Diffusion models for time-series applications: a survey
Diffusion models, a family of generative models based on deep learning, have become
increasingly prominent in cutting-edge machine learning research. With distinguished …
increasingly prominent in cutting-edge machine learning research. With distinguished …
Traffic prediction using multifaceted techniques: A survey
Road transportation is the largest and complex nonlinear entity of the traffic management
system. Accurate prediction of traffic-related information is necessary for an effective …
system. Accurate prediction of traffic-related information is necessary for an effective …
GE-GAN: A novel deep learning framework for road traffic state estimation
D Xu, C Wei, P Peng, Q Xuan, H Guo - Transportation Research Part C …, 2020 - Elsevier
Traffic state estimation is a crucial elemental function in Intelligent Transportation Systems
(ITS). However, the collected traffic state data are often incomplete in the real world. In this …
(ITS). However, the collected traffic state data are often incomplete in the real world. In this …
A deep spatio-temporal meta-learning model for urban traffic revitalization index prediction in the COVID-19 pandemic
The COVID-19 pandemic is a major global public health problem that has caused hardship
to people's normal production and life. Predicting the traffic revitalization index can provide …
to people's normal production and life. Predicting the traffic revitalization index can provide …
A traffic flow dependency and dynamics based deep learning aided approach for network-wide traffic speed propagation prediction
The information of network-wide future traffic speed distribution and its propagation is
beneficial to develop proactive traffic congestion management strategies. However …
beneficial to develop proactive traffic congestion management strategies. However …
Using Kalman filter algorithm for short-term traffic flow prediction in a connected vehicle environment
A Emami, M Sarvi, S Asadi Bagloee - Journal of Modern Transportation, 2019 - Springer
We develop a Kalman filter for predicting traffic flow at urban arterials based on data
obtained from connected vehicles. The proposed algorithm is computationally efficient and …
obtained from connected vehicles. The proposed algorithm is computationally efficient and …
Improving short-term bike sharing demand forecast through an irregular convolutional neural network
As an important task for the management of bike sharing systems, accurate forecast of travel
demand could facilitate dispatch and relocation of bicycles to improve user satisfaction. In …
demand could facilitate dispatch and relocation of bicycles to improve user satisfaction. In …
Forecasting the Spread of COVID-19 and ICU Requirements
Since December 2019, the world is fighting against coronavirus disease (COVID-19). This
disease is caused by a novel coronavirus termed as severe acute respiratory syndrome …
disease is caused by a novel coronavirus termed as severe acute respiratory syndrome …
Short-term traffic flow prediction: An integrated method of econometrics and hybrid deep learning
This study proposes a short-term traffic flow prediction framework. The vector autoregression
(VAR) model based on econometric theory and the CNN-LSTM hybrid neural network model …
(VAR) model based on econometric theory and the CNN-LSTM hybrid neural network model …
[PDF][PDF] Machine learning based prediction of urban flood susceptibility from selected rivers in a tropical catchment area
BN Ekwueme - Civil Engineering Journal, 2022 - researchgate.net
Unexpected flood due to climate change has caused tremendous damage to both lives and
properties, especially in tropical areas. Nigeria Southeastern region has been devastated by …
properties, especially in tropical areas. Nigeria Southeastern region has been devastated by …