Page 8 - CEGE Magazine - Fall 2021

GARY DAVIS,
Transportation Engineer
FACULTYRESEARCH
GARY DAVIS HAS LONG BEEN WORKING TO KEEP YOU SAFE AS YOU DRIVE. NOW, DAVIS IS DEVELOPING WAYS TO KEEP YOU SAFE IN THE PRESENCE OF AUTOMATED VEHICLES.
Gary Davis has a long-term interest data and expert judgement must be populations and behaviors collected
in traffic safety. He researches the causes of vehicle crashes and how that information impacts roadway design— causal inference and decision support in highway safety. He helps engineers factor in safety as well as travel times and the endurance of materials when they design a roadway. Safety planning techniques are not as well developed as designing with speed and materials in mind. Davis has been researching how to determine which design options do (or might) bring about a change in the rate or severity of accidents, and then, how engineers can use that data to support safety-based design decisions.
A second focus of Davis’s research has been applying Bayesian statistical meth- ods to solve some previously intractable estimation problems in transportation engineering. Some problems are hard to set up and solve with frequentist meth- ods, but they can be broached more easily when using a Bayesian approach. So, Davis has been recasting some of those difficult problems, like quantifying uncertainty in crash reconstruction.
This problem is difficult because often
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combined. A Bayesian approach allows an engineer to combine the insufficient data with judgement, leading to more informative solutions.
AVs
A newer focus for Davis and his research group is how to account for partially or fully automated vehicles (AVs) and other new mobility modes. It is widely expected that the presence of AVs will change traffic conditions and driver behaviors, but exactly how is
not clear. One key concern for Davis is how AVs will or should change the tools and processes by which transportation engineers implement safety planning techniques in their designs.
A resource that transportation engineers refer to when making design decisions is the Highway Safety Manual (HSM). It helps engineers predict safety conse- quences (changes in crash frequencies or severity) of various roadway design decisions. The crash modification factors used in the HSM are based on
a statistical summary of driver/vehicle
over the last 20–30 years, years when there were no AVs on the roads.
It is no small matter to apply one set
of assumptions to a new situation or population. Yet, Davis and his research group have made significant progress on ways to leverage the existing re- search and statistical summaries in the era of AVs.
A New Theoretical Model
A key breakthrough for Davis came when he began thinking about these issues as akin to medical research methodologies, deconstructing the mechanisms of crashes and thinking of the two pools of drivers, humans and AVs, as populations for evidence-based experiments. (See sidebar.)
Davis and graduate student Jingru Gao developed a theoretical framework for recalibrating crash modification factors from the set of conditions prevalent in the past, with only human drivers, to new conditions involving both human drivers and AVs.