A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Bottleneck Based Gridlock Prediction in an Urban Road Network Using Long Short-Term Memory
2020
Electronics
Preserved Fulltext
It was calibrated with the past actual traffic data collection by using the Simulation of Urban MObility (SUMO) software. ...
A bottleneck indicates the congestion evolution and queue formation, which consequently disturb travel delay and degrade the urban traffic environment and safety. ...
Gridlock is used to describe severe road traffic congestion with zero flow [27, 28] . ...
doi:10.3390/electronics9091412
fatcat:p4do7sldxndgxn3ljvquowstue
Road Traffic Forecast Based on Meteorological Information through Deep Learning Methods
2022
Sensors
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Will the use of a dataset with information on transit flows enhanced with meteorological information allow the construction of a precise traffic flow forecasting model, allowing predictions to be made ...
The present work evaluates different machine learning methods, namely long short-term memory, autoregressive LSTM, and a convolutional neural network, and data attributes to predict traffic flows based ...
Acknowledgments: We are thankful to Joaquim Ferreira for providing us access to the traffic dataset and to Instituto Português do Mar e da Atmosfera for providing us the weather datasets from Universidade ...
doi:10.3390/s22124485
pmid:35746265
pmcid:PMC9227396
fatcat:jwiygfxeo5gvlpsmioyefl4rwq
Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Some representative applications that suffer from these problems are traffic flow estimation, congestion detection, autonomous driving perception, vehicle interaction, and edge computing for practical ...
Complex urban traffic environments have irregular lighting and occlusions, and surveillance cameras can be mounted at a variety of angles, gather dirt, shake in the wind, while the traffic conditions are ...
The multi-step methods first apply traffic flow estimation models to measure traffic variables and then use the traffic flow variables to infer congestion. ...
arXiv:2211.05120v1
fatcat:owqs7k65vfcophl2khmqxoqro4
Improving traffic prediction using congestion propagation patterns in smart cities
2021
Advanced Engineering Informatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
The congestion phenomena propagating on the road network of large cities have a major impact on the development of traffic patterns. ...
In this article, we present a new traffic prediction model, the Congestion-based Traffic Prediction Model (CTPM), which refines previous forecasts based on congestion propagation patterns. ...
For example, we can foresee situations where congestion on a road segment would soon lead to congestion on adjacent road segments. ...
doi:10.1016/j.aei.2021.101343
fatcat:rudokevecvgbrkdnrthjhqhmxm
Scanning the Issue
2022
IEEE transactions on intelligent transportation systems (Print)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
The LSTM-CNN model outperformed the other models in learning time-series features, and the inception-CNN model is superior in reproducing the dynamics of traffic congestion patterns on urban expressways ...
A Novel Coding Architecture for Multi-Line LiDAR Point Clouds Based on Clustering and Convolutional LSTM Network X. Sun, S. Wang, and M. ...
doi:10.1109/tits.2022.3152066
fatcat:szsznss35fc7rf43edw6xsibya
Why Is Internet of Autonomous Vehicles Not As Plug And Play As We Think? Lessons To Be Learnt From Present Internet and Future Directions
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
traffic situation and take dynamic driving decisions accordingly, while preventing accidents. ...
(iv) We discuss in detail how vehicle traffic grooming in the IAV would present as much of a challenge as in the legacy Internet. ...
We foresee that in order to regulate traffic on the IAV, we will need similar closed-loop flow and congestion control mechanisms such as the TCP/IP protocol stack of the internet, which will mainly be ...
doi:10.1109/access.2020.3009336
fatcat:djvcl3endvchhg5m4bwqr3zxme
A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory
2022
Applied Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
In this paper, we analyse the state of the art with regards to usefulness of AI within aviation/ATM domain. ...
Nonetheless, despite this, Artificial Intelligence (AI), which is one of the most researched topics in computer science, has not quite reached end users in ATM domain. ...
Assessment of colli-sion scenarios and of drone operation risks in urban areas Traffic, Traffic 5D Traffic, 5D Traffic Mod-elling Flying [239] Reinforcement Learning for Traffic Flow Manage-ment Decision ...
doi:10.3390/app12031295
fatcat:bo6bkycd7fcz3putjfb4cyw5aa
Survey of Federated Learning Models for Spatial-Temporal Mobility Applications
[article]
2024
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2024; you can also visit the original URL.
The file type is application/pdf.
