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Survey on Research of RNN-Based Spatio-Temporal Sequence Prediction Algorithms

Wei Fang, Yupeng Chen, Qiongying Xue
2021 Journal on Big Data  
, and includes precipitation nowcasting algorithms and traffic flow forecasting algorithms.  ...  In the past few years, deep learning has developed rapidly, and many researchers try to combine their subjects with deep learning.  ...  This new deep learning architecture for short-term traffic flow prediction first combines the ideas of convolution and LSTM to generate a ConvLSTM module to extract the temporal and spatial characteristics  ... 
doi:10.32604/jbd.2021.016993 fatcat:tu5ctgr5p5em7afjc66wqwq3ya

Multi-Lane Short-Term Traffic Forecasting with Convolutional LSTM Network

Yixuan Ma, Zhenji Zhang, Alexander Ihler
2020 IEEE Access  
ACKNOWLEDGMENT The authors would like to make a grateful acknowledgement to the Caltrans Performance Measurement System for providing valuable daily traffic flow data, and the anonymous reviewers whose  ...  A deep belief network (DBN) was used to capture the spatial-temporal features of traffic flow and a multi-task learning architecture to perform exit station flow and road flow forecasting [37] .  ...  To fill the gap, we propose a novel convolutional LSTM recurrent neural network architecture for multi-lane short-term traffic flow prediction.  ... 
doi:10.1109/access.2020.2974575 fatcat:luztkgh4hve7taq4qrbyiehnma

Video Contents Understanding using Deep Neural Networks [article]

Mohammadhossein Toutiaee, Abbas Keshavarzi, Abolfazl Farahani, John A. Miller
2020 arXiv   pre-print
This representation is achieved with the advent of "deep neural network" (DNN), which is being studied these days by many researchers.  ...  those architectures would perform in foggy or rainy weather conditions.  ...  VGG19 Very deep convolutional networks, also known as VGG, for large-scale image classification task is employed for labels prediction in traffic flow [6] .  ... 
arXiv:2004.13959v1 fatcat:iccsimscmjgxjbm7u5ihozkaqq

High-resolution rainfall-runoff modeling using graph neural network [article]

Zhongrun Xiang, Ibrahim Demir
2021 arXiv   pre-print
In this paper, we propose the GNRRM (Graph Neural Rainfall-Runoff Model), a novel deep learning model that makes full use of spatial information from high-resolution precipitation data, including flow  ...  Several studies investigated the use of GNN (Graph Neural Networks) for data integration by decomposing a large watershed into multiple sub-watersheds, but each sub-watershed is still treated as a whole  ...  Conclusion This study proposed GNRRM, a new type of network architecture for rainfall-runoff modeling based on the idea of graph neural networks, to use the geospatial information from the precipitation  ... 
arXiv:2110.10833v1 fatcat:ztial2jpkrecjebt6zgyuldznu

Deep Learning for Spatio-Temporal Data Mining: A Survey [article]

Senzhang Wang, Jiannong Cao, Philip S. Yu
2019 arXiv   pre-print
Then a framework is introduced to show a general pipeline of the utilization of deep learning models for STDM.  ...  In this paper, we provide a comprehensive survey on recent progress in applying deep learning techniques for STDM.  ...  It simultaneously predicts traffic flow for all road segments based on the information gathered from the whole graph.  ... 
arXiv:1906.04928v2 fatcat:4zrdtgkvirfuniq3rb2gl7ohpy

Context-Aware Link Embedding with Reachability and Flow Centrality Analysis for Accurate Speed Prediction for Large-Scale Traffic Networks

Chanjae Lee, Young Yoon
2020 Electronics  
This paper presents a novel method for predicting the traffic speed of the links on large-scale traffic networks.  ...  We combine the features regarding the traffic flow centrality with the external conditions around the links, such as climate and time of day information.  ...  Various approaches have been used for traffic speed prediction using statistical methods [1] [2] [3] [4] [5] [6] [7] and machine learning with neural networks with deep hidden layers [8] [9] [10] [11  ... 
doi:10.3390/electronics9111800 fatcat:s2voedbe35dzpdvab75mge7aka

A Deep Learning–Based Approach for Moving Vehicle Counting and Short-Term Traffic Prediction From Video Images

Ye Zheng, Xiaoming Li, LiuChang Xu, Nu Wen
2022 Frontiers in Environmental Science  
traffic flow forecast with weather conditions.  ...  Subsequently, combined with the temporal characteristics of historical traffic flow, this article introduces weather conditions into the LSTM network and realizes the short-term prediction of traffic flow  ...  Little et al. (1981) proposed an end-toend deep learning architecture that consists of convolution and LSTM to form a Conv-LSTM module to extract the spatial-temporal information from the traffic flow  ... 
doi:10.3389/fenvs.2022.905443 fatcat:nlecsyv64felfmoc2yvic4douu

Convolutional LSTM Architecture for Precipitation Nowcasting Using Satellite Data

Carlos Javier Gamboa-Villafruela, José Carlos Fernández-Alvarez, Maykel Márquez-Mijares, Albenis Pérez-Alarcón, Alfo José Batista-Leyva
2021 Environmental Sciences Proceedings  
In this research, we propose an LSTM convolutional neural network (CNN-LSTM) architecture for immediate prediction of various short-term precipitation events using satellite data.  ...  The trained neural network model is used to predict the sixteenth precipitation data of the corresponding fifteen precipitation sequence and up to a time interval of 180 min.  ...  We found that the LSTM model with three layers obtained the best results and predicts precipitation with good accuracy even for a lead time of 180 min.  ... 
doi:10.3390/ecas2021-10340 fatcat:itvmqdx2ibdy3av6xo6ybhmf6i

