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

• Published in: Scientific reports Vol. 14; no. 1; pp. 23929 - 30
• Main Authors: Amini Pishro, Ahad, Zhang, Shiquan, L’Hostis, Alain, Liu, Yuetong, Hu, Qixiao, Hejazi, Farzad, Shahpasand, Maryam, Rahman, Ali, Oueslati, Abdelbacet, Zhang, Zhengrui
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 13.10.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/986; 639/705/1041; 639/705/1042; 639/705/531; Accuracy; Algorithms; Architecture, space management; Case studies; Classification; Clustering methods; Decision making; Humanities and Social Sciences; Learning algorithms; Machine learning; Machine Learning algorithms; Mathematical models; multidisciplinary; Neural networks; Rail Transit Station Classification; Regression analysis; Regression models; Science; Science (multidisciplinary); Sustainable development; Transit Oriented Development; Urban development; Urban planning; Urban sprawl; Rail Transit Station Classification; Regression models; Transit Oriented Development; Clustering methods; Machine Learning algorithms
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-75541-8

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 forecasts, and data clustering using mathematical algorithms reveals distinct categories of stations. Evaluation metrics confirm the rationality and accuracy of the results. Additionally, a neural network achieving high accuracy on labeled data enhances the model’s predictive capabilities for unlabeled instances. The research demonstrates high accuracy, with the Mean Squared Error (MSE) for regression models (Multiple Linear Regression (MLR), Deep-Learning Neural Network (DNN), and K-Nearest Neighbor (KNN)) remaining below 0.012, while the neural networks used for station classification achieve 100% accuracy across seven time intervals and 98.15% accuracy for the eighth, ensuring reliable ridership forecasts and classification outcomes. Accuracy in rail transit station classification is critical, as it not only strengthens the model’s predictive capabilities but also ensures more reliable data-driven decisions for transit planning and development, allowing for more precise ridership forecasts and evidence-based strategies for optimizing TOD. This classification model provides stakeholders with valuable insights into the dynamics and features of rail transit stations, supporting sustainable urban development planning.