Speed enforcement cameras are used to improve traffic safety. These cameras are usually placed in areas with significant crashes related to speeding. However, in many countries, detailed crash data are not available or are of poor quality. This study aims to propose and explore three simple ranking methods for identifying optimal locations for speed enforcement cameras that do not depend on crash records. The first method is based on the Star Rating Scores and the traffic volume data. The second method relies on developing a data-driven model for predicting speeding violation rate (VR) based on road geometry aspects and comparing it with available road segment crash risk. The third method uses a combination of the first two methods. The proposed methods and prediction models were applied to a case study and successfully identified the optimal location for speed enforcement cameras. The developed prediction models suggest that speed limit, junction spacing, and the number of access points per kilometer are the most common variables to estimate speed VRs for both urban and rural roads. Availability of service roads and the number of lanes were variables that affect the speed VR on urban roads only. The proposed methods are considered valuable simple tools for road safety practitioners to effectively prepare speed enforcement plans for different road networks.
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