Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges
In the last few years, there has been an exponential increase in the usage of the
autonomous vehicles across the globe. It is due to an exponential increase in the popularity …
autonomous vehicles across the globe. It is due to an exponential increase in the popularity …
Intrinsic mechanism and multiphysics analysis of electromagnetic wave absorbing materials: New horizons and breakthrough
L Xia, Y Feng, B Zhao - Journal of Materials Science & Technology, 2022 - Elsevier
Electromagnetic absorbers (EMA) have driven the development of Electromagnetic (EM)
technology and advanced EM devices. Utilizing the EM energy conversion of EM absorbers …
technology and advanced EM devices. Utilizing the EM energy conversion of EM absorbers …
A hybrid deep learning model with attention-based conv-LSTM networks for short-term traffic flow prediction
Accurate short-time traffic flow prediction has gained gradually increasing importance for
traffic plan and management with the deployment of intelligent transportation systems (ITSs) …
traffic plan and management with the deployment of intelligent transportation systems (ITSs) …
A survey on modern deep neural network for traffic prediction: Trends, methods and challenges
In this modern era, traffic congestion has become a major source of severe negative
economic and environmental impact for urban areas worldwide. One of the most efficient …
economic and environmental impact for urban areas worldwide. One of the most efficient …
Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach
Short-term passenger demand forecasting is of great importance to the on-demand ride
service platform, which can incentivize vacant cars moving from over-supply regions to over …
service platform, which can incentivize vacant cars moving from over-supply regions to over …
Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks
H Yu, Z Wu, S Wang, Y Wang, X Ma - Sensors, 2017 - mdpi.com
Predicting large-scale transportation network traffic has become an important and
challenging topic in recent decades. Inspired by the domain knowledge of motion prediction …
challenging topic in recent decades. Inspired by the domain knowledge of motion prediction …
DeepPF: A deep learning based architecture for metro passenger flow prediction
This study aims to combine the modeling skills of deep learning and the domain knowledge
in transportation into prediction of metro passenger flow. We present an end-to-end deep …
in transportation into prediction of metro passenger flow. We present an end-to-end deep …
Traffic flow prediction with big data: A deep learning approach
Accurate and timely traffic flow information is important for the successful deployment of
intelligent transportation systems. Over the last few years, traffic data have been exploding …
intelligent transportation systems. Over the last few years, traffic data have been exploding …
Deep architecture for traffic flow prediction: Deep belief networks with multitask learning
Traffic flow prediction is a fundamental problem in transportation modeling and
management. Many existing approaches fail to provide favorable results due to being: 1) …
management. Many existing approaches fail to provide favorable results due to being: 1) …
Short-term traffic forecasting: Where we are and where we're going
EI Vlahogianni, MG Karlaftis, JC Golias - Transportation Research Part C …, 2014 - Elsevier
Since the early 1980s, short-term traffic forecasting has been an integral part of most
Intelligent Transportation Systems (ITS) research and applications; most effort has gone into …
Intelligent Transportation Systems (ITS) research and applications; most effort has gone into …