Crash course
Page 2

...traffic equations. By analyzing traffic and putting its variables into equations and graphs, scientists hoped to quantify traffic flow to predict the point when traffic turns viscous and then to stop-and-go. The first traffic mathematics were published in the late 1930s, about 30 years after the first Ford Model T was produced. The topic became urgent with the traffic jams of the late 1950s and only became more so as traffic became an increasingly annoying part of everyday life.

Most of these models -- and the ones that followed in the digital era -- saw traffic as a flowing liquid. A traffic jam occurs when more traffic can get into an area than can leave it, like a hose filling a bucket with only a small drain-hole in the bottom. Yet those models can’t predict the waves of stop-and-go motion that ripple through a real highway. So while they may get the average speed of the cars correct, they’ll be wrong about factors like driving behavior and acceleration. A step in the right direction came in the 1950’s, when experts at General Motors did race track testing to quantify braking and acceleration behaviors. But they still didn’t count for individual decisions.

Ben-Akiva’s view was that the models needed more room for individuality. So he and his colleagues created computer programs to imitate individual drivers. Every one of the thousands of drivers on the virtual roads in the DynaMIT model has a home, a destination , and a route in mind. They also have pre-determined “driving behaviors,” such as reaction time, a tendency to jump out into a faster lane; to speed or dawdle; to ignore traffic signs; or to bypass a waiting line of cars and try to muscle back in. “Drivers are strategic,” Ben-Akiva says, “They anticipate turns, they see stoppages and react. But they don’t all anticipate [equally], so you need to model human differences.”

To model lane changes, for example, the engineers digitized video footage showing vehicles merging. They tagged each car that traveled through the frame, then had a computer vision program track the cars through later frames. Creating tracks through space and time for each vehicle meant they could observe the details of how drivers change lanes. As the program puts itself into each driver’s place, one after another. By making such simple decisions based on different driver profiles for thousands of drivers each second, the model predicts how a whole highway will behave.