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Breaking the Bottleneck
If you’ve ever been stuck in the left lane behind a vehicle going 40 mph
in a 55 mph speed zone, Associate Professor of Civil Engineering Lu
Sun feels your frustration — or at least he plans to, with the help of his
model and computer-simulation of traffic flow.
Sun hopes to get to the heart of traffic congestion by better understanding
an often-overlooked but crucial ingredient in traffic patterns: individual
driver behavior. The National Science Foundation awarded Sun a $750,000
grant to lead a collaborative study of driver behavior.
Historically, says Sun, traffic managers have taken a macroscopic
approach to monitoring traffic patterns, looking at broad indicators such as
average speed density and flow rate on highways.“In previous approaches,
the driver is more or less ignored,” Sun says. “But in reality, the vehicle
reflects the driver.” And so Sun is emphasizing human behavior and individual
thinking in his approach to studying larger traffic realities. And in doing
so, his team has narrowed driving activities down to four key behaviors that
greatly impact traffic: “lead vehicle behavior,” or the way the first car in a
pack accelerates from a stop light or on a highway; “car following behavior,”
which includes how quickly others follow the lead car and the relative
distance they keep between themselves and a lead car; “lane-changing
behavior,” or the likelihood of non-lead cars to switch from one lane to
another; and “panic behavior,” which accounts for the decision-making
process of drivers in emergency, panic situations, such as an accident.
“There is a very close connection between the macro and micro
approach to studying traffic,” Sun notes, though he offers a very important
reason for working bottom up: “If we start from the micro, we can describe
the macroscopic results; if we start from macro, we can’t describe an individual
car’s behavior.”
Not that Sun hasn’t looked at the big picture. His team used video cameras
to record traffic flow data on some of Washington’s most notoriously
packed highways, including Route 66 and Interstate 270. Sun’s hope in
integrating the four behaviors key into one model is to develop intelligent
agent-based computer simulation software that reflects the different driving
behaviors and realities that exist on many highways, and then insert various
scenarios to see how traffic changes. What happens to traffic flow if the
speed limit is reduced by 5 mph? What if the limit is increased? How does
the traffic change when a lane is closed for repair? Mimicking real life transit
scenarios, Sun says, will enable highway officials to understand the consequences
of possible readjustments, for better or worse.
But solving chronic traffic tie-ups isn’t as simple as adding another lane
to a region’s favorite throughway. A highway system, Sun notes, is just that:
a system. Making changes to one arm of a network without considering the
impact on connecting roadways may cause more aggravation than answers.
Ultimately, by integrating his research into traffic operation, Sun hopes
the model and software simulation will help transportation and highway
officials propose wise traffic management that will both reduce congestion
and increase safety. As an additional bonus, Sun notes energy consumption
would dip and air pollution caused by vehicle emissions would drop, since
constant braking increases fuel use.
Sun hopes the model, which is being calibrated with the help of graduate
students in his research group, will be up and running by 2008. In the
meantime, expect delays.
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