STREETCAR
From Research to Practice:
Implementing
Real-Time
Control to Avoid
Bus Bunching
B
us bunching naturally occurs on
high-frequency routes. To solve
the bunching problem, Dr. Kari
Watkins, Dr. Jorge Laval and Dr.
Simon Berrebi developed a new
method to dispatch vehicles. Since
it has been shown that passengers
tend to arrive randomly at stops
when buses come every 12 minutes or less,
the method disregards the schedule and
instead uses real-time predicted arrivals
to dispatch vehicles at the same frequency
as they arrive. When a vehicle is predicted
to arrive with great delay, each preceding
vehicle is held to absorb its share of that
delay. Th e method considers every vehicle
on the route to produce a natural headway,
which may fl uctuate throughout the day.
In simulations, they found that the
method, compared with other methods
in practice, could dispatch vehicles with
the most stable headways and the least
holding time. Although encouraging, it
needed to be implemented on a live route.
Th ey partnered with Sean Óg Crudden,
an independent soft ware developer,
and based on an open-source prediction
platform called TransiTime, they built a
series of tools that compute recommended
holding times. Crudden created the
fi rst open-source frequency-based prediction
algorithm in the process. Th e end
result was a dashboard that displayed a
countdown to the recommended departure
time, a map of the system with
real-time vehicle positions, and other
arrival and departure information.
In spring, they successfully implemented
the proposed holding method
on three high-frequency transit routes:
the Atlanta Streetcar, the Georgia Tech
Red Stinger Route and VIA’s Route 100
in San Antonio, Texas. Th e method replaced
the schedule for several hours at a
time on one or multiple days and in each
case, dispatchers instructed operators
how long to hold at control points using
the DynamicTime dashboard.
In all three, the method reduced bus
bunching and big gaps in service, compared
to the regular schedule. On the Atlanta
Streetcar, 43 percent of headways
were within a two-minute interval under
the proposed method against 31 percent
under the schedule. On the Georgia Tech
Red Route, the proposed method was able
to recover from severe bus bunching that
was triggered before implementation. On
the VIA Route, the method systematically
maintained the most stable headways
when compared to the schedule in similar
periods of historical data.
Th rough the implementation, they
tested the importance of vehicle-loca-
8 | Expo Daily | Mass Transit | MassTransitmag.com | OCTOBER 9, 2017
In the Spring of 2017, they successfully implemented the
proposed holding method on the Atlanta Streetcar.
Prediction-based holding methods can be
used to avoid the bus-bunching problem.
By Simon Berrebi
Georgia Tech
Dr. Simon Berrebi,
Dr. Kari Watkins
and Dr. Jorge Laval
research ways
to optimize
public
transportation
with real-time
information
at Georgia
Institute of
Technology.
tion data quality and prediction accuracy.
Th e DynamicTime soft ware was able
to manage the issues of wandering signal
and low polling frequency of the vehicle
location feed encountered, however, losing
GPS signal for several minutes at a time
caused substantial problems. Th ey found
the frequency-based prediction algorithm
to be adequate for this application.
Involving dispatchers in the communication
protocol improved operator
accountability but also created opportunity
for confusion. Knowing they
were monitored, operators had a good
reason to follow instructions. In certain
situations, such as construction or
other events, operators needed to make
ground-level decisions. Th ey found operators
are best positioned to make the
fi nal decision, and should therefore be
provided holding instructions directly.
Th e implementation of this method
on high-frequency routes showed that
prediction-based holding methods can
be used to avoid bus-bunching. Th e next
step should be to implement the method
with tablets inside vehicles or kiosks at
control points to communicate holding
instructions directly to operators.
All the tools developed in this research
are available in open-source format for
any agency to implement and expand.
