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 …
Short-term traffic flow prediction based on optimized deep learning neural network: PSO-Bi-LSTM
Traffic flow prediction is important for urban planning and traffic congestion alleviation as
well as for intelligent traffic management systems. Due to the periodic characteristics and …
well as for intelligent traffic management systems. Due to the periodic characteristics and …
[HTML][HTML] AIS data-driven ship trajectory prediction modelling and analysis based on machine learning and deep learning methods
Maritime transport faces new safety challenges in an increasingly complex traffic
environment caused by large-scale and high-speed ships, particularly with the introduction …
environment caused by large-scale and high-speed ships, particularly with the introduction …
How to build a graph-based deep learning architecture in traffic domain: A survey
In recent years, various deep learning architectures have been proposed to solve complex
challenges (eg spatial dependency, temporal dependency) in traffic domain, which have …
challenges (eg spatial dependency, temporal dependency) in traffic domain, which have …
Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems
A Boukerche, Y Tao, P Sun - Computer networks, 2020 - Elsevier
In recent years, the Intelligent transportations system (ITS) has received considerable
attention, due to higher demands for road safety and efficiency in highly interconnected road …
attention, due to higher demands for road safety and efficiency in highly interconnected road …
Traffic flow prediction by an ensemble framework with data denoising and deep learning model
Accurate traffic flow data is important for traffic flow state estimation, real-time traffic
management and control, etc. Raw traffic flow data collected from inductive detectors may be …
management and control, etc. Raw traffic flow data collected from inductive detectors may be …
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 …
Intelligent traffic management: A review of challenges, solutions, and future perspectives
R Ravish, SR Swamy - Transport and Telecommunication Journal, 2021 - sciendo.com
Congestion of traffic is a key problem faced in a majority of metro cities, especially in the
developing world. Traffic congestion comprises of queues, reduced speeds, and increased …
developing world. Traffic congestion comprises of queues, reduced speeds, and increased …
An improved fuzzy neural network for traffic speed prediction considering periodic characteristic
This paper proposes a new method in construction fuzzy neural network to forecast travel
speed for multi-step ahead based on 2-min travel speed data collected from three remote …
speed for multi-step ahead based on 2-min travel speed data collected from three remote …
Short-term traffic flow forecasting: An experimental comparison of time-series analysis and supervised learning
The literature on short-term traffic flow forecasting has undergone great development
recently. Many works, describing a wide variety of different approaches, which very often …
recently. Many works, describing a wide variety of different approaches, which very often …