An AI and equity driven framework for mobile photo enforcement deployment

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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.