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Abstract:This study examines determinants of bicycle volume in the built environment with a five-year bicycle count dataset from Seattle, Washington. A generalized linear mixed model (GLMM) is used to capture the bicycle volume change over time while controlling for temporal autocorrelations. The GLMM assumes that bicycle count follows a Poisson distribution. The model results show that (1) the variables of non-winter seasons, peak hours, and weekends are positively associated with the increase of bicycle counts over time; (2) bicycle counts are fewer in steep areas; (3) bicycle counts are greater in zones with more mixed land use, a higher percentage of water bodies, or a greater percentage of workplaces; (4) the increment of bicycle infrastructure is positively associated with the increase of bicycle volume; and (5) bicycling is more popular in neighborhoods with a greater percentage of whites and younger adults. It concludes that areas with a smaller slope variation, a higher employment density, and a shorter distance to water bodies encourage bicycling. This conclusion suggests that to best boost bicycling, decision-makers should consider building more bicycle facilities in flat areas and integrating the facilities with employment densification and open-space creation and planning.

Although developed only in the past 20 years, Chinese high-speed rail (HSR) has overtaken many of its forerunners in its unprecedented scale. However, such a scale raises questions about its implications for regional economic development. Previous studies have discussed the impact of HSR at the regional and city levels, but few have addressed its impact on the individual level, which is crucial for understanding the distribution of the impact. To fill the gap, this study focused on the economic impact of recent HSR development between 2009 and 2012 on Chinese household income and discussed its significance, magnitude, and distribution. The survey data from the China Family Panel Survey were used and a difference-in-differences approach was implemented. Two key explanatory variables, weighted average travel time and probability of living proximate to HSR stations, were included in the models to examine the direct and spillover impacts of HSR. The study shows that these impacts both contribute to the HSR impact but affect urban and rural regions and production sectors differently. In particular, the spillover effect or the agglomeration effect contributes the most and favors more urbanized regions with stronger service sectors. As a consequence, although HSR plays a positive role in stimulating the regional economy, it may further widen the gap between developed regions and underdeveloped regions. From the analyses, it is concluded that HSR projects need more comprehensive studies of the full spectrum of its impact to ensure both economic growth and regional balance and coordination.

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