Impact of the Built Environment and Bicycling Psychological Factors on the Acceptable Bicycling Distance of Rural Residents

Wang, Yan; Ao, Yibin; Zhang, Yuting; Liu, Yan; Zhao, Lei; Chen, Yunfeng

doi:10.3390/su11164404

Cited by 14 publications

(12 citation statements)

References 59 publications

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“…A positive correlation is found between new rural building density (and road density) and the number of vehicles in rural households, and the higher the building density, the more people will choose to travel by car or electric bicycle. The closer the destination, the more rural residents will decide to go on foot (Ao et al, 2020a,b) and the lower the average daily travel CO 2 emission (Ao et al, 2019a,b;Wang et al, 2019). The acceptable cycling distance of rural residents will be increased where special bicycle lanes are provided in rural areas.…”

Section: Travel Research In Rural Chinamentioning

confidence: 99%

Built Environment Impacts on Rural Residents’ Daily Travel Satisfaction

Zhang

et al. 2022

Front. Ecol. Evol.

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The rapid urbanization in China urges scholars to investigate the impacts of built environment on the level of travel satisfaction of rural residents to improve their quality of life and make planning exercises more human-centric. This study samples six villages out of the 25 top rural areas in Chengdu, Sichuan, China, as the research object and constructs a structural equation model to explore the direct and indirect impacts of the built environment on daily travel satisfaction of rural residents. The research finds that building density (0.609), road density (0.569), the number of accessible markets (0.314), and private car ownership (0.02) have significant positive impacts on travel satisfaction. Public transport (−0.063) has a direct negative impact on travel satisfaction. Consequently, in order to further improve travel satisfaction, construction departments and rural planners should improve the building and road densities of new rural areas and increase the number of accessible markets. The convenience of rural public transport services also needs improvement.

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Section: Travel Research In Rural Chinamentioning

confidence: 99%

Built Environment Impacts on Rural Residents’ Daily Travel Satisfaction

Zhang

et al. 2022

Front. Ecol. Evol.

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“…Hence, multiple linear regression analysis was conducted using SPSS (25.0) software. The multiple linear regression has seen wide use in the identification of the critical factors that affect the management of PPP projects, and it can effectively evaluate the relationship between several independent variables (i.e., factors) and a dependent variable (i.e., willingness) [97,130,131]. The general form of this study's linear regression model was as follows:…”

Section: Multiple Linear Regression Analysismentioning

confidence: 99%

Investigating Critical Factors That Encourage Private Partners to Participate in Sports and Leisure Characteristic Town Public-Private Partnerships: Evidence from China

Chen¹,

Zhao²,

Zhou³

et al. 2020

Sustainability

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The Sports and Leisure Characteristic Town (SLCT) has become a reasonable strategy for enhancing the sustainability of new urbanization processes in rural China. Although the Chinese government has issued a series of policies to support the development of SLCTs, limited financial resources are a major obstacle. Thus, public-private partnerships (PPPs) have been increasingly encouraged for establishing SLCT projects. However, the factors that significantly influence private partners’ willingness to participate in SLCT PPP projects remain unclear. The authors of this paper conducted expert interviews and questionnaire surveys concerning the SLCT PPP projects that have been implemented in China, identifying 23 factors that can be grouped into five major components using principal component factor analysis. Multiple linear regression was then performed to evaluate the relationships between factors and private partners’ willingness to participate. The results indicated that the factors that critically influence private partners’ willingness to participate include stakeholder factors, the internal factors of private partners, external environmental risk factors, the supporting measure factors of SLCTs, and the locational factors of SLCTs. This work also offers suggestions for encouraging private partners to participate in SLCT PPP projects. This study can provide a theoretical basis and practical guidance for government and private partners in order to help them implement sustainable SLCT PPP projects.

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“…When analysing such data (same as when analysing the influence of topography on walking), it is necessary to consider the fact that topography is one of the influence factors. Further major influence factors are the density, quality and safety of bike paths in the given area [42], [43].…”

Section: Research On the Influence Of Topography On Pamentioning

confidence: 99%

Smartphone-Based Sensing: Lifestyle and Mobility Data Interpretation by Smart Cities

Sobková

Jiráèek

Èertický

2020

WIT Transactions on Ecology and the Environment

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The increasing demand for data and smart solutions is one of the fastest growing sectors of human activity. In recent decades smartphones and mobile phones have become a significant and stable source of data. Architects and urban planners have used them in various cases to identify urban patterns. This paper focuses on data gathered by fitness tracker applications which collect information about the movement of their users. The applications record the trajectory of the movement and detect the mode of transport. They require some basic information about the user (age, weight, height and sex) to calculate their caloric consumption. The data from activity tracking smartphone applications create a data lake that can be transformed into a new data source for the designing of healthier and more liveable cities. Combining the data layers and analysing them further could reveal properties and qualities of life in a given location that would not be apparent without processing these data. By analysing the data, we can observe the current state as well as tendencies in human behaviour over longer periods of time. Through the observation and comparison of physical activity in different urban contexts (topography, size of settlement, density of population, density of infrastructure, quality of public spaces, location, etc.), we can develop new alternatives and better knowledge of the influence the above-mentioned factors have on the life in cities. This paper describes data layer combinations that bring novel insights into the connection of physical activity and urban contexts by using data mining technology based on smartphone applications. The theoretical framework can be subsequently applied to various data sets with certain properties.

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Impact of the Built Environment and Bicycling Psychological Factors on the Acceptable Bicycling Distance of Rural Residents

Cited by 14 publications

References 59 publications

Built Environment Impacts on Rural Residents’ Daily Travel Satisfaction

Built Environment Impacts on Rural Residents’ Daily Travel Satisfaction

Investigating Critical Factors That Encourage Private Partners to Participate in Sports and Leisure Characteristic Town Public-Private Partnerships: Evidence from China

Smartphone-Based Sensing: Lifestyle and Mobility Data Interpretation by Smart Cities

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