Asia Pacific Academy of Science Pte. Ltd. (APACSCI) specializes in international journal publishing. APACSCI adopts the open access publishing model and provides an important communication bridge for academic groups whose interest fields include engineering, technology, medicine, computer, mathematics, agriculture and forestry, and environment.
As China's pillar industry, the property market has suffered a considerable impact in recent years, with a decline in turnover and many developers at risk of bankruptcy. As one of the most concerned factors for stakeholders, housing prices need to be predicted more objectively and accurately to minimize decision-making errors by developers and consumers. Many prediction models in recent years have been unfriendly to consumers due to technical difficulties, high data demand, and varying factors affecting house prices in different regions. A uniform model across the country cannot capture local differences accurately, so this study compares and analyses the fitting effects of multiple machine learning models using February 2024 new building data in Changsha as an example, aiming to provide consumers with a simple and practical reference for prediction methods. The modeling exploration applies several regression techniques based on machine learning algorithms, such as Stepwise regression, Robust regression, Lasso regression, Ridge regression, Ordinary Least Squares (OLS) regression, Extreme Gradient Boosted regression (XGBoost), and Random Forest (RF) regression. These algorithms are used to construct forecasting models, and the best-performing model is selected by conducting a comparative analysis of the forecasting errors obtained between these models. The research found that machine learning is a practical approach to property price prediction, with least squares regression and Lasso regression providing relatively more convincing results.
High-speed rail’s impact on athlete mobility, event management, and tourism: Enhancing recovery, accessibility, and experience
Vol 6, Issue 1, 2025
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Abstract
This study investigates the impact of high-speed rail (HSR) on athlete mobility, sport event management, and regional economic development, particularly in the context of major international sporting events. The research aims to address the role of HSR in improving accessibility, reducing travel times, and supporting efficient logistics for athletes, event staff, and spectators. This study employs a systematic literature review (SLR) methodology, using data from Scopus, to synthesize existing research. The review process involved screening 962 documents, resulting in the analysis of 71 relevant articles. The study follows PRISMA and SPAR-4-SLR guidelines to ensure methodological rigor and transparency in selecting and analyzing studies. The study highlights both the positive contributions of HSR, including enhanced mobility and economic benefits for host cities, as well as the challenges posed by spatial imbalances in infrastructure development. While HSR facilitates improved connectivity and contributes to the economic growth of metropolitan areas, smaller cities and peripheral regions often face marginalization in terms of economic opportunities and event participation. The findings suggest that although HSR significantly enhances event logistics, there is a pressing need for more inclusive infrastructure planning to ensure equitable access to these benefits. Additionally, the study underscores the environmental sustainability of HSR systems as an alternative to more carbon-intensive transport modes. Overall, this research provides insights into how HSR can be leveraged to improve the management of international sporting events and contribute to long-term urban and regional development while also addressing the existing disparities in accessibility and economic development across regions.
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Prof. Mehmet Cetin
Kastamonu University,
Turkey
