# Urban Greenway Planning and Designing Based on MGWR and the Entropy Weight Method  
[PERSON]  
1 School of Architecture, Chang'an University, Xi'an 710064, China; [EMAIL_ADDRESS]. promote urban cycling, and may even overcome other planning and policy changes [4]. In addition, although the city's public transport, transit, and private cars have supported the development of the city, they have also fragmented the urban space, limiting the accessibility and sharing of urban space. In this regard, it is necessary to plan some greenways in the urban space that connect the cultural veins and charming areas of city. The non-motorized urban transport system will be more dynamic. Because of the advantages of greenways, many cities have carried out practical research on greenway planning and issued relevant standards and documents, such as the Chengdu, Wuhan, Shanghai, and cities in the United States [6; 7; 8; 9]. How to rationally plan the greenway corridor and maximize the demand for green travel has become the focus of greenway researchers. The emergence of advanced tools such as big data and intelligent algorithms provides new opportunities for greenway planning. A great deal of theoretical and practical researches has been done on greenway planning. Suitability analysis is one of the most commonly used methods in greenway planning and design [10; 11]. Suitability assessment based on ratings and trade-offs of relevant factors is often used to determine the extent of green space, a process that usually requires support such as GIS [10]. The exploration of urban green spaces at the human scale has usually been done through manual surveys with small data sets that could not effectively support further research. Subsequently, the emergence of new data and analytical techniques has allowed access to high resolution open data such as road networks, points of interest (POIs), and travel trajectory points. These open data provide new research potential to assess previously unmeasurable factors, and new opportunities to measure greenway suitability [12]. Initially, suitability was mainly based on 3 Ds, and in 2010, [PERSON] and [PERSON] developed 5 Ds by adding the dimensions of destination accessibility and transport distance and focusing on measuring the relationship between the street environment and travel behavior in a more systematic way [13]. Researchers point out that the distance to the metro station, density, the accessibility to the destination, land use mix, POIs influence the greenway planning [10; 14; 15; 16]. POI data have been widely used in the study of urban issues, such as functional diversity analysis and urban vitality mapping. The POIs can fully reflect the urban spatial functional layout and density status and are an important source of data for the indicators among the 5 Ds. The methods used to calculate the suitability in greenway planning mainly include regression algorithms [17; 18], hierarchical analysis [14; 16; 19; 20; 21], the Delphi method [22], and line density [23]. Data from the 5 Ds can be projected onto the urban spatial layout and density of the city. Suitability calculations and greenway planning were carried out by projecting data from the 5 Ds onto roads [10]. Thus on one hand, this research will focus on suitability calculations, with emphasis on variable selection and method improvement. Greenway planning is strongly associated with bike share travel [10]. In quantitative research, bike-sharing data are used as the dependent variable in regression models to measure the impact of built environment factors [8]. Early studies on the impact of the built environment on bike share usage often rely on subjective evaluation. Recent studies mainly use urban big data to objectively evaluate the impact of the built environment on bike-sharing use patterns, and the results vary according to urban background and mobile culture. In East Asia, the impact of urbanization and population density on bike-sharing use is opposite to that of Western cities.