The impact of bike network indicators on bike kilometers traveled and bike safety: A network theory approach

Abstract

There has been recent interest in the use of network analysis to quantify bike network features and their impact on biking levels and safety. However, limited bike network indicators have been evaluated. This study introduces a list of network indicators to quantify the bike network and study its effect on bike kilometers traveled and bike–vehicle crashes. Data from the city of Vancouver, Canada, are used as a case study. Full Bayesian modeling incorporating spatial effects is employed to develop Bike Kilometers Travelled (BKT) and bike–vehicle crash models. The developed BKT models show that the bike network centrality, assortativity, and weighted slope have negative associations with BKT, while the bike network directness, length, complexity and development, and connectivity have positive associations with BKT. The developed crash models show that the bike network length, centrality, assortativity, and continuity have negative associations with bike–vehicle crashes. On the other hand, the bike network complexity and development, connectivity, and linearity have positive associations with bike–vehicle crashes. The models provide insights that can be useful for planning bike networks to increase bike traffic and improve bike safety. The models also show that some changes to a bike network to increase bike traffic should be accompanied by crash risk-mitigating measures. As well, the models can be used to identify zones within a city that require safety improvements.

Association between network characteristics and bicycle ridership across a large metropolitan region

“…Consistent with prior research, we demonstrated that the length of the bicycle network was positively associated with various measures of bicycle ridership. 11,12,27 A surprising finding was variation in the direction of association between degree centrality and bicycle ridership. In the innercity region of Inner Melbourne, the finding of centrality being negatively associated with bicycle ridership is logical; high network centrality indicates low inter-connectivity and accessibility of the network.…”

“…To explore characteristics of the network, we calculated seven network metrics that have previous been demonstrated to be associated with bicycle ridership. 9,11,12 These were measures of bicycle network length, network centrality (betweenness and degree centrality), connectivity and coverage (network density, network coverage, and intersection density), and topography (average weighted slope). These are described in detail below.…”

“…Three measures of connectivity and coverage were implemented in this study: network coverage, network density and intersection density. 12,19 Network coverage was calculated as the ratio of the number of bicycle network links to the number of street network links in the SA2. Network density was calculated as the ratio of the total length of bicycle network links in the SA2 to the SA2 area.…”

“…Recent developments in measuring bicycle network connection include the use of indicators developed using graph theory, such as density, directness and centrality. 9,11,12 Such approaches offer systematic methods for measuring network quality for comparison within and between cities. For example, Schoner and Levinson demonstrated that connectivity and directness were important factors in predicting bicycle commuting, 9 Osama et al suggested more connected, dense, flat, continuous and off-street bicycle networks yield higher bicycling, 11 and Kamel and Sayed showed that network centrality, assortativity, weighted slope, directness, length, complexity and connectivity were associated with bicycle ridership.…”

“…For example, Schoner and Levinson demonstrated that connectivity and directness were important factors in predicting bicycle commuting, 9 Osama et al suggested more connected, dense, flat, continuous and off-street bicycle networks yield higher bicycling, 11 and Kamel and Sayed showed that network centrality, assortativity, weighted slope, directness, length, complexity and connectivity were associated with bicycle ridership. 12 While these studies have been important in advancing knowledge on the role of various network characteristics, they have commonly been conducted in inner metropolitan regions, such as inner Vancouver 11,12 or inner Seattle, 8 or have used single city-wide network measures to contrast these factors between cities. 9 As such, there is limited knowledge on how various characteristics of bicycle networks relate to bicycle trips within and across entire metropolitan regions, and how the size and composition of study regions impact on the association between bicycle network characteristics and bicycle ridership.…”