Literature Review on Behavioral Analysis of Bike Use and Decision Support Methods for Soft Mobility Infrastructure and Service Development

This literature review synthesizes empirical evidence on bicycle use behavior and decision-support methods for soft mobility infrastructure and service development. The reviewed studies show that cycling behavior is strongly influenced by safety perceptions, network continuity, traffic stress, built-environment characteristics, and demographic and socioeconomic factors. Cyclists frequently favor safer and more connected routes even when these involve longer travel distances, while cycling demand exhibits substantial spatial and temporal heterogeneity across demographic groups, land-use contexts, and travel purposes. The review further finds that machine-learning and spatiotemporal models provide effective capabilities for capturing complex demand patterns and incorporating contextual factors such as weather and events, whereas GIS-based multi-criteria decision analysis, geographically weighted approaches, and other spatial decision-support methods facilitate infrastructure prioritization and locally adapted planning. Evidence also indicates that user-centered and equity-oriented service design is important for addressing differences in accessibility, safety perceptions, and service preferences. The original contribution of this review is an integrated analytical perspective that links cyclist behavioral evidence with demand prediction, spatial decision support, infrastructure planning, and service design, thereby connecting research domains that are often examined separately. This synthesis provides a basis for evidence-based, context-sensitive, and inclusive planning of cycling infrastructure and soft mobility services.