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Evaluating the role of AI and empirical models for predicting regional economic growth and transportation dynamics: an application of advanced AI approaches

Year: 2024

Type: article

Abstract: The advent of the artificial intelligence (AI) age offers substantial potentials for predicting regional gross domestic product (GDP) growth and transportation dynamics. This article presents an in-depth overview of the AI and empirical modeling techniques used in this area, emphasizing the significant possibilities that AI presents and discussing potential obstacles. The use of AI is essential in managing complicated data, allowing for effective analysis of detailed regional economic trends. This capacity will be essential for making economic policies and plans that respond to each region’s specific needs and capabilities. This paper first explores the relationship and impact of different modes of transportation and regional economic growth. Subsequently, various empirical models and methodological frameworks, including the factors employed for studied economic analysis were comprehensively discussed and summarized. In the last part, the discussion focuses on the potential role of AI to revolutionize regional economic research using different AI approaches. This includes its capacity to handle vast and intricate databases, its ability to forecast future patterns using historical and current data, and its assistance in advanced decision making . The present study enhances our awareness of how AI is revolutionizing the field of regional economic growth study, shedding light on both its current application and future possibilities. This study contributes to the advancement of AI predictive models in decision making for predicting regional economic growth across the globe.

Source: International Journal of Transportation Science and Technology

DOI: 10.1016/j.ijtst.2024.08.007

Authors: Xinyuan Wang, Xingyi Zhu, Muhammad Kashif Anwar, Qingwei Meng, Ninghua Zhong

Institution: Tongji University

Language: English

Topic: Regional Economic and Spatial Analysis

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDGs: Industry, innovation and infrastructure, Decent work and economic growth

Open Access status: diamond

Funders: Ministry of Science and Technology of the People's Republic of China, Tongji University, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities