A methodology for urban bike network design using floating car data: evidence from a medium-size city

Comi, Antonio; Polimeni, Antonio · 2026 · Crossref

DOI: 10.1080/19427867.2025.2612627

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Summary

This paper presents a methodology for designing urban bicycle networks by leveraging Floating Car Data (FCD) and stated preference surveys to identify potential demand shifts from private cars to cycling. The research addresses the challenge of incomplete and unsafe bike networks in European cities, which often force cyclists into mixed traffic and discourage modal shift. By focusing on a medium-size city, the study aims to provide a scalable approach that prioritizes the creation of safe, dedicated lanes while maximizing connectivity for trips currently undertaken by car. The methodology involves two parallel stages: supply analysis and demand estimation. Supply analysis identifies the existing bike network and potential links where dedicated lanes can be added without compromising vehicle flow. Demand estimation utilizes FCD from 1,912 cars over five working days, comprising approximately 24,000 trips, to construct an origin-destination (O/D) matrix. This data is filtered to identify systematic, home-based trips by residents, expanded to the total vehicle population, and adjusted based on survey results indicating that 77.5% of users would cycle for systematic trips if protected lanes were available. A distance threshold of 5 km is applied, as surveys showed over 74% of respondents are willing to cycle for trips under this length. The network design algorithm iteratively adds links from the potential supply to the current network to connect O/D pairs, prioritizing shortest paths within the potential network to maximize overlap with observed car routes. The approach was validated in Rovigo, Italy, a city of approximately 50,000 inhabitants divided into 44 traffic zones. The existing bike network was 24.1 km long and discontinuous. The study combined FCD analysis with a survey of 107 residents to refine demand estimates. The results demonstrated that the proposed methodology successfully extended the bike network by approximately 28% in length. The analysis revealed that users are most willing to cycle for trips between 3 and 5 km (51.3%) and under 2 km (23.1%), with willingness dropping significantly for longer distances. The resulting network design connected previously isolated segments, creating a more cohesive infrastructure that aligns with actual travel patterns. The significance of this work lies in its demonstration that FCD and behavioral data can effectively support sustainable mobility planning without relying on complex traditional modeling frameworks. By focusing on the shift of car trips to bikes, the methodology provides a practical tool for urban planners to prioritize infrastructure investments that maximize safety and utility. The study confirms that leveraging existing data sources can reduce investigation costs while delivering actionable insights for enhancing cycling infrastructure in medium-sized cities, contributing to broader goals of reducing sedentary habits and improving urban livability.

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