A hybrid deep learning based traffic flow prediction method and its understanding
Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic
flow with big data. While existing DNN models can provide better performance than shallow …
flow with big data. While existing DNN models can provide better performance than shallow …
A survey of hybrid deep learning methods for traffic flow prediction
Y Shi, H Feng, X Geng, X Tang, Y Wang - Proceedings of the 2019 3rd …, 2019 - dl.acm.org
Traffic flow prediction using big data and deep learning attracts great attentions in recent
years. Researchers show that DNN models can provide better traffic prediction accuracy …
years. Researchers show that DNN models can provide better traffic prediction accuracy …
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 …
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 …
Deeptrend: A deep hierarchical neural network for traffic flow prediction
In this paper, we consider the temporal pattern in traffic flow time series, and implement a
deep learning model for traffic flow prediction. Detrending based methods decompose …
deep learning model for traffic flow prediction. Detrending based methods decompose …
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) …
An interpretable model for short term traffic flow prediction
Predicting short term traffic flow to improve traffic control is a research problem attracting
increased attention over the past 30 years. With increasing number of traffic data acquisition …
increased attention over the past 30 years. With increasing number of traffic data acquisition …
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 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 …
A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial–temporal data features
Predicting traffic flow is one of the fundamental needs to comfortable travel, but this task is
challenging in vehicular cyber–physical systems because of ever-increasing uncertain traffic …
challenging in vehicular cyber–physical systems because of ever-increasing uncertain traffic …