Improving Traffic Safety Risk Analysis Through Big Data

How do we know whether a section of the roadway is at high safety risk?

Historically, the answer has been relatively straightforward: we count the number of crashes at that location over a period of time and compare it to other roadway sections. If the roadway section of our interest contains a high number of crashes, we then decide what can be done, if anything, to lower the number of crashes.

This method has been the go-to method for safety evaluation for decades, and with good reason. The number of crashes (or other crash-based safety measures) at a roadway section is a fairly straightforward piece of evidence regarding its relative safety risk level.

There’s only one problem with it, which is that in order to obtain this data, we have to wait for crashes to happen.

But, thanks to big data, there is a different way to do things.

C2SMART Participates in NYU Research Exhibition

On Friday, April 29, NYU Tandon held its annual Research Excellence Exhibit, a public event that features exhibits that illustrate the scope of engineering and the applied sciences — and their potential for improving the world. The day-long program provides faculty and students with a platform to showcase the high-caliber, innovative work they’re doing. This year, four projects were selected to represent C2SMART at the Exhibit.