Predictive Method for Assessing the Renewal Potential of Urban Rail Transit Station Areas Based on Passenger Flow and Spatial Heat Maps

Abstract:

Under the background of urban stock renewal， it is essential to strategically allocate limited funds and resources to existing rail transit station areas with higher potential returns. Scientifically identifying stations with renewal potential is critical for promoting urban spatial integration and structural optimization. From the perspective of sustainable development of rail transit passenger flow， this paper argues that station areas with renewal potential should possess both the capacity for passenger flow growth and the potential to stimulate surrounding vitality. To effectively detect changes in passenger flow–heat interaction types， it employs multi-source data， taking 408 metro stations in Shanghai as case studies. Explainable machine learning models are developed for the years 2019， 2021， and 2023 to identify the driving and limiting factors of station passenger flows. Based on this， station area passenger flow–heat interaction types are classified into four main categories and eight subcategories. The spatial Markov chain method is then applied to analyze the transition patterns of these interaction types from 2019 to 2021 and from 2021 to 2023. In addition， it explores the positive and negative spillover effects from neighboring station areas. Moreover， it proposes a method to pre-assess the renewal potential of station areas from the passenger flow-heat interaction perspective， providing a scientific foundation and policy suggestions for the urban renewal of existing rail transit stations.