Road User Collision Prediction Using Motion Patterns Applied to Surrogate Safety Analysis

Surrogate safety analysis is the process of diagnosing road safety indirectly from measures of ordinary (non-collision) road user behaviour, such as absolute speed and time-to-collision. While absolute speed has enjoyed much popularity in the literature, other measures such as time-to-collision are currently under developed. Before conflict measures such as time-to-collision can be adopted, several challenges need to be overcome, notably the problem of accurately modeling collision courses and collision probability from normal road user behaviour. This paper describes and explores the feasibility of implementing discretized motion pattern maps for the purpose of predicting potential collisions between road users and their measures based on an empirical naturalistic behaviour model calibrated from site-specific data for use in surrogate safety analysis. The methodology is applied to a pre-existing framework which extracts road user trajectory data from video data of a traffic scene, and then predicts and estimates potential collisions. To this end, this paper examines the motion pattern model discretization process, the probabilistic framework, the resulting indicators, and then compares the motion prediction methodology with that of the classical constant velocity motion prediction methodology. The methodology is explored using road user behaviour inside the weaving zone of a roundabout to illustrate the flawed use of constant velocity motion prediction.