An AI and equity driven framework for mobile photo enforcement deployment

用于移动照片执法部署的人工智能和股权驱动框架

• 批准号: 562466-2021
• 负责人: ElBasyouny, KarimK
• 金额: $ 2.07万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=748930
• 关键词: AI; equity; driven; framework; mobile

Mobile automated speed enforcement (ASE) programs have been in operation in many jurisdictions around the world, including in Canada, for over a decade. Although there is much in the published literature in assessing the traffic safety results of such programs, there has been very little in terms of mapping program inputs to the achievement of these outcomes. In other words, the decisions that are made with respect to program operations (i.e., site selection, operator scheduling) and how they connect to resulting traffic safety outcomes has not been explored extensively. Given the recently renewed interest in ASE technology by jurisdictions throughout Canada, it has become critical to pulling attention back to how such programs are designed and operated, and how decisions are made. However, the recent attention to biases in policing reveals that it is also important to investigate the outcomes of such community-based traffic law enforcement programs as ASE, particularly whether site selection and site enforcement outcomes exhibit biases with respect to gender, race, and other socio-demographic characteristics within urban areas. To address this challenge, this research project will develop a systematic yet transferrable approach to deploy ASE resources, investigating and explicitly considering equity and efficiency, using Big Data and machine learning techniques. The expected outcomes of this research are to improve the efficiency of ASE deployment in a dense urban environment, by facilitating the development of GIS-enabled interpolation and visualization tools that would i) prioritize equity issues, ii) optimize the efficiency of ASE deployment under limited resources, and iii) increase compliance to speed limits, which will ultimately improve safety. Using knowledge gained and methods developed from this research, ASE operators will be able to support their deployment with a visualization and deployment tool, which will assist them in making more informed decisions on the use of existing ASE to improve traffic safety while also considering urban social equity implications. This research will generate a significant body of new knowledge to advance policing equity and ASE decision support tools that will benefit all Canadians.

移动的自动速度执法（ASE）程序已在世界各地的许多司法管辖区（包括加拿大）运行了十多年。虽然有很多在已发表的文献中，在评估交通安全的结果，这些程序，很少有在映射程序的投入，以实现这些成果。换句话说，关于程序操作所做出的决定（即，站点选择、运营商调度）以及它们如何与所产生的交通安全结果相关联尚未被广泛探索。鉴于最近加拿大各地的司法管辖区对ASE技术重新产生了兴趣，因此将注意力拉回到如何设计和操作这些程序以及如何做出决策变得至关重要。然而，最近的关注偏见的警务显示，它也是重要的调查结果，以社区为基础的交通执法计划ASE，特别是网站的选择和网站的执法结果是否表现出偏见的性别，种族和其他社会人口特征在城市地区。为了应对这一挑战，该研究项目将开发一种系统而可转移的方法来部署ASE资源，使用大数据和机器学习技术，调查并明确考虑公平和效率。本研究的预期成果是提高ASE部署在密集的城市环境中的效率，通过促进GIS启用的插值和可视化工具的开发，i）优先考虑公平问题，ii）在有限的资源下优化ASE部署的效率，iii）提高对速度限制的遵守，这将最终提高安全性。利用从这项研究中获得的知识和开发的方法，ASE运营商将能够通过可视化和部署工具支持他们的部署，这将有助于他们在使用现有ASE改善交通安全方面做出更明智的决策，同时考虑城市社会公平的影响。这项研究将产生大量新知识，以促进警务公平和ASE决策支持工具，使所有加拿大人受益。