First Advisor

Jenny H. Liu

Term of Graduation

Spring 2020

Date of Publication


Document Type


Degree Name

Doctor of Philosophy (Ph.D.) in Urban Studies


Urban Studies and Planning




Cycling -- Oregon -- Portland -- Longitudinal studies, Cycling -- Minnesota -- Minneapolis -- Longitudinal studies, Bicycle trails -- Design, Bicycle commuting, Cyclists, Route choice



Physical Description

1 online resource (ix, 130 pages)


Bicycling is a promising approach to improve health, environment, and economic development of urban places. Theoretically, a bicycle network's component goes beyond lanes and paths, and would generate greater impacts than the sum of its parts. However, most previous research focused on how individual types of bicycle-related infrastructure could promote bicycling. Few empirical studies investigated how bicycle networks impact bicycling activity. This project attempts to address this question. Specifically, how to properly measure bicycle networks, and what impacts bicycle networks have on bicycling activity, e.g. bike ridership and bike mode choice, across different cities and longitudinally.

To address the first question, I constructed two types of bicycle network measures -- the regional level measures and the route level measures -- based on the definition of Level of Traffic Stress from Mekuria et al. (2012). Then I adjusted these measures to better account for the bike networks in the two US case cities, Portland, OR and Minneapolis, MN. To address the second question, I first used regression approaches to examine the correlational relationship between bicycle networks and bicycling ridership in both case cities. Then, I studied the causal relationship between bicycle networks and bike ridership using the Difference-In-Difference (DID) approach. Finally, I evaluated the robustness of the relationship between bike networks and bicycling activity using a different output measure, bike mode choice, and a different dataset.

The results suggested the bicycle network measures that incorporated the morphology, connectivity and comfort characteristics provided a more complete view of the network property. The low stress bicycle network was associated with high bicycle ridership and high probability of choosing bikes among other travel modes. In addition, the results also indicated that improvements in bicycle networks would disproportionally benefit disadvantaged populations, such as female and low-income groups, more by increasing their possibilities of riding bikes. However, no causal relationship could be inferred between bike networks and bicycling ridership, which is potentially explained by some limitations of applying DID approach to my datasets. Future research is needed to further explore the causal relationship between bicycle networks and bicycling activity using other approaches.


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