Abstract
New research on artificial intelligence is the subject of a new report. According to news reporting from Handan, People’s Republic of China, by NewsRx journalists, research stated, “Understanding the built environment’s impact on metro ridership is essential for developing targeted strategies for built environment renewal.” Funders for this research include Hebei Social Science Development Research Project in 2023. The news journalists obtained a quote from the research from Hebei University of Engineering: “Taking into consideration the limitations of existing studies, such as not proposing targeted strategies, using unified pedestrian catchment areas (PCA), and not determining the model’s accuracy, Beijing was divided into three zones from inside to outside by the distribution pattern of metro stations. Three PCAs were assumed for each zone and a total of 27 PCA combinations. The study compared the accuracy of the Ordinary Least Square (OLS) and several machine learning models under each PCA combination to determine the model to be used in this study and the recommended PCA combination for the three zones. Under the recommended PCA combinations for the three zones, the model with the highest accuracy was used to explore the built environment’s impact on metro ridership. Finally, prioritized stations for renewal were identified based on ridership and the built environment’s impact on metro ridership. The results are as follows: (1) The eXtreme Gradient Boosting (XGBoost) model has a higher accuracy and was appropriate for this study.”
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