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Long Short-Term Memory Networks for Traffic Flow Forecasting: Exploring Input Variables, Time Frames and Multi-Step Approaches
2020
Informatica
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frames used by the model and the employed approach for multi-step forecasting. ...
Recent times came, however, with promising results regarding the use of Recurrent Neural Networks (RNNs), such as Long Short-Term Memory networks (LSTMs), to accurately address time series problems. ...
Long Short-Term Memory networks (LSTMs), a specific type of RNNs, are among those that have been showed to produce valid results on time series data Ma et al., 2015) . ...
doi:10.15388/20-infor431
fatcat:ineg5l5pi5gd7cogcoqfqxzqna
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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Timely accurate traffic forecast is crucial for urban traffic control and guidance. ...
Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal dependencies ...
By combining long short-term memory (LSTM) network [Hochreiter and Schmidhuber, 1997 ] and 1-D CNN, Wu and Tan [2016] presented a feature-level fused architecture CLTFP for shortterm traffic forecast ...
doi:10.24963/ijcai.2018/505
dblp:conf/ijcai/YuYZ18
fatcat:du2tmnse6bawxersbwhvi63am4
A Comparison of ARIMAX, VAR and LSTM on Multivariate Short-Term Traffic Volume Forecasting
2021
Zenodo
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This paper proposes a comparison of statistical learning models, Vector Auto Regression, ARIMAX and a deep learning model, LSTM neural network, in the context of multivariate short-term (24 hours) time ...
VAR model produced the best performance with an accuracy of 91.459% and proved to be used successfully in short term traffic forecasting in ITS applications. ...
ACKNOWLEDGEMENT This research was supported by the Accelerating Higher Education Expansion and Development (AHEAD) Operation of the Ministry of Higher Education, Sri Lanka funded by the World Bank. ...
doi:10.5281/zenodo.4514955
fatcat:lbkucqzgzvau3cdl2xyag5none
Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion
[article]
2022
arXiv
pre-print
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Considering the lack of suitable existing datasets for it, we release TRANS, a benchmark for explicitly studying the stop and go behaviors of pedestrians in urban traffic. ...
We evaluate our model and several baselines on TRANS, and set a new benchmark for the community to work on pedestrian stop and go forecasting. ...
Long Short-Term Memory (LSTM) networks, as variations of RNNs, address the problem of vanishing gradients and long-term dependency in modeling long sequences [49] . ...
arXiv:2203.02489v1
fatcat:auat4itsmfg47oqg6lvdfeu27y
Deep learning for spatio-temporal forecasting – application to solar energy
[article]
2022
arXiv
pre-print
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The motivating application at Electricity de France (EDF) is short-term solar energy forecasting with fisheye images. ...
Our second direction is to augment incomplete physical models with deep data-driven networks for accurate forecasting. ...
Related work We review here related multi-step video prediction approaches dedicated to long-term forecasting. ...
arXiv:2205.03571v1
fatcat:dwkprkwf6ncgjcnvkpx3yrdfjm
Combining Deep Learning and Multiresolution Analysis for Stock Market Forecasting
2021
IEEE Access
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INDEX TERMS Deep learning, multiresolution analysis, long-short term memory, financial time series, forecasting. ...
In this article, we propose a model based on deep neural networks that improves the forecasting of stock prices. ...
ACKNOWLEDGMENT The authors would like to thank King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia, for support during this work. ...
doi:10.1109/access.2021.3051872
fatcat:vdlnsq66bzfxthrnsprds6bwgq
Sound Levels Forecasting in an Acoustic Sensor Network Using a Deep Neural Network
2020
Sensors
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., vehicles flow or street geometry, and predict equivalent levels for a temporal long-term. ...
Therefore, forecasting of temporal short-term sound levels could be a helpful tool for urban planners and managers. ...
These problems are solved by the Long Short-Term Memory neural networks (LSTM), for which it incorporates a series of steps to decide which information will be stored and which erased. ...
doi:10.3390/s20030903
pmid:32046231
pmcid:PMC7038967
fatcat:t55wzounrjdqzdfiltiy2mkt4y
ADST: Forecasting Metro Flow using Attention-Based Deep Spatial-Temporal Networks with Multi-Task Learning
2020
Sensors
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In this paper, we identify the unique spatiotemporal correlation of urban metro flow, and propose an attention-based deep spatiotemporal network with multi-task learning (ADST-Net) at a citywide level ...
Furthermore, we enforce multi-task learning to utilize transition passenger flow volume prediction as an auxiliary task during the training process for generalization. ...
