Road Traffic Forecast Based on Meteorological Information through Deep Learning Methods

Braz, Fernando José; Ferreira, Joao C Ferreira or Joao; Gonçalves, Francisco; Weege, Kawan; Almeida, João; Baldo, Fabiano; Gonçalves, Pedro

doi:10.3390/s22124485

Cited by 15 publications

(4 citation statements)

References 19 publications

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“…On the one hand, it is about the limitations of data. The data in this paper are spatio-temporal data, but the factors that affect the traffic flow are not only spatio-temporal data, but also weather conditions [ 22 ], whether it is a holiday, the proportion of car models [ 23 ], and the individual behavior of drivers. In this study, no comprehensive analysis of other factors can be conducted, which will reduce the accuracy of prediction.…”

Section: Discussionmentioning

confidence: 99%

Multi-Section Traffic Flow Prediction Based on MLR-LSTM Neural Network

Shi

2022

Sensors

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As the aggravation of road congestion leads to frequent traffic crashes, it is necessary to relieve traffic pressure through traffic flow prediction. As well, the traffic flow of the target road section to be predicted is also closely related to the adjacent road sections. Therefore, in this paper, a prediction method based on the combination of multiple linear regression and Long-Short-Term Memory (MLR-LSTM) is proposed, which uses the incomplete traffic flow data in the past period of time of the target prediction section and the continuous and complete traffic flow data in the past period of time of each adjacent section to jointly predict the traffic flow changes of the target section in a short time. The accurate prediction of future traffic flow changes can be solved based on the model supposed when the traffic flow data of the target road section is partially missing in the past period of time. The accuracy of the prediction results is the same as that of the current mainstream prediction results based on continuous and non-missing target link flow data. Meanwhile, there is a small-scale improvement when the data time interval is short enough. In the case of frequent maintenance of cameras in actual traffic sections, the proposed prediction method is more feasible and can be widely used.

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Section: Discussionmentioning

confidence: 99%

Multi-Section Traffic Flow Prediction Based on MLR-LSTM Neural Network

Shi

2022

Sensors

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“…The prediction of cellular network traffic involves forecasting future cellular network traffic data through the analysis of the spatial-temporal distribution of known cellular traffic data. Over the past decade, deep learning techniques have gained widespread application in time-series prediction, including the prediction of vehicle flow and subway passenger flow [9][10][11][12][13][14]. Incorporating deep learning into time-series prediction has significantly contributed to the advancement of cellular network traffic prediction.…”

Section: Related Workmentioning

confidence: 99%

DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction

Cai,

Tan,

Zhang

et al. 2024

Applied Sciences

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As network technology continues to develop, the popularity of various intelligent terminals has accelerated, leading to a rapid growth in the scale of wireless network traffic. This growth has resulted in significant pressure on resource consumption and network security maintenance. The objective of this paper is to enhance the prediction accuracy of cellular network traffic in order to provide reliable support for the subsequent base station sleep control or the identification of malicious traffic. To achieve this target, a cellular network traffic prediction method based on multi-modal data feature fusion is proposed. Firstly, an attributed K-nearest node (KNN) graph is constructed based on the similarity of data features, and the fused high-dimensional features are incorporated into the graph to provide more information for the model. Subsequently, a dual branch spatio-temporal graph neural network with an attention mechanism (DBSTGNN-Att) is designed for cellular network traffic prediction. Extensive experiments conducted on real-world datasets demonstrate that the proposed method outperforms baseline models, such as temporal graph convolutional networks (T-GCNs) and spatial–temporal self-attention graph convolutional networks (STA-GCNs) with lower mean absolute error (MAE) values of 6.94% and 2.11%, respectively. Additionally, the ablation experimental results show that the MAE of multi-modal feature fusion using the attributed KNN graph is 8.54% lower compared to that of the traditional undirected graphs.

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“…Jose Braz et al [ 13 ] developed and compared three deep learning models for forecasting the traffic flow in the Barra and Costa Nova regions. They divided their dataset into training, validation, and testing sets (i.e., the holding out method).…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Machine Leaning Algorithms for Streets Traffic Prediction: A Smart Home Use Case

Feng

Ahvar

Lee

2023

Sensors

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This paper defines a smart home use case to automatically adjust home temperature and/or hot water. The main objective is to reduce the energy consumption of cooling, heating and hot water systems in smart homes. To this end, the residents set a temperature (i.e., X degree Celsius) for home and/or hot water. When the residents leave homes (e.g., for work), they turn off the cooling or heating devices. A few minutes before arriving at their residences, the cooling or heating devices start working automatically to adjust the home or water temperature according to the residents’ preference (i.e., X degree Celsius). This can help reduce the energy consumption of these devices. To estimate the arrival time of the residents (i.e., drivers), this paper uses a machine learning-based street traffic prediction system. Unlike many related works that use machine learning for tracking and predicting residents’ behaviors inside their homes, this paper focuses on predicting resident behavior outside their home (i.e., arrival time as a context) to reduce the energy consumption of smart homes. One main objective of this paper is to find the most appropriate machine learning and neural network-based (MLNN) algorithm that can be integrated into the street traffic prediction system. To evaluate the performance of several MLNN algorithms, we utilize an Uber’s dataset for the city of San Francisco and complete the missing values by applying an imputation algorithm. The prediction system can also be used as a route recommender to offer the quickest route for drivers.

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Road Traffic Forecast Based on Meteorological Information through Deep Learning Methods

Cited by 15 publications

References 19 publications

Multi-Section Traffic Flow Prediction Based on MLR-LSTM Neural Network

Multi-Section Traffic Flow Prediction Based on MLR-LSTM Neural Network

DBSTGNN-Att: Dual Branch Spatio-Temporal Graph Neural Network with an Attention Mechanism for Cellular Network Traffic Prediction

Evaluation of Machine Leaning Algorithms for Streets Traffic Prediction: A Smart Home Use Case

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