Amini Pishro Ahad, Zhang Shiquan, L'Hostis Alain, Liu Yuetong, Hu Qixiao, Hejazi Farzad, Shahpasand Maryam, Rahman Ali, Oueslati Abdelbacet, Zhang Zhengrui
School of Civil Engineering, Sichuan University of Science and Engineering, Zigong, 643000, China.
Univ. Gustave Eiffel, Ecole des Ponts, LVMT, Marne-la-Vallée, 77454, France.
Sci Rep. 2024 Oct 13;14(1):23929. 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.
准确对轨道交通站点进行分类对于成功的以公交为导向的发展（TOD）和城市可持续增长至关重要。本文介绍了一种将传统方法与先进机器学习算法相结合的新型分类模型。通过运用数学模型、聚类方法和神经网络技术，该模型提高了站点分类的精度，从而能够对站点属性进行更精细的评估。对成都轨道交通网络进行的全面案例研究验证了该模型的有效性，突出了其在优化TOD策略以及为城市规划者和政策制定者指导决策过程方面的价值。该研究采用了几个基于现有数据训练的回归模型来生成准确的客流量预测，并且使用数学算法进行数据聚类揭示了不同类别的站点。评估指标证实了结果的合理性和准确性。此外，一个在标记数据上实现高精度的神经网络增强了该模型对未标记实例的预测能力。研究显示出高精度，回归模型（多元线性回归（MLR）、深度学习神经网络（DNN）和K近邻（KNN））的均方误差（MSE）保持在0.012以下，而用于站点分类的神经网络在七个时间间隔内达到了100%的准确率，第八个时间间隔的准确率为98.15%，确保了可靠的客流量预测和分类结果。轨道交通站点分类的准确性至关重要，因为它不仅增强了模型的预测能力，还确保了在交通规划和发展中基于数据的决策更加可靠，从而能够进行更精确的客流量预测以及制定基于证据的优化TOD策略。这种分类模型为利益相关者提供了有关轨道交通站点动态和特征的宝贵见解，支持可持续城市发展规划。
