Using Crowdsourcing to Prioritize Bicycle Route Network Improvements

LaMondia, Jeffrey; Watkins, Kari · 2017 · ROSA P / Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE)

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Summary

This study addresses the challenge of prioritizing bicycle infrastructure improvements by identifying the factors that influence cyclist route choices. As cities increase investment in cycling facilities to reduce congestion and improve livability, planners require data-driven methods to determine where facilities should be placed and what types are most effective. The research aims to model whether specific roadway links are likely to be used for commute cycling and to identify the relative importance of link characteristics, including roadway features, land-use accessibility, and connectivity. The study hypothesizes that links with higher connectivity to the network and destinations are more likely to be chosen, provided they offer safety and comfort. The researchers employed crowdsourced GPS data from smartphone applications—Strava, CycleDixie, and CycleAtlanta—to analyze cycling behavior in both suburban (Auburn, Alabama) and urban (Atlanta, Georgia) contexts. This data was integrated with roadway characteristics from city GIS databases, land-use data, and socio-demographic information from U.S. Census records. The methodology involved three primary analytical steps: modeling cycling facility prioritization preferences, modeling route segment and path choices using ordinal logistic regression, and developing route suitability scores. The study compared traditional Bicycle Level of Service (BLOS) measures with a modified Level of Traffic Stress (LTS) metric, which categorizes roads based on the stress they impose on different cyclist typologies, ranging from "No Way No How" to "Strong and Fearless." Key findings indicate that demographics, roadway characteristics, and surrounding land use significantly impact route selection. Cyclists preferred routes with continuous facilities, low traffic volumes, lower speed limits, and minimal on-street parking, while avoiding roads with steep grades, high travel times, or frequent traffic signals. The analysis revealed that the Level of Traffic Stress measure provided a more nuanced understanding of route suitability than traditional BLOS scores, particularly when mapped against cyclist comfort levels. In the Atlanta case study, the LTS model helped identify gaps in the low-stress network, guiding proposed improvements to connect key destinations like MARTA stations and the Beltline. The models demonstrated that connectivity to various access groups and socio-demographic clusters was a strong predictor of link usage. The significance of this work lies in its provision of a comprehensive framework for data-driven bicycle network planning. By leveraging crowdsourced data, the study offers a scalable method for understanding actual cycling behavior rather than relying solely on stated preferences. The findings support the design of universal facilities that cater to the lowest caliber of bicyclist to maximize usage, aligning with Federal Highway Administration guidelines. The developed route suitability and preference models enable planners to prioritize infrastructure investments that enhance network connectivity and safety, ultimately encouraging broader community adoption of cycling as a sustainable transportation mode.

Key finding

Demographics, roadway characteristics, and surrounding land use had a significant impact on whether a particular street segment would be used by cyclists.

Methodology

dataset

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• cyclist safety