Predicting Highway Crashes
The Policía de Tránsito (traffic police) in Costa Rica could soon have a new tool in preventing traffic accidents thanks to a computer modeling software program developed by researchers at Ohio State University that identifies the probability of traffic accidents at certain times and locations.

The program--the first of its kind in the United States and is based on historical crash data. It uses existing statistical and mapping software to create a color-coded geographical display of the accident-risk levels on segments of roadway throughout the state.

"The model is saying, 'This area has a higher risk than this area at this specific time of having this specific type of crash,' which lets us predict where and when there are going to be higher risks of crashes," says Christopher Holloman, the project leader and the associate director of the Statistical Consulting Service in Ohio State's Department of Statistics.

Currently, the model is being used by the Ohio State Highway Patrol to monitor roadways and position troopers.

The same program could be applied in Costa Rica and in attempt by the Tránsito officials to reduce the carnage on our roads.

The predictive crash model was initially developed as a tool to help the Ohio State Highway Patrol better prevent accidents and explore the reasons some roads are riskier than others. Scientists have taken the historical crash data collected over the past five years by the highway patrol, which tracks details of accidents--including time, location, weather conditions, and whether alcohol or speeding was involved--and analyzed the data for roadway trends using statistical analysis software from SAS, the Cary, NC-based software giant.

The software provides an output of the numerical risk levels for every piece of roadway. So on a particular day, one could look and learn which roadways have the highest risk of, for instance, alcohol-related crashes.

What makes this model novel is that scientists have now combined the statistical software with Google Earth -a program that offers an interactive map of the entire globe - to map the results as color-coded lines.

Google Earth is able to perform this function because it reads the output from the statistical model in KML files; much as a Web browser reads HTML files, the KML files tell the program where on the planet to draw lines or place images, explains Holloman.

"We have done reports on individual places, on a specific weekend, to look at where the most dangerous spots are for people to watch out for," he says. "We can make predictions for every major roadway in Ohio, under all possible road conditions, for every hour of the day, for every day of the week."

"The main use for this type of technology, which is pretty straightforward, would be in the public sector: working with government and state departments of transportation to provide them that information so they could make modifications, whether it is designs of the road or different signage, to protect drivers," says Bryan Mistele, the founder and CEO of Inrix, a startup based in Kirkland, WA, that provides real-time and predictive traffic information.

Holloman's group is continuing to work on the model to include more types of data and trends, such as where the police have been stationed and if low crash rates are related to the proximity of the highway patrol. The group also plans to study the reasons a certain roadway is more prone to alcohol-related crashes or having drivers who speed.

The best past of the program is that it can be used and adapted anywhere, based on its own statistical data.

So far there has been no word from the Ministerio de Obras Pública y Tránsito (MOPT) on the possibility of purchasing the program and adopting it for use in Costa Rica.