| Under the background of carbon-neutral development,advocating green travel has become one of the main ways to alleviate urban traffic problems and reduce carbon emissions.In recent years,bike-sharing has developed rapidly in various cities in China,and it has become the main tool for residents to travel slowly,providing an opportunity to promote green travel.However,in the past incremental planning period,cities in our country had fallen into the misunderstanding of motor traffic,which led to the problems of poor riding friendliness and imperfect construction of slow-moving system in urban space environment.Therefore,under the concept of data enhancement design,it is of great significance to use big data to study the travel characteristics of bike-sharing and the influence mechanism of built environment on it,so as to guide the efficient operation of bike-sharing system and the construction of urban green slow-moving system.In this study,taking the downtown area of Chengdu as an example,using Mobike’s order data and multi-source city open data,the spatial analysis of bike-sharing’s big data by Python and GIS was conducted to reveal the spatial and temporal distribution characteristics of cycling trips.Based on the first law of geography and considering the scale effect,this paper constructs global regression and geographically weighted regression models on the street and microscopic scales to analyze the relationship between built environment and travel in bikesharing,so as to explore the spatial heterogeneity of built environment’s influence on bikesharing’s travel at different scales,with a view to discovering the shortcomings in the construction of cycling system and putting forward suggestions for optimizing bike-sharing’s development and slow travel environment from the perspective of planning.Firstly,through data analysis and visualization,the overall cycling characteristics,the spatial and temporal distribution characteristics of cycling trips on the street and micro scale are analyzed.It is found that the usage of bike-sharing has the time distribution characteristics of morning and evening peaks,and the riding activities are mainly based on the purpose of short-distance travel.In the spatial distribution,there is a high amount of riding along the track,which indicates that riding is mostly used to connect with rail transit and meet the demand of "the last mile".The riding density is high in areas with complex functions,dense commercial residence and office facilities.Then,based on the location conditions,construction density,road network,public transport facilities and land use,the built environment factor set is constructed,and the built environment variables with significant correlation with bike-sharing’s trip volume at different scales are screened out by using multiple collinearity test and global regression model,so as to explore the influence of built environment on bike-sharing’s trip at the global level.Through spatial autocorrelation analysis,it is proved that the characteristics of built environment elements as independent variables are not randomly distributed in space.Therefore,a geographically weighted regression model is constructed to analyze the spatial variation characteristics of the influence of built environment on weekday and weekend riding trips on different scales,and the results of the model are analyzed visually.The research shows that there are differences in the types of built-up environment factors that affect bike-sharing’s travel in different scales and time dimensions,and the correlation between variables is strong and weak,and there are positive and negative differences in space,which indicates that the built-up environment’s impact on bike-sharing’s travel is spatially non-stationary.Finally,based on the analysis of riding characteristics,the problems of resource scheduling are discovered from the aspect of policy governance.Based on the analysis of the influence mechanism of built environment on bike-sharing travel,the problems of riding system construction,collaborative planning and riding environment quality are discovered from the perspective of spatial design,and optimization suggestions are put forward for various problems from the perspective of planning management and design. |
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