Long Short-Term Memory Networks for Traffic Flow Forecasting: Exploring Input Variables, Time Frames and Multi-Step Approaches

Bruno Fernandes, Fabio Silva, Hector Alaiz-Moreton, Paulo Novais, Jose Neves, Cesar Analide
2020 Informatica  
flow for several future timesteps.  ...  This study aims to conceive and find the best possible LSTM model for traffic flow forecasting while addressing several important aspects of such models such as the multitude of input features, the time  ...  Conclusions Traffic flow forecasting has been assuming a prominent position with the rise of deep learning.  ... 
doi:10.15388/20-infor431 fatcat:ineg5l5pi5gd7cogcoqfqxzqna

Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method [article]

Jinlei Zhang, Hongshu Che, Feng Chen, Wei Ma, Zhengbing He
2021 arXiv   pre-print
Different from other short-term traffic forecasting methods, the short-term OD flow prediction possesses three unique characteristics: (1) data availability: real-time OD flow is not available during the  ...  Short-term origin-destination (OD) flow prediction in urban rail transit (URT) plays a crucial role in smart and real-time URT operation and management.  ...  SHI, X., GAO, Z., LAUSEN, L., WANG, H., YEUNG, D., WONG, W. & WOO, W. (2017), "Deep learning for precipitation nowcasting: A benchmark and a new model", in Advances in neural information processing  ... 
arXiv:2008.08036v2 fatcat:udyuttbgkncujo6yp6jmticzn4

Deep-PRESIMM: Integrating Deep Learning with Microsimulation for Traffic Prediction

Aniekan E. Essien, Ilias Petrounias, Pedro Sampaio, Sandra Sampaio
2019 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)  
This paper presents a proactive model and tool for traffic analysis and management that integrates deep learning for traffic parameter prediction with microscopic traffic simulation providing traffic analysts  ...  in order to forecast traffic flow and speed, which is subsequently passed on to a traffic microsimulation tool Simulation of Urban Mobility (SUMO) where the predicted parameters are used to generate a  ...  Using a deep CNN-LSTM stacked autoencoder architecture applied on historical sensor-collected traffic and non-traffic datasets, multivariate (speed and volume) traffic prediction was made on a 1-hour prediction  ... 
doi:10.1109/smc.2019.8914604 dblp:conf/smc/EssienPSS19 fatcat:pynfmnfp5ba3bkfycxwqtenwk4

Improving Urban Traffic Speed Prediction Using Data Source Fusion and Deep Learning

Aniekan Essien, Ilias Petrounias, Pedro Sampaio, Sandra Sampaio
2019 2019 IEEE International Conference on Big Data and Smart Computing (BigComp)  
Motivated by deep learning prediction methods, we propose a Long Short-Term Memory Neural Network (LSTM-NN) for traffic speed prediction that combines traffic and weather datasets on an urban road network  ...  Traffic parameter forecasting is critical to effective traffic management but is a challenging task due to the stochasticity of traffic flow characteristics, especially in urban road networks.  ...  Also, [39] investigated the impact of fusing weather data with traffic data for predicting traffic flow.  ... 
doi:10.1109/bigcomp.2019.8679231 dblp:conf/bigcomp/EssienPSS19 fatcat:utnc26mqmbar3otpjlf2rijmv4

TaxiInt: Predicting the Taxi Flow at Urban Traffic Hotspots Using Graph Convolutional Networks and the Trajectory Data

Jinmao Zhang, Huanchang Chen, Yiming Fang, Jit S. Mandeep
2021 Journal of Electrical and Computer Engineering  
The experiments carried on a real taxi dataset showed the validity of our model. It can predict the taxi flow at a given urban intersection with high accuracy.  ...  The accurate prediction of taxi flow provides an attractive way to find the potential traffic hotspots in the city, which helps to avoid serious traffic congestions by taking effective measures in advance  ...  A deep learning approach was proposed to extract the complex features of traffic flow and then predict the short-term traffic flow forecast with high accuracy and stability [19] .  ... 
doi:10.1155/2021/9956406 fatcat:5wddprbx4valnfkymvf4xm3rc4

Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction [article]

Lingbo Liu, Jiajie Zhen, Guanbin Li, Geng Zhan, Zhaocheng He, Bowen Du, Liang Lin
2020 arXiv   pre-print
As a crucial component in intelligent transportation systems, traffic flow prediction has recently attracted widespread research interest in the field of artificial intelligence (AI) with the increasing  ...  Extensive experiments on two standard benchmarks well demonstrate the superiority of the proposed method for traffic flow prediction.  ...  Based on the proposed ATFM, we further develop a deep architecture for forecasting the citywide short-term traffic flow.  ... 
arXiv:1909.02902v4 fatcat:w3fm72776nh7vg7toydupefdk4

Improving Parking Availability Information Using Deep Learning Techniques

Jamie Arjona, MªPaz Linares, Josep Casanovas-Garcia, Juan José Vázquez
2020 Transportation Research Procedia  
Similar studies and proposed solutions for parking prediction are described in terms of the technology and current state-of-the-art predictive models.  ...  This work focuses on studying the data generated by parking systems in order to develop predictive models that generate forecasted information.  ...  Another approach with a Deep NN is presented in Polson and Sokolov (2017) , where the model is used for traffic flow forecasting.  ... 
doi:10.1016/j.trpro.2020.03.113 fatcat:rjzwsbuihjahlg6ofsmwkb3tva
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