Build It. But Where? The Use of Geographic Information Systems in Identifying Locations for New Cycling Infrastructure

Larsen, Jacob; Patterson, Zachary; El-Geneidy, Ahmed · 2012 · OpenAlex-citations

DOI: 10.1080/15568318.2011.631098

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Summary

This paper addresses the lack of systematic methods for prioritizing and locating new cycling infrastructure, a gap identified despite growing interest in utilitarian cycling and urban congestion mitigation. Using Montreal, Canada, as a case study, the authors propose a Geographic Information Systems (GIS)-based, grid-cell model to identify high-priority areas for bicycle facility investments. The goal is to provide transportation planners with a flexible, interpretable tool that integrates multiple data sources to determine where new infrastructure would yield the maximum benefit for existing and potential cyclists. The methodology employs a four-step process: identifying pertinent indicators, spatially aggregating them into a grid, calculating a prioritization index, and visualizing results. Five indicators were selected to address objectives of improving access, safety, and network connectivity: (1) observed cycling trips from the Montreal Origin-Destination (OD) survey; (2) potential cycling trips, defined as car trips shorter than the 75th percentile of cycling trip lengths (approx. 2km); (3) priority segments identified by nearly 3,000 cyclists in an online survey; (4) bicycle-vehicle collision locations from insurance records (2003–2008); and (5) "dangling nodes," or discontinuities in the existing network. Data were aggregated into 300-meter grid cells to mitigate biases in route assignment algorithms. A prioritization index was calculated by summing the percentage contributions of observed trips, potential trips, collisions, and survey-identified priorities for each cell, with equal weighting applied to demonstrate the technique. The results reveal distinct spatial patterns: observed cycling is concentrated in downtown and inner suburbs, while potential car trips are widespread. Collision data show a dispersed distribution across the island, contrasting with concentrated usage, suggesting safety issues in peripheral areas. The combined prioritization index identified specific high-priority zones, such as north-south corridors between the Rivière-des-Prairies and the St. Lawrence River, where infrastructure is currently lacking or disconnected. The study further demonstrates how indicator combinations can infer specific interventions; for example, high-priority corridors without existing facilities suggest new construction, while high-priority areas with existing facilities and crash concentrations suggest upgrades or field studies. The significance of this work lies in providing a structured, data-driven approach to bicycle planning that complements broader planning processes. By visualizing the mismatch between existing infrastructure and high-priority demand/safety zones, the tool helps planners optimize limited resources. The authors conclude that while the model offers objective prioritization, it should be used alongside professional judgment and broader policy objectives, particularly when addressing network connectivity and specific facility designs. This framework offers a replicable method for cities seeking to expand cycling networks systematically.

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• cyclist safety