Benchmarking Socio-Economic Impacts of High-Speed Rail Networks Using K-Nearest Neighbour and Pearson's Correlation Coefficient Techniques through Computational Model-Based Analysis

Featured Application This research is the world's first to assess the widespread benefits of HSR services to entire societies, including the young generation and adults. Also, the big data analytic techniques have been established through models in Python incorporating KNN and PCC techniques. Not only have high-speed rail (HSR) services stimulated the economy of many countries, but they have also significantly uplifted quality of lives (QoL) of countless people. For many decades, the aspiration for HSR network development has dramatically risen, and HSR networks have inevitably become an icon of civilisation. However, only a few successful HSR networks globally can truly generate socio-economic impacts on their societies. This research aims to understand the impact of HSR networks on social and economic impacts and to provide recommendations for success. This study is the world's first to examine the benefits of HSR across all community demographic groups, including young and elderly people. The findings will illustrate the QoL, economic, and educational elements' advantages in explicit terms. It has established two interconnected models via Python to codify a novel customised model for socio-economic evaluation. 'Pearson correlation coefficient' and 'K-Nearest Neighbour' techniques are applied to bolster the reliability of the research findings. The outcomes have been reviewed by 30 international HSR specialists. The benchmarking exhibits that socio-economic impacts apparently occur across vast areas. The insight stemming from this benchmarking also offers policy implications and empirical data for long-term HSR improvement, assisting the government in developing new methods for sustainable communities.