particular interval of time in the future …
accurate estimation of traffic flow in a given region at a particular interval of time in the future …
Multivariate Time-Series Forecasting: A Review of Deep Learning Methods in Internet of Things Applications to Smart Cities
V Papastefanopoulos, P Linardatos… - Smart Cities, 2023 - mdpi.com
Smart cities are urban areas that utilize digital solutions to enhance the efficiency of
conventional networks and services for sustainable growth, optimized resource …
conventional networks and services for sustainable growth, optimized resource …
Effective energy consumption forecasting using empirical wavelet transform and long short-term memory
L Peng, L Wang, D Xia, Q Gao - energy, 2022 - Elsevier
Energy consumption is an important issue of global concern. Accurate energy consumption
forecasting can help balance energy demand and energy production. Although there are …
forecasting can help balance energy demand and energy production. Although there are …
Machine learning-based traffic prediction models for intelligent transportation systems
A Boukerche, J Wang - Computer Networks, 2020 - Elsevier
Abstract Intelligent Transportation Systems (ITS) have attracted an increasing amount of
attention in recent years. Thanks to the fast development of vehicular computing hardware …
attention in recent years. Thanks to the fast development of vehicular computing hardware …
Privacy-aware traffic flow prediction based on multi-party sensor data with zero trust in smart city
F Wang, G Li, Y Wang, W Rafique… - ACM Transactions on …, 2023 - dl.acm.org
With the continuous increment of city volume and size, a number of traffic-related urban units
(eg, vehicles, roads, buildings, etc.) are emerging rapidly, which plays a heavy burden on …
(eg, vehicles, roads, buildings, etc.) are emerging rapidly, which plays a heavy burden on …
Interpretable spatio-temporal attention LSTM model for flood forecasting
Modeling interpretable artificial intelligence (AI) for flood forecasting represents a serious
challenge: both accuracy and interpretability are indispensable. Because of the uncertainty …
challenge: both accuracy and interpretability are indispensable. Because of the uncertainty …
A novel reinforced dynamic graph convolutional network model with data imputation for network-wide traffic flow prediction
Traffic data missing issues due to unpredictable equipment failure, extreme weather, and
other reasons have brought great challenges to traffic flow prediction modeling. In this …
other reasons have brought great challenges to traffic flow prediction modeling. In this …
Congestion prediction for smart sustainable cities using IoT and machine learning approaches
Congestion on road networks has a negative impact on sustainability in many cities through
the exacerbation of air pollution. Smart congestion management allows road users to avoid …
the exacerbation of air pollution. Smart congestion management allows road users to avoid …
Multiscale attention-based LSTM for ship motion prediction
T Zhang, XQ Zheng, MX Liu - Ocean Engineering, 2021 - Elsevier
Ship motion prediction is applied to the shipboard stabilized platform to keep the equipment
on the platform stable all the time, which is of great practical significance to the safety and …
on the platform stable all the time, which is of great practical significance to the safety and …
Short-term forecasting and uncertainty analysis of wind power based on long short-term memory, cloud model and non-parametric kernel density estimation
B Gu, T Zhang, H Meng, J Zhang - Renewable Energy, 2021 - Elsevier
Conventional wind power forecasting (WPF) methods adopt deterministic forecasting
methods to produce a definite value of wind power output at a future time instant. However …
methods to produce a definite value of wind power output at a future time instant. However …