Spatiotemporal traffic state prediction based on discriminatively pre-trained deep neural networks
M Elhenawy, H Rakha - Advances in Science, Technology and …, 2017 - eprints.qut.edu.au
The availability of traffic data and computational advances now make it possible to build
data-driven models that capture the evolution of the state of traffic along modeled stretches …
data-driven models that capture the evolution of the state of traffic along modeled stretches …
Stretch-wide traffic state prediction using discriminatively pre-trained deep neural networks
M Elhenawy, H Rakha - 2016 IEEE 19th International …, 2016 - ieeexplore.ieee.org
The paper adopts the state-of-the-art machine learning deep neural network to model the
evolution of the traffic state along a 21.1 miles long stretch of the I-15 highway. The built …
evolution of the traffic state along a 21.1 miles long stretch of the I-15 highway. The built …
Traffic state prediction using one-dimensional convolution neural networks and long short-term memory
Traffic prediction is a vitally important keystone of an intelligent transportation system (ITS). It
aims to improve travel route selection, reduce overall carbon emissions, mitigate congestion …
aims to improve travel route selection, reduce overall carbon emissions, mitigate congestion …
Short-term traffic state prediction from latent structures: Accuracy vs. efficiency
Recently, deep learning models have shown promising performances in many research
areas, including traffic states prediction, due to their ability to model complex nonlinear …
areas, including traffic states prediction, due to their ability to model complex nonlinear …
Applications of deep learning models for traffic prediction problems
R Rahman - 2019 - stars.library.ucf.edu
Deep learning coupled with existing sensors based multiresolution traffic data and future
connected technologies has immense potential to improve traffic operation and …
connected technologies has immense potential to improve traffic operation and …
Survey of neural network‐based models for short‐term traffic state prediction
LNN Do, N Taherifar, HL Vu - Wiley Interdisciplinary Reviews …, 2019 - Wiley Online Library
Traffic state prediction is a key component in intelligent transport systems (ITS) and has
attracted much attention over the last few decades. Advances in computational power and …
attracted much attention over the last few decades. Advances in computational power and …
Predicting short-term traffic speed using a deep neural network to accommodate citywide spatio-temporal correlations
The traffic speed on a given road segment is affected by the current and past speeds on
nearby segments, and the influence further cascades into the rest of a transport network …
nearby segments, and the influence further cascades into the rest of a transport network …
A deep learning-based framework for road traffic prediction
R Benabdallah Benarmas, K Beghdad Bey - The Journal of …, 2024 - Springer
Due to the exponential rise in the number of vehicles and road segments in cities, traffic
prediction becomes more difficult, necessitating the application of sophisticated algorithms …
prediction becomes more difficult, necessitating the application of sophisticated algorithms …
Adarules: Learning rules for real-time road-traffic prediction
R Mena-Yedra, R Gavaldà, J Casas - Transportation Research Procedia, 2017 - Elsevier
Traffic management is being more important than ever, especially in overcrowded big cities
with over-pollution problems and with new unprecedented mobility changes. In this …
with over-pollution problems and with new unprecedented mobility changes. In this …
Traffic Flow Prediction Using Deep Learning Techniques
Abstract In the “Intelligent Transportation System (ITS)”, accurate and real-time traffic flow
prediction is crucial, particularly for traffic control. To develop a smart city, data related to …
prediction is crucial, particularly for traffic control. To develop a smart city, data related to …