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The Risk Model of Traffic Engineering Investment and Financing by Artificial Intelligence

This study aims to analyze the influencing factors and mechanisms of investment and financing risks in transportation projects so that regions do not restrict the transportation investment and financing risk models in all areas to achieve intelligent transportation financial risk assessment. Firstly...

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Detalles Bibliográficos
Autores principales: Wang, Shangen, Zhang, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9365536/
https://www.ncbi.nlm.nih.gov/pubmed/35965773
http://dx.doi.org/10.1155/2022/9402472
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author Wang, Shangen
Zhang, Wei
author_facet Wang, Shangen
Zhang, Wei
author_sort Wang, Shangen
collection PubMed
description This study aims to analyze the influencing factors and mechanisms of investment and financing risks in transportation projects so that regions do not restrict the transportation investment and financing risk models in all areas to achieve intelligent transportation financial risk assessment. Firstly, the investment and financing modes are studied and analyzed. According to the analysis of intellectual investment and the financing report of traffic engineering infrastructure, a traffic engineering investment and a financing model based on intelligent computing is established, which is based on artificial intelligence (AI) big data analysis technology. Secondly, the investment and the financing risk model of traffic engineering is established based on multimodal learning. Finally, the urban traffic engineering of Xi'an is taken as the research object. Based on its investment and financing data in the construction of urban roads, the risk assessment is carried out. Combined with risk influencing factors, the accuracy of the intelligent calculation in the risk assessment model is calculated. Different grades of urban transportation projects have different risks in the investment and financing of transportation projects. The results show that different levels of urban transport projects have different risks in the investment and financing (IAF) performance of transport projects. Among them, the risk index of the first-class project is the highest, reaching 0.55. The risk index of the second-class project is 0.49. The results before and after using the flow engineering IAF risk model are compared. In the test results of traffic engineering risk, all target risks did not increase after the AI-based traffic engineering IAF is tested. The model test results for credit risk and financial risk are the highest at 70 and 60, respectively. Combined with the actual urban development situation, this study can provide investment and financing risk models for urban transportation projects in different regions and provide a reference for the resource control of transportation projects. This study uses AI to learn and analyze traffic engineering investment and financing data and more accurately provide data references for traffic engineering investment and financing risk models.
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spelling pubmed-93655362022-08-11 The Risk Model of Traffic Engineering Investment and Financing by Artificial Intelligence Wang, Shangen Zhang, Wei Comput Intell Neurosci Research Article This study aims to analyze the influencing factors and mechanisms of investment and financing risks in transportation projects so that regions do not restrict the transportation investment and financing risk models in all areas to achieve intelligent transportation financial risk assessment. Firstly, the investment and financing modes are studied and analyzed. According to the analysis of intellectual investment and the financing report of traffic engineering infrastructure, a traffic engineering investment and a financing model based on intelligent computing is established, which is based on artificial intelligence (AI) big data analysis technology. Secondly, the investment and the financing risk model of traffic engineering is established based on multimodal learning. Finally, the urban traffic engineering of Xi'an is taken as the research object. Based on its investment and financing data in the construction of urban roads, the risk assessment is carried out. Combined with risk influencing factors, the accuracy of the intelligent calculation in the risk assessment model is calculated. Different grades of urban transportation projects have different risks in the investment and financing of transportation projects. The results show that different levels of urban transport projects have different risks in the investment and financing (IAF) performance of transport projects. Among them, the risk index of the first-class project is the highest, reaching 0.55. The risk index of the second-class project is 0.49. The results before and after using the flow engineering IAF risk model are compared. In the test results of traffic engineering risk, all target risks did not increase after the AI-based traffic engineering IAF is tested. The model test results for credit risk and financial risk are the highest at 70 and 60, respectively. Combined with the actual urban development situation, this study can provide investment and financing risk models for urban transportation projects in different regions and provide a reference for the resource control of transportation projects. This study uses AI to learn and analyze traffic engineering investment and financing data and more accurately provide data references for traffic engineering investment and financing risk models. Hindawi 2022-08-03 /pmc/articles/PMC9365536/ /pubmed/35965773 http://dx.doi.org/10.1155/2022/9402472 Text en Copyright © 2022 Shangen Wang and Wei Zhang. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wang, Shangen
Zhang, Wei
The Risk Model of Traffic Engineering Investment and Financing by Artificial Intelligence
title The Risk Model of Traffic Engineering Investment and Financing by Artificial Intelligence
title_full The Risk Model of Traffic Engineering Investment and Financing by Artificial Intelligence
title_fullStr The Risk Model of Traffic Engineering Investment and Financing by Artificial Intelligence
title_full_unstemmed The Risk Model of Traffic Engineering Investment and Financing by Artificial Intelligence
title_short The Risk Model of Traffic Engineering Investment and Financing by Artificial Intelligence
title_sort risk model of traffic engineering investment and financing by artificial intelligence
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9365536/
https://www.ncbi.nlm.nih.gov/pubmed/35965773
http://dx.doi.org/10.1155/2022/9402472
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