SATP-GAN: self-attention based generative adversarial network for traffic flow prediction

Zhang, Liang; Wu, Jianqing; Chen, Ming; Wang, Rui; Zhou, Xinliang; Xu, Changwu; Yao, Quankai; Wu, Qiang

doi:10.1080/21680566.2021.1916646

Cited by 30 publications

(8 citation statements)

References 36 publications

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“…One-hundred forty-four fully fledged research articles were finally selected based on the following inclusion criteria: most relevant, most cited, and most recent. For traffic state forecasting, in terms of performances, the GAN-based methods and also hybrid approaches showed better performance on state-of-the-art datasets, i.e., PeMS (Li et al [154], Zhang et al [95]). For intersection signal control, DRL-and DQN-based approaches showed much better efficiency and robustness (Wang et al [155], Bouktif et al [9]) relative to other baselines.…”

Section: Discussionmentioning

confidence: 99%

“…To further increase the accuracy, Zhang et al [94] proposed TrafficGAN employing both the CNN and LSTM models, which achieved an MAE of 1.76 during weekdays for a 30 min prediction horizon. In the work of Liang Zhang et al [95], a Self-Attention Generative Adversarial Network (SATP-GAN) was proposed that used Reinforcement Learning (RL), showing an improvement of 6.5% over baseline methods. Different approaches of integrating rules as inductive biases into deep-learning-based prediction models were evaluated by Li et al [96], confirming the usefulness of GANs in achieving better performance.…”

Section: Fuzzy Neural Networkmentioning

confidence: 99%

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Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System

Reza

Oliveira

Machado

et al. 2021

Sensors

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With the rapid growth and development of cities, Intelligent Traffic Management and Control (ITMC) is becoming a fundamental component to address the challenges of modern urban traffic management, where a wide range of daily problems need to be addressed in a prompt and expedited manner. Issues such as unpredictable traffic dynamics, resource constraints, and abnormal events pose difficulties to city managers. ITMC aims to increase the efficiency of traffic management by minimizing the odds of traffic problems, by providing real-time traffic state forecasts to better schedule the intersection signal controls. Reliable implementations of ITMC improve the safety of inhabitants and the quality of life, leading to economic growth. In recent years, researchers have proposed different solutions to address specific problems concerning traffic management, ranging from image-processing and deep-learning techniques to forecasting the traffic state and deriving policies to control intersection signals. This review article studies the primary public datasets helpful in developing models to address the identified problems, complemented with a deep analysis of the works related to traffic state forecast and intersection-signal-control models. Our analysis found that deep-learning-based approaches for short-term traffic state forecast and multi-intersection signal control showed reasonable results, but lacked robustness for unusual scenarios, particularly during oversaturated situations, which can be resolved by explicitly addressing these cases, potentially leading to significant improvements of the systems overall. However, there is arguably a long path until these models can be used safely and effectively in real-world scenarios.

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Section: Discussionmentioning

confidence: 99%

Section: Fuzzy Neural Networkmentioning

confidence: 99%

Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System

Reza

Oliveira

Machado

et al. 2021

Sensors

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show abstract

“…Considering the interactive spatio-temporal relationships, Spatial-Temporal Synchronous Graph Convolutional Networks (STSGCN) [21] proposed the extraction of the time and space connections simultaneously, and Spatial-Temporal Fusion Graph Neural Networks (STFGNN) [22] integrated spatial and temporal modules to achieve concurrent capture of spatio-temporal relationships. Models based on generative adversarial networks (GAN) have also been applied to traffic flow prediction [23,24]. For example, PL-WGAN [25] combines GCN together with RNN to capture spatio-temporal relationships and introduces additional information through GAN for traffic flow prediction, also achieving adequate results.…”

Section: Introductionmentioning

confidence: 99%

Global spatio‐temporal dynamic capturing network‐based traffic flow prediction

Sun

Wei

Huang

et al. 2023

IET Intelligent Trans Sys

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Capturing the complex spatio‐temporal relationships of traffic roads is essential to accurately predict traffic flow data. Traditional models typically collect spatial and temporal relationships and increase the complexity of the model by considering connected and unconnected roads. However, global road networks are dynamic and hidden connectivity relationships generally undergo variations over time. A deterministic single‐connection correlation inevitably limits the learning capability of the model. In this paper, the authors propose a global spatio‐temporal dynamic capturing network (GSTDCN) for traffic flow prediction. First, the global encoding module based on the attention mechanism is set up to describe the dynamic spatio‐temporal relationships. It is shown that GSTDCN can learn the hidden node information by spatial correlation at different times. Meanwhile, an effective temporal prediction module is constructed, which facilitates the data augmentation and improves the prediction results of GSTDCN. The model is experimented on four public transportation datasets, and the results show that the GSTDCN outperforms the state‐of‐the‐art baseline.

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“…The key contributions of this manuscript are abridged below. SabGAN 38 technique with AqOA 2 espoused energy aware cluster head selection in WSN. SabGAN has engaged by the effective fitness function for picking CH with in wireless sensor network. The CH used for each cluster has certain objectives: sensor nodes delay, detachment from sensor nodes to cluster head, energy, cluster density and traffic rate . The ideal path is selected depending on three parameters: trust, connectivity, and degree of amenity (DoA).…”

Section: Introductionmentioning

confidence: 99%

Self‐attention based generative adversarial network with Aquila optimization algorithm espoused energy aware cluster head selection in WSN

Soundararajan,

Tapas Bapu,

Sargunavathi

et al. 2023

Int J Communication

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SummaryWSNs have a wide range of applications, and the effective Wireless Sensor Network (WSN) design includes the best energy optimization techniques. The nodes in wireless sensor networks run on batteries. The existing cluster head selection methods do not take into account the latency and rate of wireless network traffic when optimizing the node's energy constraints. To overcome these issues, a self‐attention based generative adversarial network (SabGAN) with Aquila Optimization Algorithm (AqOA) is proposed for Multi‐Objective Cluster Head Selection and Energy Aware Routing (SabGAN‐AqOA‐EgAwR‐WSN) for secured data transmission in wireless sensor network. The proposed method implements the routing process through cluster head. SabGAN classifiers are utilized to select the CH based on firm fitness functions, including delay, detachment, energy, cluster density, and traffic rate. After the selection of the cluster head, the malicious node gains access to the cluster. Therefore, the ideal path selection is carried out by three parameters: trust, connectivity, and degree of amenity. These three parameters are optimized under proposed AqOA. The data are transferred to the base station with the support of optimum trust path. The proposed SabGAN‐AqOA‐EgAwR‐WSN method is activated in NS2 simulator. Finally, the proposed SabGAN‐AqOA‐EgAwR‐WSN method attains 12.5%, 32.5%, 59.5%, and 32.65% higher alive nodes; 85.71%, 81.25%, 82.63%, and 71.96% lower delay; and 52.25%, 61.65%, 37.83%, and 20.63% higher normalized network energy compared with the existing methods.

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SATP-GAN: self-attention based generative adversarial network for traffic flow prediction

Cited by 30 publications

References 36 publications

Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System

Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System

Global spatio‐temporal dynamic capturing network‐based traffic flow prediction

Self‐attention based generative adversarial network with Aquila optimization algorithm espoused energy aware cluster head selection in WSN

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