Advancing knowledge of the safety implications of transportation engineering decisions with surrogate safety data and innovative statistical techniques

利用替代安全数据和创新统计技术增进对交通工程决策的安全影响的了解

• 批准号: RGPIN-2017-05288
• 负责人: Sacchi, Emanuele
• 金额: $ 1.89万
• 依托单位: University of Saskatchewan
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636293
• 关键词: Advancing; knowledge; safety; implications; transportation

The way transportation engineers study and evaluate road and traffic safety has remarkably advanced in recent years. Engineers can now employ a systematic and rigorous process to analyze safety at road sites that identifies opportunities to reduce crashes and implement potential safety countermeasures. This process mainly relies on a set of statistical techniques and models developed over several years of research. In turn, these models rely on crash observations as the main data source to quantify safety levels. However, the use of crash data in safety models has several limitations, mainly related to quality and availability issues. Moreover, the use of crash observations is reactive: a substantial number of crashes must occur before any action is taken. These limitations also inhibit application of new opportunities offered by methodological advancements in the field.

近年来，交通工程师研究和评估道路和交通安全的方式有了显着的进步。工程师现在可以采用系统和严格的流程来分析道路现场的安全性，以确定减少碰撞的机会并实施潜在的安全对策。这一过程主要依赖于一套经过多年研究开发的统计技术和模型。反过来，这些模型依赖于碰撞观察作为量化安全水平的主要数据来源。然而，在安全模型中使用碰撞数据有几个限制，主要涉及质量和可用性问题。此外，使用碰撞观察是反应性的：在采取任何行动之前，必须发生大量的碰撞。这些局限性也阻碍了利用该领域方法进步所提供的新机会。