Topological Graph Convolutional Network-Based Urban Traffic Flow and Density Prediction

Qiu, Han; Zheng, Qinkai; Msahli, Mounira; Memmi, Gérard; Qiu, Meikang; Lu, Jialiang

doi:10.1109/tits.2020.3032882

Cited by 105 publications

(8 citation statements)

References 29 publications

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“…First, by utilizing a graph convolution network to forecast traffic volumes, and then, by giving priority to transmit packets for the V2X communication network. This might also be overcome by using drone cells to reduce traffic congestion in Road Side Units, in agreement to paper [22] . According to article [24] , the spatio-temporal trajectories are utilized to estimate the vehicle density, and the predicted data is then used to determine the optimal path for transmitting data packets to the preferred destination.…”

Section: Related Worksupporting

confidence: 58%

“…Having an ideal communication system also requires proper UAV allocation in the necessary areas and the lowering of message delay. According to article [22] , there is a lot of traffic on the Vehicle to Everything (V2X) network in urban areas. As a result, there is a delay in the communications that the cars transmit and receive.…”

Section: Related Workmentioning

confidence: 99%

“…Reference number Scenario Main method [16] Space Air Ground Integrated Networks (SAGINs) Artificial Intelligence (AI) [17] Low altitude platforms (LAPs) Hybrid satellite-aerial terrestrial (HSAT) networks [19] Unmanned aerial vehicle (UAV) networks Swarm optimization [20] Unmanned aerial vehicle (UAV) networks Monotonic optimization [21] Multicarrier (MC) systems Suboptimal algorithm [22] Intelligent Transportation System (ITS) Topological Graph Convolutional Network (ToGCN) [23] Radio access network (RAN) Proactive drone cell deployment framework [24] Internet of Things (IoTs) Collaborative communication scheme [25] Vehicular ad hoc networks (VANETs) Intelligent prediction based routing algorithms [26] unmanned aerial vehicle (UAV) networks Energy efficiency (EE) maximization scheme [27] UAV enabled wireless communications Efficient iterative algorithm [28] Unmanned aerial vehicle (UAV) networks Block coordinate descent method per [10] , and [11] . Each location data point that falls within the chosen area 𝑅 has the vehicle ID 𝑉 𝑖𝑑 labeled on it.…”

Section: Table 1 Comparison Among the Related Workmentioning

confidence: 99%

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UAV assisted communication for ground users using machine learning and optimization

Prakash¹,

Zang²,

Scott³

et al. 2022

J Smart Environ Green Comput

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Aim: Among a large number of products that support communication, there is one called space air ground integrated networks (SAGIN's), which is the most commonly used to support users in rural and emergency situations. Typicaly in emergency situations SAGIN's use unmanned aerial vehicles (UAVs) in their air layer to temporarily support the ground users. Although the cost of UAVs is lower than that of traditional base stations, and their actions are more flexible, but their battery life problems lead to frequent charging of drones, resulting in many resource losses and unable to provide communication support. In order to mitigate the issues, novel optimization algorithms need to be developed to support the ground users. Methods: In this work, we develop a grid-based deep learning method using the LSTM model to estimate the number of ground users as vehicles in each area, and developed an optimization algorithm to minimize the number of UAVs needed to the user's and meanwhile to satisfy quality of service (QoS) requirements. For optimization, we mainly use the Linear Optimization Tools and an objective function has been developed using density predicted and SINR data to achieve acceptable QoS. Results: The simulation results shows that this approach has improved the quality of the communication by 50%. Conclusion: Using unique grid technique and the LSTM machine learning model, the user densities on each partitioned grid is determined. Finally, a linear optimization algorithm is developed based on the user density to determine the lowest number of UAVs to support the users in each grid while maintaining the QoS.

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Section: Related Worksupporting

confidence: 58%

Section: Related Workmentioning

confidence: 99%

Section: Table 1 Comparison Among the Related Workmentioning

confidence: 99%

See 1 more Smart Citation

UAV assisted communication for ground users using machine learning and optimization

Prakash¹,

Zang²,

Scott³

et al. 2022

J Smart Environ Green Comput

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show abstract

“…There are many practical application scenarios that can be studied in the field of traffic prediction, such as the travel time [ 1 , 2 , 3 ], speed [ 4 , 5 , 6 , 7 , 8 ], congestion [ 9 , 10 ], and traffic flow, which can be divided into taxi flow [ 11 , 12 , 13 , 14 ], bicycle flow [ 15 , 16 ], and highway flow [ 17 , 18 , 19 , 20 , 21 ], which are all studied in this paper.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Correlation Adjacency-Matrix-Based Graph Neural Networks for Traffic Flow Prediction

Jia

Cai

et al. 2023

Sensors

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Modeling complex spatial and temporal dependencies in multivariate time series data is crucial for traffic forecasting. Graph convolutional networks have proved to be effective in predicting multivariate time series. Although a predefined graph structure can help the model converge to good results quickly, it also limits the further improvement of the model due to its stationary state. In addition, current methods may not converge on some datasets due to the graph structure of these datasets being difficult to learn. Motivated by this, we propose a novel model named Dynamic Correlation Graph Convolutional Network (DCGCN) in this paper. The model can construct adjacency matrices from input data using a correlation coefficient; thus, dynamic correlation graph convolution is used for capturing spatial dependencies. Meanwhile, gated temporal convolution is used for modeling temporal dependencies. Finally, we performed extensive experiments to evaluate the performance of our proposed method against ten existing well-recognized baseline methods using two original and four public datasets.

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“…The authors in [4] introduce a choreography-based service composition platform to accelerate the reuse-based development for a choreographybased urban coordinative application. The authors in [5] proposed a novel topological graph convolutional network to predict the urban traffic flow and traffic density. Q. Chen, Z.…”

Section: Introductionmentioning

confidence: 99%

Semi-asynchronous Hierarchical Federated Learning for Cooperative Intelligent Transportation Systems

Chen¹,

You²,

Jiang³

2021

Preprint

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Cooperative Intelligent Transport System (C-ITS) is a promising network to provide safety, efficiency, sustainability, and comfortable services for automated vehicles and road infrastructures by taking advantages from participants. However, the components of C-ITS usually generate large amounts of data, which makes it difficult to explore data science. Currently, federated learning has been proposed as an appealing approach to allow users to cooperatively reap the benefits from trained participants. Therefore, in this paper, we propose a novel Semi-asynchronous Hierarchical Federated Learning (SHFL) framework for C-ITS that enables elastic edge to cloud model aggregation from data sensing. We further formulate a joint edge node association and resource allocation problem under the proposed SHFL framework to prevent personalities of heterogeneous road vehicles and achieve communication-efficiency. To deal with our proposed Mixed integer nonlinear programming (MINLP) problem, we introduce a distributed Alternating Direction Method of Multipliers (ADMM)-Block Coordinate Update (BCU) algorithm. With this algorithm, a tradeoff between training accuracy and transmission latency has been derived. Numerical results demonstrate the advantages of the proposed algorithm in terms of training overhead and model performance.

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Topological Graph Convolutional Network-Based Urban Traffic Flow and Density Prediction

Cited by 105 publications

References 29 publications

UAV assisted communication for ground users using machine learning and optimization

UAV assisted communication for ground users using machine learning and optimization

Dynamic Correlation Adjacency-Matrix-Based Graph Neural Networks for Traffic Flow Prediction

Semi-asynchronous Hierarchical Federated Learning for Cooperative Intelligent Transportation Systems

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