| Abstract |
Cycling plays an important role in improving mobility, health, and sustainability; however, designing
attractive and usable bicycle infrastructure requires a clear understanding of users’ preferences and
perceptions. Achieving this understanding often demands substantial financial resources, robust
methodologies, and considerable research effort. Existing approaches—including surveys, naturalistic
observations, and field experiments—provide valuable insights but are frequently constrained by
hypothetical bias, high costs, long implementation timelines, safety risks, limited applicability to unbuilt
infrastructure, and reduced replicability.
Virtual reality (VR) has emerged as a promising methodological tool for addressing these limitations by
offering a cost-effective and scalable means of evaluating cyclists’ perceptions of existing and proposed
bicycle infrastructure within controlled yet immersive environments. VR-based approaches support early-
stage assessment of design alternatives while enabling the collection of behavioral, perceptual, and
physiological responses without exposing participants to real-world risk. Importantly, VR environments
allow diverse user groups (e.g., children, less-experienced cyclists) to safely test and experience
infrastructure designs, supporting more inclusive, capabilities-centred planning processes.
This presentation will showcase an inclusive, immersive, and replicable VR-based bicycling simulator for
infrastructure planning and behavioral perception analysis. The proposed framework outlines the
development of a high-fidelity virtual cycling laboratory using commercially available bicycle trainers,
sensors, and the Unity game engine to replicate real-world cycling contexts. The methodology enables the
integration of physiological health data (e.g., eye-tracking, heart rate, and physical movement measures
such as leaning, braking, and turning). These data support a detailed examination of how different
infrastructure configurations influence real-time health indicators, along with perceived safety, comfort,
and usability.
The intellectual contribution of this work lies in advancing realism-driven methods for active
transportation research through the systematic integration of behavioral, physiological, and perceptual
data within a human-centred simulation framework. Practically, the proposed approach offers planners,
engineers, and policymakers a scalable and cost-effective tool to evaluate bicycle infrastructure designs
before implementation. Beyond the case study context, the framework can be adapted by other
municipalities and communities to support inclusive, evidence-based, and community-informed cycling
infrastructure planning, contributing to more equitable, healthy, and sustainable transportation systems.
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