Results 21 to 30 of about 1,128,066 (300)
Research on Satellite Network Traffic Prediction Based on Improved GRU Neural Network
Sensors, 2022 The current satellite network traffic forecasting methods cannot fully exploit the long correlation between satellite traffic sequences, which leads to large network traffic forecasting errors and low forecasting accuracy.
Zhiguo Liu +3 more
doaj +1 more source
Graph Neural Network for Traffic Forecasting: The Research Progress
ISPRS International Journal of Geo-Information, 2023 Traffic forecasting has been regarded as the basis for many intelligent transportation system (ITS) applications, including but not limited to trip planning, road traffic control, and vehicle routing. Various forecasting methods have been proposed in the
Weiwei Jiang +3 more
doaj +1 more source
Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting
Algorithms, 2020 Transfer learning is a modern concept that focuses on the application of ideas, models, and algorithms, developed in one applied area, for solving a similar problem in another area.
Dmitry Pavlyuk
doaj +1 more source
Advances in forecasting with artificial neural networks [PDF]
, 2010 There is decades long research interest in artificial neural networks (ANNs) that has led to several successful applications. In forecasting, both in theoretical and empirical works, ANNs have shown evidence of good performance, in many cases ...
Crone, S, Kourentzes, N
core +4 more sources
A Two-Stream Graph Convolutional Neural Network for Dynamic Traffic Flow Forecasting
, 2020 Forecasting the traffic flow is a critical issue for researchers and practitioners in the field of transportation. Using the graph convolutional network (GCN) is widespread in traffic flow forecasting. Existing GCN-based methods mostly rely on undirected
Zhaoyang Li +7 more
core +1 more source
Traffic Volatility Forecasting Using an Omnibus Family GARCH Modeling Framework
Entropy, 2022 Traffic volatility modeling has been highly valued in recent years because of its advantages in describing the uncertainty of traffic flow during the short-term forecasting process.
Jishun Ou +4 more
doaj +1 more source
Kaggle Wikipedia Web Traffic Daily Dataset (with Missing Values)
, 2020 This dataset was used in the Kaggle Wikipedia Web Traffic forecasting competition. It contains 145063 daily time series representing the number of hits or web traffic for a set of Wikipedia pages from 2015-07-01 to 2017-09 ...
Webb, Geoff +2 more
core +1 more source
Graph Neural Networks and Open-Government Data to Forecast Traffic Flow
Information, 2023 Traffic forecasting has been an important area of research for several decades, with significant implications for urban traffic planning, management, and control.
Petros Brimos +3 more
doaj +1 more source
LLM Multimodal Traffic Accident Forecasting
Sensors, 2023 With the rise in traffic congestion in urban centers, predicting accidents has become paramount for city planning and public safety. This work comprehensively studied the efficacy of modern deep learning (DL) methods in forecasting traffic accidents and enhancing Level-4 and Level-5 (L-4 and L-5) driving assistants with actionable visual and language ...
Irene de Zarzà +3 more
openaire +6 more sources
Traffic Allocation Mode of PPP Highway Project: A Risk Management Approach
Advances in Civil Engineering, 2018 Highway projects are the favorites of public-private partnership (PPP) investors because of their stable cash flow. However, there are high uncertainties in terms of traffic volume, resulting in unpredictable revenues, which has drawn major concern of ...
Jie Li +3 more
doaj +1 more source