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Non-motorized travel in terms of walking or bicycling plays a critical role in promoting healthy living style, offering sustainable alternatives to environmental impacts, energy consumption, and societal costs of motorized travel modes, and allocating limited resources for constructing pedestrian-orientated transportation infrastructure facilities. Historically, much research has been focused on the nexus between ambient built-environment attributes and travel mode choices or shares or non-motorized travel prediction. For a better understanding of non-motorized travels at multimodal facilities, spatial dependency should be considered since traffic volume at one monitoring station is correlated with that at neighboring sites due to the continuity in area-wide traffic circulation. However, few studies have been conducted in spatial analysis of walking and bicycling traffic at intersections. Utilizing a 10-year assembly of non-motorized traffic counts and a geographical information system (GIS) which contains intersection-based location data and functional classifications of habitable infrastructure in Chittenden County of Vermont, this study determined whether spatial autocorrelation exists for non-motorized volumes and ambient built-environment attributes, and geographically weighted regression (GWR) was applied on two different data-collection scales to identify whether spatially varying relationships operate significantly between non-motorized volumes and specific surrounding characteristics on each scale. Some variables are found significant in spatially influencing non-automobile travel. The resultant models can estimate walking and bicycling volumes at countywide intersections. Better estimation of non-motorized travel locally facilitates transportation planning, facility design, safety enhancement, and operational analysis.
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