Fuzzy Inspired Deep Belief Network for the Traffic Flow Prediction in Intelligent Transportation System Using Flow Strength Indicators

George, Shiju; Santra, Ajit Kumar

doi:10.1089/big.2019.0007

Cited by 7 publications

(3 citation statements)

References 34 publications

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“…Although in the previous study the dominant topic in the field of logistics and transportation were intelligent transportation systems (ITS), in recent years, logistics planning has gained slightly more prominence. Routing optimization (Dimokas et al, 2020;Wiegmans et al, 2020) and traffic monitoring (Ibrahim et al, 2020;Hwang, 2021;George & Santra, 2020; prevail in the ITS domain. In addition, process traceability (Yineng Harrison et al, 2020) is a newly emerged topic in literature in this context.…”

Section: In What Areas Of Scm Is Da Being Applied?mentioning

confidence: 99%

Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review

Darbanian,

Brandtner,

Falatouri

et al. 2024

OSCM: An Int. Journal

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In recent years, there has been a growing surge of interest in the application of data analytics (DA) within the realm of supply chain management (SCM), attracting attention from both practitioners and researchers. This paper presents a comprehensive examination of recent implementations of DA in SCM. Employing a systematic literature review (SLR), we conducted a meticulous analysis of over 354 papers. Building upon a prior SLR conducted in 2018, we identify contemporary areas where DA has been applied across various functions within the supply chain and scrutinize the DA models and techniques that have been employed. A comparison between past findings and the current literature reveals a notable upsurge in the utilization of DA across most SCM functions, with a particular emphasis on the prevalence of predictive analytics models in contemporary SCM applications. The findings of this paper offer a detailed insight into the specific DA models and techniques currently in use across various SCM functions. Additionally, a discernible increase in the adoption of mixed or hybrid DA models is observed. However, several research gaps persist, including the need for more attention to real-time DA in SCM, the integration of publicly available data, and the application of DA to mitigate uncertainty in SCM. To address these areas and guide future research endeavors, the paper concludes by delineating six concrete research directions. These directions offer valuable avenues for further exploration in the field.

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Section: In What Areas Of Scm Is Da Being Applied?mentioning

confidence: 99%

Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review

Darbanian,

Brandtner,

Falatouri

et al. 2024

OSCM: An Int. Journal

View full text Add to dashboard Cite

show abstract

“…У задачах прогнозування транспортних потоків [16] можна спостерігати поєднання теорії нечітких обчислень та глибокої залишкової мережі для вирішення проблеми невизначеності. Такі системи, як правило, містять ряд модулів, а саме: вхідні дані, глибоку згорткову мережу, нечітку мережу, модуль злиття та предиктор.…”

Section: постановка проблемиunclassified

The Construction of Intelligent Rule-Based System With Deep-Learning Functionality for Oil and Gas Wells Drilling

ПРОЦЮК

2024

Вісник Хмельницького національного університету. Серія: Технічн

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The article is devoted to solving the problems of predictive modeling using machine learning methods under conditions of uncertainty in huge data sets created in various sectors of the economy, in particular, the oil and gas industry, due to the spread of IoT and cloud services. The author notes that existing machine learning methods, including widely used deep learning methods, face limitations in achieving high prediction accuracy due to various uncertainties. The article explores fuzzy learning approaches to overcome uncertainties such as imprecision and ambiguity, but emphasizes their limitations of incompleteness and vagueness. It is argued that rule-based expert systems augmented with deep learning offer a more robust framework for dealing with such uncertainties by integrating associative memory, helping to produce more accurate predictive models, especially in data-intensive scenarios such as oil and gas drilling processes. The study focuses on improving predictive modeling under conditions of uncertainty in large data sets. We discuss the limitations of fuzzy learning when processing incomplete data and propose a hybrid approach to the problem. Integration of the associative memory learning method into the logical inference procedures allows to identify accurate data models, thereby improving the quality of forecasting under uncertainty. This integrated approach aims to improve forecasting accuracy by leveraging the strengths of deep learning in pattern recognition and the ability of rule-based systems to cope with uncertainty. The main result of the research work is the development of a new method that integrates a level of deep processing with associative memory into a rule-based system to improve forecasting accuracy under uncertainty. This was achieved by using a multilayer neural network and adding additional parameters. This method significantly improved the accuracy of predicting the dynamics of operational parameters during drilling. Thus, the presented study is a new contribution to the task of developing forecasting methods under conditions of uncertainty, which is especially relevant for oil and gas drilling.

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“…Traditional traffic flow prediction models, such as the ARIMA model [1], mainly use linear time series methods, which cannot deal with complex space-time dependence problems. In order to overcome the defects of the linear time series models, researchers devoloped machine learning methods [2], [3] and deep learning methods [4], [5], [6], such as STSGCN [7] which extracted spatio-temporal dependence features of spatio-temporal map to achieve more accurate prediction. However, the existing methods rarely consider the impact of the spatio-temporal characteristics of multi-modal VOLUME 11, 2023 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.…”

Section: Introductionmentioning

confidence: 99%

Traffic Flow Prediction Based on Two-Channel Multi-Modal Fusion of MCB and Attention

Qin

2023

IEEE Access

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The accuracy of urban traffic flow prediction is influenced by nearby regional road network, historical traffic flow and seasonal climate, which has complex spatial and temporal dependence. In view of the above factors, we proposed a multi-modal traffic flow prediction model fusing road network, historical traffic flow and weather data. Firstly, the weighted spatio-temporal graph was constructed based on the traffic flow time series data, and the weighted STSGCN model was used to extract the spatio-temporal graph features. Secondly, the image sequence was constructed by road network, vehicle track and sensor position data, and the visual features were extracted by ResNet. Finally, based on the MCB and Attention two-channel multi-modal fusion model, the spatio-temporal graph features and the visual features of the image sequence were fused to obtain the aligned fusion vector. Finally, the aligned fusion vector was combined with the weather feature vector to complete the traffic flow prediction. The experimental results showed that the prediction results of our proposed model were better than those of other baseline models. At the same time, the ablation results also proved the effectiveness of each module in our proposed prediction model.

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Fuzzy Inspired Deep Belief Network for the Traffic Flow Prediction in Intelligent Transportation System Using Flow Strength Indicators

Cited by 7 publications

References 34 publications

Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review

Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review

The Construction of Intelligent Rule-Based System With Deep-Learning Functionality for Oil and Gas Wells Drilling

Traffic Flow Prediction Based on Two-Channel Multi-Modal Fusion of MCB and Attention

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