Distance decay is a vital aspect for modeling spatial interactions of human movements and an indispensable input for land use planning and travel demand prediction models. Although many studies have investigated the usage demand of bike-sharing systems in an area, research investigating the distance decay patterns of using dockless bike-sharing systems (DLBS) from a spatially heterogeneous perspective based on large-scale datasets is lacking. This study firstly utilizes massive transaction record data from DLBS in Shanghai of China and online map navigator Application Programming Interface to empirically estimate the distance decay patterns of using DLBS and reveal the spatial heterogeneity in distance decay of using DLBS across different urban contexts. Afterward, this study examines the mechanism of spatial heterogeneity in distance decay, leveraging multiple data resources including Point of Interest (POI) data, demographic data, and road network data. The associations among the distance decay of using DLBS with built environment factors are investigated by multiple linear regression. Results indicate that factors such as population density, land use entropy, branch road density, and metro station density are significantly related to larger distance decay of using DLBS, while factors such as commercial land use ratio, industrial land use ratio, and motorway density are significantly linked to smaller distance decay in Shanghai. Lastly, we further employ an adaptative geographically weighted regression to investigate the spatial divergences of the influences of built environment factors on distance decay. Results reveal notably distinct and even inverse influences of a built environment factor on the distance decay of using DLBS in different urban contexts. The findings provide insights into the distance decay patterns of using DLBS in different urban contexts and their interactions with the built environment, which can support accurate planning and management of sustainable DLBS as per specific urban characteristics.

Spatial heterogeneity in distance decay of using bike sharing: An empirical large-scale analysis in Shanghai

To better understand dockless bike-sharing (DBS) usage and advance the knowledge on shared bicycle service, this study empirically investigated the riding behavior in the time and space dimensions based on multisource datasets. Taking Central Business District (CBD) and Beijing West Railway Station (BWRS) as study areas, this study analyzed and compared the DBS usage based on the traffic grid between the two study areas. Furthermore, the random forest (RF) model was applied to investigate the contribution of influencing factors on origin/ destination and origin–destination pair trip volume. Partial Dependence Plots (PDP) analysis was conducted to explore the nonlinear effects of influencing factors. Results show considerable variation across different scenarios. Variables such as government agencies, restaurants, bus stop distance, and metro distance show nonlinear and threshold effects on DBS usage. The findings offer valuable insights for urban infrastructure development and bike rebalancing strategies, and the formulation of green and sustainable transportation policies. 

Nonlinear effects of factors on dockless bike-sharing usage considering grid-based spatiotemporal heterogeneity

Predicting waste sorting intention of residents of Jiangsu Province, China

Scrambling behavior is one of the main causes of road traffic accidents in China. This study aimed to investigate the characteristics of drivers’ scrambling behavior and its influencing factors based on the theory of planned behavior. A total of 388 drivers answered the questionnaire and 359 provided valid data. The structure equation model of scrambling behavior showed that positive attitudes towards scrambling behavior, subjective norms and perceived behavior control increased the intention of scrambling behavior. Furthermore, the path coefficient of the structural equation model for the scrambling behavior revealed that attitude was the most important factor influencing scrambling behavior. Thus, to prevent drivers from scrambling, traffic regulators should focus on improving drivers’ attitude towards this behavior, while auxiliary measures should be enacted to regulate drivers' subjective norms and perceptual behavior control. Implications on intervention strategy and policy to reduce scrambling behavior are discussed.

Modeling drivers’ scrambling behavior in China: An application of theory of planned behavior

Does government involvement and awareness of benefit affect Ghanaian's willingness to pay for renewable green electricity?

With the rapid global expansion of dockless shared bicycles, disorderly parking has not only generated convenience to users but also negative consequences to enterprises and urban management. We proposed an integrated model and recruited 1722 participants from diverse professions and geographic locations in China, examining the influence of individual and social environmental factors on orderly parking behavior. It was shown that descriptive social norms played an important role in shaping user’s attitudes toward orderly parking directly and indirectly via personal norms, and thus influence the behavioral intention of orderly parking. Cultural tightness-looseness further moderated the effect of descriptive social norms. At the individual level, antecedents of personal norms (moral awareness, awareness of consequences, and ascription of responsibility) were investigated based on Norm Activation Model. This study indicates that descriptive social norms can act as a complimentary policy and regulations of dockless bike-sharing, which provides valuable insights on urban operation and policy making concerning shared bicycles.

Jiaping Wang

Papers

Examining the influence of social norms on orderly parking behavior of dockless bike-sharing users