Scoring Cycling Environments Perceived Safety using Pairwise Image Comparisons

CoRR(2023)

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摘要
Today, many cities seek to transition to more sustainable transportation systems. Cycling is critical in this transition for shorter trips, including first-and-last-mile links to transit. Yet, if individuals perceive cycling as unsafe, they will not cycle and choose other transportation modes. This study presents a novel approach to identifying how the perception of cycling safety can be analyzed and understood and the impact of the built environment and cycling contexts on such perceptions. We base our work on other perception studies and pairwise comparisons, using real-world images to survey respondents. We repeatedly show respondents two road environments and ask them to select the one they perceive as safer for cycling. We compare several methods capable of rating cycling environments from pairwise comparisons and classify cycling environments perceived as safe or unsafe. Urban planning can use this score to improve interventions' effectiveness and improve cycling promotion campaigns. Furthermore, this approach facilitates the continuous assessment of changing cycling environments, allows for a short-term evaluation of measures, and is efficiently deployed in different locations or contexts.
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关键词
Pairwise Comparisons,Perceptions Of Safety,Road Environment,Cycling Safety,Accuracy Of Model,Hyperparameters,Risk Perception,Binary Classification,Item Scores,Gaussian Process,Convex Optimization,XGBoost,Cycle Profile,Urban Quality,Risk Of Accidents,Latent Scores,Bike Lanes,Log Loss,Thurstone,Street View Images
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