Traffic crashes result from many complex factors; roadway conditions, weather, traffic signals, traffic flow, drivers’ behavior, vehicle health can all significantly influence or contribute to roadway conflicts. Generally, crashes happen when a conflict arises among or between vehicles and/or other road users.

With the enormous advances in connected vehicles technology, the Internet of Things (IOT) and smart cars, the opportunities for more advanced safety techniques that are proactive and customizable to individual drivers are becoming more realizable.

The main objective of this project is to build advanced analytics to estimate a composite traffic safety risk measure that change temporally and spatially, and take into account driver behavior, roadway quality conditions, and historical safety characteristics of roadways.

Our vision is that with smart cars and smart roadways, a travel plan for a given driver will be associated with a safety risk profile composed of these risk estimates that are sampled in time and change whenever one or more of the underlying data streams change. This project will focus more on the development of such a methodology and less on how it should be implemented and calibrated for different applications.