使用成对图像比较评估骑行环境的感知安全性
Scoring Cycling Environments Perceived Safety using Pairwise Image Comparisons
AI总结：
本研究提出一种基于真实图像成对比较的新方法，用于评估和分类骑行环境的感知安全性，以辅助城市规划者优化干预措施并促进骑行推广。
摘要：
如今，许多城市寻求向更可持续的交通系统转型。骑行在这种转型中对短途出行至关重要，包括与公共交通的首末段接驳。然而，如果人们认为骑行不安全，他们就不会骑行，而会选择其他交通方式。本研究提出了一种新方法，用于识别如何分析和理解骑行安全感知，以及建成环境和骑行背景对这种感知的影响。我们的工作基于其他感知研究和成对比较，使用真实世界图像对受访者进行调查。我们反复向受访者展示两种道路环境，并要求他们选择他们认为骑行更安全的一个。我们比较了几种能够根据成对比较对骑行环境进行评分的方法，并将骑行环境分类为感知安全或感知不安全。城市规划可以使用该评分来提高干预措施的有效性，并改善骑行推广活动。此外，这种方法便于对不断变化的骑行环境进行持续评估，允许对措施进行短期评价，并能高效部署在不同地点或环境中。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
