Machine learning-aided hybrid technique for dynamics of rail transit stations classification: a case study.

ABSTRACT: Accurate classification of rail transit stations is crucial for successful Transit-Oriented Development (TOD) and sustainable urban growth. This paper introduces a novel classification model integrating traditional methodologies with advanced machine learning algorithms. By employing mathematical models, clustering methods, and neural network techniques, the model enhances the precision of station classification, allowing for a refined evaluation of station attributes. A comprehensive case study on the Chengdu rail transit network validates the model's efficacy, highlighting its value in optimizing TOD strategies and guiding decision-making processes for urban planners and policymakers. The study employs several regression models trained on existing data to generate accurate ridership fore

SUBMITTER: Amini Pishro A 

PROVIDER: S-EPMC11471756 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature