Decision support traffic model for assessing transportation safety performance

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This research will develop a behavioural microscopic model to identify potentially unsafe vehicle interactions in the traffic stream for varying road geometric conditions. The model is based on a number of fundamental traffic protocols, including car-following, lane change, gap acceptance and over-taking. Safety performance is expressed over time as a function of pair-wise vehicle speed and spacing differentials, vehicle deceleration profiles and braking capabilities. A major aspect of the proposed research concerns the calibration of traffic model input parameters, based on observable real-world vehicle tracking data. The process involves: 1) identifying parameters that individually or in combination have a statistically significant effect on safety performance, and 2) using a multi-criteria fitting function to establish "best estimate" values for these parameters. A comprehensive calibration of model parameters provides a sound objective basis for simulating safety performance. This proposed research will assess the ability of the simulated safety performance measures to capture observational crash occurrence in real-time (i.e. for a time interval preceding each observed crash). A number of statistical tests will be applied to establish the ability of safety performance measures in identifying high risk driver-vehicle behaviour in the traffic stream. This high risk behaviour is expected to result in crashes for different road geometric and traffic scenarios. Research carried out thus far suggests that while behavoural microscopic traffic models may be unable to predict crashes they can replicate or identify high risk driving situations in the traffic stream. The usefulness of the microscopic simulation model is investigated with respect to two fundamental questions of safety: Which locations are inherently unsafe? and Which countermeasures have the highest potential for improving safety at these locations.