Traffic jams are organised chaos

It won’t seem like it to those of us stuck in holiday gridlock this summer, but there is a science to traffic jams.

Scientists have used statistical physics to hunt for discernable patterns in six cities, the nearest being Melbourne, in a study that seems to have pinpointed the moment at which traffic backs up.

The researchers compared individual drivers’ travel times and how long it took them to reach their destinations from the beginning of peak hour, with drivers starting their journeys over the next hour of that peak period.

The time it took drivers to reach their destinations was labelled recovery time.

Team member Dr Meead Saberi, of the University of New South Wales, said as recovery time passed the critical threshold where cars using the network outweighed the network’s full capacity, traffic started to move beyond congestion to network collapse, or gridlock.

“We have found that this simple recovery time measure is directly related to demand and supply — no surprise,” Saberi said.

“What is surprising is that all the cities that we have studied perform similarly.”

In other words, despite the differences between cities in topography, population size, infrastructure, demand and other characteristics particular to each city transition to gridlock happened in every city in a similar fashion.

The point when this transition happens might be unique to each city, but the researchers now have a quantifiable measure for it.

“The demand over supply ratio that we have measured is the ratio of the vehicle kilometres travelled in a city to the total vehicle distance the road network can support per hour,” Saberi said.

“When this ratio exceeds a critical value, we see transition to gridlock. For example, a major global city like London may have a smaller critical value and that’s why it sees gridlock more often than, say, a smaller city like Adelaide.”

Transport authorities and governments could use the findings to understand when and how traffic forms and how likely it could develop into network collapse.

Monitoring the number of vehicles entering the network and the recovery time could provide an early indication of whether a gridlock is likely to happen or not. “This information can be used to intervene in the network by managing travel demand or increasing transport supply when and where needed.”