Evaluation And Optimization Method Of Walking Network In Rail Station Catchment Area

The development of urban rail transit has been systematically promoted at the national level.The rational development of rail station catchment areas is a strong driving force for urban renewal efforts,as these areas are the main public function areas and traffic intersections of large cities.The problem of "last mile" travel between stations and neighboring towns can be solved by creating a high-quality walking network in the rail station catchment area,which will also improve the effectiveness of stations.In order to improve the construction of walking network and support the coordinated development of rail transit and cities,it’s important to study the evaluation and optimization methods of walking network in rail station catchment areas.It can effectively quantify the characteristics of walking networks,identify potential problems and optimize them.Based on rail transit card swiping data,POI and road network data,and walkability data,the location of the station,peak hour passenger flow,number of various POIs in the station catchment area,number of bus stops,and walkability were extracted as clustering indicators for the classification of the rail station catchment area.Considering that the indicators are numerical and have differences in data dimensions,the K-means clustering method is applied to classify the rail station catchment areas.The key elements influencing the walking network in the station catchment area are broken down into four categories using Maslow’s hierarchy of needs theory: accessibility,safety,convenience,and comfort.Integration,permeability,relative walking width,street surveillance,motor traffic impact,POI density,POI diversity,greenery ratio,sky openness,and non-linear coefficient are chosen as evaluation indicators for the walking network in order to take data availability into account.The entropy weighting approach is used to establish the indicator weights because all evaluation indicators are impartial,and the TOPSIS model is applied to create a walking network evaluation model.A walking network optimization framework is established by specifying the optimization objectives and principles of the walking network in the rail station catchment areas,proposing a set of optimization strategies,and dividing the strategies into three aspects: network repair,service facilities,and street interface.The hierarchy level should be considered during the walking network optimization.Therefore,the decision tree can be used for visualization,and a decision tree-based walking network optimization model is constructed.Taking Nanjing city as an example,the evaluation model is applied to analyze the walking network of Sanshanjie,Zhonghuamen,and Tianlongsi rail station catchment areas and verify the composite evaluation results.The characteristics of the walking networks of the three rail station catchment areas are analyzed based on the multidimensional indicators.The composite evaluation results and the multidimensional indicators are input into the optimization model to optimize the walking network in the rail station catchment areas according to the model results.