Short-Term Traffic Speed Forecasting Model for a Parallel Multi-Lane Arterial Road Using GPS-Monitored Data Based on Deep Learning Approach

Tran, Quang Hoc; Fang, Yao-Min; Chou, Tien-Yin; Hoang, Thanh-Van; Wang, Chun-Tse; Vu, Van Truong; Ho, Thi Lan Huong; Le, Quang Van; Chen, Mei-Hsin

doi:10.3390/su14106351

Cited by 14 publications

(11 citation statements)

References 56 publications

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“…Compared to traditional traffic flow [ 1 , 3 , 4 , 5 ] prediction and traffic speed prediction [ 6 , 7 , 8 , 9 ], urban traffic congestion prediction mainly focuses on congestion levels of road networks in cities. However, forecasting congestion levels of road networks is very challenging due to the following two complex factors: Spatio-temporal correlation.…”

Section: Introductionmentioning

confidence: 99%

AST3DRNet: Attention-Based Spatio-Temporal 3D Residual Neural Networks for Traffic Congestion Prediction

Li,

Dai,

Huang

et al. 2024

Sensors

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Traffic congestion prediction has become an indispensable component of an intelligent transport system. However, one limitation of the existing methods is that they treat the effects of spatio-temporal correlations on traffic prediction as invariable during modeling spatio-temporal features, which results in inadequate modeling. In this paper, we propose an attention-based spatio-temporal 3D residual neural network, named AST3DRNet, to directly forecast the congestion levels of road networks in a city. AST3DRNet combines a 3D residual network and a self-attention mechanism together to efficiently model the spatial and temporal information of traffic congestion data. Specifically, by stacking 3D residual units and 3D convolution, we proposed a 3D convolution module that can simultaneously capture various spatio-temporal correlations. Furthermore, a novel spatio-temporal attention module is proposed to explicitly model the different contributions of spatio-temporal correlations in both spatial and temporal dimensions through the self-attention mechanism. Extensive experiments are conducted on a real-world traffic congestion dataset in Kunming, and the results demonstrate that AST3DRNet outperforms the baselines in short-term (5/10/15 min) traffic congestion predictions with an average accuracy improvement of 59.05%, 64.69%, and 48.22%, respectively.

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Section: Introductionmentioning

confidence: 99%

AST3DRNet: Attention-Based Spatio-Temporal 3D Residual Neural Networks for Traffic Congestion Prediction

Li,

Dai,

Huang

et al. 2024

Sensors

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show abstract

“…Although there are many city government efforts to determine solutions to limit congestion, few synchronous methods meet the current reality. The optimal solution is to optimize the current traffic signal timings of traffic networks and provide a predicted road map for commuters [6][7][8]. Therefore, Traffic Speed Forecasting (TSF) plays a vital role in improving efficiency and reducing traffic congestion, and there is more and more quality scientific research on this issue.…”

Section: Introductionmentioning

confidence: 99%

“…The state-of-the-art solution (called LSTM*) is a contribution to further improving previously published research [7]. In a previous publication, the authors optimized three parameters of the LSTM network, including window size, the number of epochs, and the number of neurons.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Deep Neural Networks for Traffic Speed Forecasting Regarding Sustainable Traffic Management Using Probe Data from Registered Transport Vehicles on Multilane Roads

Do,

Tran,

et al. 2024

Sustainability

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Early forecasting of vehicle flow speeds is crucial for sustainable traffic development and establishing Traffic Speed Forecasting (TSF) systems for each country. While online mapping services offer significant benefits, dependence on them hampers the development of domestic alternative platforms, impeding sustainable traffic management and posing security risks. There is an urgent need for research to explore sustainable solutions, such as leveraging Global Positioning System (GPS) probe data, to support transportation management in urban areas effectively. Despite their vast potential, GPS probe data often present challenges, particularly in urban areas, including interference signals and missing data. This paper addresses these challenges by proposing a process for handling anomalous and missing GPS signals from probe vehicles on parallel multilane roads in Vietnam. Additionally, the paper investigates the effectiveness of techniques such as Particle Swarm Optimization Long Short-Term Memory (PSO-LSTM) and Genetic Algorithm Long Short-Term Memory (GA-LSTM) in enhancing LSTM networks for TSF using GPS data. Through empirical analysis, this paper demonstrates the efficacy of PSO-LSTM and GA-LSTM compared to existing methods and the state-of-the-art LSTM approach. Performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Median Absolute Error (MDAE) validate the proposed models, providing insights into their forecasting accuracy. The paper also offers a comprehensive process for handling GPS outlier data and applying GA and PSO algorithms to enhance LSTM network quality in TSF, enabling researchers to streamline calculations and improve supposed model efficiency in similar contexts.

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“…These localization systems have become essential to the tremendous demands for new services and applications in multiple civil and military domains. GPS is very widely used in several domains, such as in monitoring systems [1][2][3][4][5][6], intelligent systems [7][8][9], power management [10], and other applications. Moreover, GPS can provide real-time location information for an unlimited number of users.…”

Section: Introductionmentioning

confidence: 99%

Design and Study of a Microstrip Patch Antenna for GPS Application

Ayad¹,

Saoudi²,

Alharbi³

et al. 2022

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A microstrip patch antenna for Global Positioning System (GPS) application is presented in this study. It has a rectangular shape with two notched slots. The notched slots are introduced to improve the adaptation between the microstrip line and the antenna. The antenna was designed and simulated by CST using FR-4 material as the substrate with relative permittivity of 4.3. The proposed microstrip antenna is designed to operate in the GPS band frequency from 1.555 GHz to 1.595 GHz. The performance analysis of the proposed antennas has been carried out in terms of return loss (dB), gain (dB), and VSWR.

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Short-Term Traffic Speed Forecasting Model for a Parallel Multi-Lane Arterial Road Using GPS-Monitored Data Based on Deep Learning Approach

Cited by 14 publications

References 56 publications

AST3DRNet: Attention-Based Spatio-Temporal 3D Residual Neural Networks for Traffic Congestion Prediction

AST3DRNet: Attention-Based Spatio-Temporal 3D Residual Neural Networks for Traffic Congestion Prediction

Enhanced Deep Neural Networks for Traffic Speed Forecasting Regarding Sustainable Traffic Management Using Probe Data from Registered Transport Vehicles on Multilane Roads

Design and Study of a Microstrip Patch Antenna for GPS Application

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