Acknowledgments: Our sincere thankfulness be tendered to all the reviewers for their valuable comments and helpful suggestions.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s20164574
pmid:32824074
pmcid:PMC7472615
fatcat:ynrvnnerabby5hpnqrcqesv4qa
Multistep traffic forecasting by dynamic graph convolution: Interpretations of real-time spatial correlations
2021
Transportation Research Part C: Emerging Technologies
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A B S T R A C T Accurate and explainable short-term traffic forecasting is pivotal for making trustworthy decisions in advanced traffic control and guidance systems. ...
We believe that this research paves a path to more transparent deep learning models applied for short-term traffic forecasting. ...
We thank them for supporting this study. ...
doi:10.1016/j.trc.2021.103185
fatcat:nfn2igym7bao3nl6nro4t64upq
Spatial-Temporal Graph Attention Networks: A Deep Learning Approach for Traffic Forecasting
2019
IEEE Access
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Compared with previous related research, the proposed approach is able to capture dynamic spatial dependencies of traffic networks. ...
For more information, see ...
[19] have shown a good result by combining CNN with Long Short-term Memory (LSTM) network for TSP. ...
doi:10.1109/access.2019.2953888
fatcat:h3mjwe3765bophjanjexw6gs24
SA-JSTN: Self-Attention Joint Spatiotemporal Network for Temperature Forecasting
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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performance especially in short-term prediction. ...
propose a new deep learning model for temperature forecasting, Self-Attention Joint Spatiotemporal Network (SA-JSTN), which simultaneously captures the spatiotemporal interdependency information. ...
ACKNOWLEDGMENTS The authors would like to thank Rafaela Castro of the Federal Center for Technological Education of Rio de Janeiro, Brazil, and the Hebei Meteorological Bureau of China, for providing the ...
doi:10.1109/jstars.2021.3112131
fatcat:qfjhuxv5ibffdk7p35c57b6eky
DESIGN AND RESULTS OF AN AI-BASED FORECASTING OF AIR POLLUTANTS FOR SMART CITIES
2021
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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To forecast these air pollutants for the next 48 hours, a sequence-to-sequence encoder-decoder model with a recurrent neural network (RNN) was implemented. ...
of air pollutants in urban environments by including real time weather forecast data. ...
ACKNOWLEDGEMENTS The research is supported by the German Federal Ministry of Transport and Digital Infrastructure (BMVI) under the frame of mFUND, a research initiative funding R & D projects related to ...
doi:10.5194/isprs-annals-viii-4-w1-2021-89-2021
doaj:f0c949b1e85841d2b817a4608f26cbef
fatcat:2p5f5uw2arepfgzzclnw3u55ge
Deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks
2021
Energy Science & Engineering
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The results demonstrate that the proposed model can deal with complex nonlinear gas network flow forecasting with high accuracy and effectiveness. ...
Since the average velocity of gas in a pipeline is only 25 km/h, an adequate high-precision, high-frequency forecasting of supply and demand is crucial for efficient control and operation of such a transmission ...
Bogaerts at al. 37 combined graph convolutions and Long Short Term Memory network (LSTM) for making short-term and long-term prediction of traffic flows while similar method was proposed by Wu et al ...
doi:10.1002/ese3.932
fatcat:xrxkgump5nhm3ds6iy7mme6z74
Long Short Term Memory Networks for Bandwidth Forecasting in Mobile Broadband Networks under Mobility
[article]
2020
arXiv
pre-print
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In this work, we introduce HINDSIGHT++, an open-source R-based framework for bandwidth forecasting experimentation in MBB networks with Long Short Term Memory (LSTM) networks. ...
We primarily focus on bandwidth forecasting for Fifth Generation (5G) networks. ...
ACKNOWLEDGMENTS This work is partially funded by the EU H2020 5GENESIS (815178), and by the Norwegian Research Council project MEMBRANE (250679). ...
arXiv:2011.10563v1
fatcat:3h3teaszyzc3lcokupyz3pptya
Multi-Horizon Air Pollution Forecasting with Deep Neural Networks
2021
Sensors
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In this work, we propose using Recurrent Neural Network (RNN) models with long short-term memory units to predict the level of PM10 particles at 6, 12, and 24 h in the future. ...
The obtained results show that the proposed models consistently outperform the baseline model and can be successfully employed for air pollution prediction. ...
Cyril and Methodius University in Skopje, Faculty of Computer Science and Engineering. We also acknowledge the support of NVIDIA through the donation of a Titan V GPU. ...
doi:10.3390/s21041235
pmid:33578633
fatcat:zwi5gxfkmjbyrdzxcsh2veyzlu
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