Other Versions
We describe the metrics and datasets these works have been using and create a baseline of these approaches in comparison to the centralized settings. ...
Federated Learning (FL) can serve as an ideal candidate for training spatial temporal models that rely on heterogeneous and potentially massive numbers of participants while preserving the privacy of highly ...
trajectories, ii) traffic flow prediction approaches, and iii) clustering approaches. ...
arXiv:2305.05257v4
fatcat:skd3ucszdvg37m2vj7m6cnz2mi
A survey on Machine Learning Techniques for Routing Optimization in SDN
2021
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
This article surveys the use of ML techniques for routing optimization in SDN based on three core categories (i.e. supervised learning, unsupervised learning, and reinforcement learning). ...
In summary, future efforts should be focused on reproducible research rather than on new isolated ideas. Otherwise, most of these applications will be barely implemented in practice. ...
The hybrid SDN-enabled supervised ML is formed by an LSTM to perform traffic flow prediction using time-series datasets, which extracts short-term network data traffic variabilities and periodicities to ...
doi:10.1109/access.2021.3099092
fatcat:flp25cn2mbhohjxvuxgfupflny
Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions
[article]
2023
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2023; you can also visit the original URL.
The file type is application/pdf.
To address this need, we have undertaken a comprehensive review that focuses on trajectory prediction methods for AVs, with a particular emphasis on machine learning techniques including deep learning ...
This article also examines the various datasets and evaluation metrics that are commonly used in trajectory prediction tasks. ...
[201] , is the first learning-based planner that uses IRL to control a vehicle in congested urban traffic. ...
arXiv:2307.07527v1
fatcat:kgevf6lqazautmyiwssjx7thna
Application of deep learning algorithms and architectures in the new generation of mobile networks
2021
Serbian Journal of Electrical Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Having firstly presented the background of deep learning and related technologies, the paper goes on to present the architectures used for deployment of deep learning in mobile networks. ...
Finally, the paper presents practical use case of modulation classification as implementation of deep learning in an application essential for modern spectrum management. ...
Traffic load predictions at base stations in ultra-dense networks performed by employing LSTMs are used by authors in [67] to dynamically change the resource allocation policies in order to avoid congestion ...
doi:10.2298/sjee2103397d
fatcat:n3hduljspfbt3mkq2zdzbae72u
Artificial intelligence techniques for enabling Big Data services in distribution networks: A review
2021
Renewable & Sustainable Energy Reviews
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Their interdependencies are mapped, proving that multiple services can be offered as a single clustered service to different stakeholders. ...
Another finding is that unsupervised learning methods are mainly being applied to customer segmentation, buildings efficiency clustering and consumption profile grouping for non-technical losses detection ...
For instance, the day ahead aggregated demand forecasting of a particular zone is essential for the DSO to foresee possible congestion in the network. ...
doi:10.1016/j.rser.2021.111459
fatcat:43mrjxzeijhrpll35ifzyihtde
Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Since DARPA Grand Challenges (rural) in 2004/05 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications. ...
Due to the limited space, we focus the analysis on several key areas, i.e. 2D and 3D object detection in perception, depth estimation from cameras, multiple sensor fusion on the data, feature and task ...
In V2X, deep learning could be used for network congestion control, security in VANETs, vehicular edge computing, content delivery/offloading and vehicle platoons etc. ...
arXiv:2006.06091v3
fatcat:nhdgivmtrzcarp463xzqvnxlwq
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2017 pre-print arXiv:1710.02913v1 fatcat:zv6wulsdjbeird6vh7o4dyhlmu
Then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality and ...
future works that can be addressed using ANNs. ...
Existing works such as [234] , [236] - [238] studied several problems related to the use of ANNs for solving smart cities problems such as data management [234] , urban traffic flow prediction [236 ...
arXiv:1710.02913v2
fatcat:kljn2evlwba4fha4lpwxjpv4yu
Failure Prediction for Autonomous Driving
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
For instance, driving models may fail more likely at places with heavy traffic, at complex intersections, and/or under adverse weather/illumination conditions. ...
It therefore is important that automated cars foresee problems ahead as early as possible. This is also of paramount importance if the driver will be asked to take over. ...
In addition, we mainly observed failures during sharp corners, in congested traffic, and in urban environments when many pedestrians are involved.
C. ...
arXiv:1805.01811v1
fatcat:6c7xit5uu5acvmornvlfcp35eq
« Previous
Showing results 1 — 15 out of 63 results