Using Origin–Destination Centrality to Estimate Directional Bicycle Volumes

Abstract

A new method estimates directional bicycle volumes throughout a street network. The method is based on a modified form of centrality, a measure from graph theory used to quantify the relative importance of each link and node in a network. One common formulation of centrality calculates the number of times a link in a network is used along the path of all shortest paths between all nodes. The equation was modified to represent bicycle travel better. The new metric is called origin–destination (O-D) centrality. For this case study, the new metric exhibited high correlation (R 2 = .45 to .73) with observed bicycle counts at intersections. Use of O-D centrality to interpolate field observations spatially is demonstrated. Unlike other bicycle demand estimation methods, this approach requires commonly available data, is easy to use, and produces directional volumes (some methods only estimate nondirectional aggregate counts). The new method was programmed as a tool for geographic information systems by using modifiable open-source python code. The tool requires a street network, a digital elevation map, parcel data, and observed bicycle counts at select locations throughout the study area. The observed counts can be collected through any manner, but the tool was specifically designed for planners and engineers working with count data collected manually in a manner similar to that used for the National Bicycle and Pedestrian Documentation Project.

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