In this project, we propose an Automated Vehicle System Impacts Evaluation Module, AVSIEM, for both transportation and traffic operation analysis using a two-step multi-resolution modeling approach.  The approach hinges on the development of a Capacity Adjustment Factor (CAF) for automated vehicles, similar to the heavy vehicles adjustment factor used in highway capacity analysis.  CAF will be linked to input variables such as roadway facility types, traffic demand levels, and market penetration rates of automated vehicles.  CAF will be derived from a microsimulation environment, which involves the development of an integrated car-following model for both human drivers and automated vehicles, model calibration using vehicle trajectory data from real-world, and implementation of the model as a plugin in microsimulation.  The derived CAF will then be introduced into a macroscopic regional network for adjusting capacity and the impacts of automated vehicles can be analyzed through additional traffic assignment runs.

Pedestrians and bicyclists are vulnerable in vehicle crashes. To improve the safety of pedestrians and bicyclists at intersection crossing, in this project, we develop a traffic signal alert system that will integrate sensory information commonly available on a Smartphone, geographical information of an intersection, and real-time traffic signal information to alert or support decision making for pedestrians and cyclists. The system consists of a Smartphone application and a real-time traffic signal information service. The Smartphone application uses GPS, accelerometer and digital compass together with external digital maps to determine user location and orientation. The application will also obtain real-time traffic signal phasing and timing from local signal controller or Traffic Management Center (TMC). Signal information or alert can be provided to users through configurable user interfaces (e.g., visual, speech, or vibrotactile) depending on user’s need or preference.