Estimating Public Transportation Accessibility in Metropolitan Areas: A Case Study and Comparative Analysis

Abstract

Accessibility-oriented public transportation planning can improve the operational efficiency of public transportation, guide orderly urban development, and alleviate issues such as traffic congestion, environmental pollution, and resource consumption in large cities. To promote the practical application and widespread adoption of public transportation accessibility estimating systems, this study proposes an improved public transport accessibility levels (PTAL) method. It innovatively incorporates residents’ preference indices for different modes of transportation and addresses the challenge of missing timetable data in the calculation process. Using actual data from Shenzhen, a case study is conducted to analyze the public transportation accessibility index and compare the results obtained through k-means clustering, the equal spacing method, the quantile method, and the application of the London PTAL method. The research findings indicate that the optimal number of clusters for public transportation accessibility index analysis in large cities is six when using clustering algorithms. Among the statistical analysis methods, the quantile method shows favorable performance. Furthermore, a comprehensive comparison of different classification methods confirms that the improved PTAL method offers better discrimination in estimating public transportation accessibility levels compared to the London PTAL method. The study concludes by providing guidance on how cities with different characteristics can reference the improved PTAL method.