Research On The Spatial Renewal Strategy Of Slow Traffic In The Living Circle Of Beijing Subway

With the continuous promotion of the urbanisation development process,China’s urban construction is gradually shifting from incremental development to the stage of stock upgrading quality and incremental structure adjustment,the population of metro travel in first-tier cities is large,and the travel chain of urban residents mainly revolves around the metro,with the metro as the main focus,supplemented by walking and bicycle riding at the metro station feeder junction as the slow travel mode loved by urban residents,becoming the main travel choice,but To date,however,there are still a number of problems with the urban slow-moving transport space,making it an unsatisfactory environment for slow-moving travel.Slow traffic space is a necessary space for urban life,including urban transport function,urban landscape and public space quality,and improving slow traffic space is crucial for urban vitality.This paper analyses the Beijing underground living circle,studies the slow traffic space,conducts an example study by constructing an evaluation index system,and proposes an update strategy to provide reference for slow traffic planning in Beijing.The article is divided into four parts:Part I: Through the summary and analysis of the existing research at home and abroad,the necessity of improving the living circle of Beijing subway and the importance of improving the slow traffic space within the living circle are concluded,and the research purpose and research significance,research methods and research framework of the slow traffic space of the Beijing subway living circle are clarified.The second part: The application was created through the Gaode Map control platform and obtained K.The API interface was used to crawl the POI points of interest in the main urban area of Beijing and to clean and reclassify the data,and the Tyson polygon area,kernel density and the number of POIs of the metro stations were combined with Arc GIS platform to classify six functional types of metro stations in Beijing.Part 3: For the typical subway stations screened out in Chapter 3,the activity range of slow travelers at each station is determined by cycling OD data,and Baidu Street View pictures are collected and field research is carried out according to the activity trajectory,and a database of environmental elements of research objects is constructed through multi-source big data.The literature review method,frequency statistics method and expert revision method were used to collect and sort out the evaluation factors,and the evaluation system was constructed from the four perspectives of safety,accessibility,comfort and characteristics.The evaluation factors were quantified according to the image semantic segmentation software of Arc GIS platform and deep learning fully convolutional network(FCN).On the Arc GIS platform,the evaluation index is divided into different levels according to the natural breakpoint method and the median value method,and the current situation of the slow traffic space of the subway station is analyzed according to the low subregion of the station.Part 4: The SDNA software is used to analyse the access potential of the metro stations at different travel radii,and to overlay the analysis based on the spatial accessibility of the station area and the evaluation scores,and to propose corresponding optimisation strategies for the different spaces.