Maria: Hello.
Welcome to segment three of our five-part series.
In our last segment, two consultants, Jill and Paul, completed a cluster analysis for
a high-profile project.
Maria: Let’s peek in [gestures at doorway] and see how Jill and Paul are faring with
their client, Brenda, the state DOT project manager.
Brenda (grim, not making eye contact): Next on my list is the Marine Causeway study – any
progress to report?
I’m under pressure to improve inbound morning travel time reliability.
Jill (positive): Brenda, we’ve got some excellent news.
We’ve analyzed traffic count, travel time, weather and incident data for 100 workdays
to characterize the travel conditions in the network.
Before we construct and calibrate simulation models based on these conditions, we want
to be sure you are on board with our approach.
Brenda (looks up): Characterizing travel conditions?
That’s a new one to me.
Jill (calm): Yes, our approach is based on updated federal guidance on microsimulation.
We used a statistical technique, cluster analysis, to identify distinct travel conditions in
the Marine Causeway network.
These travel conditions lead to calibration targets for our traffic simulation model.
Brenda (looks at her watch): OK, sounds good.
But let’s cover this quickly -- I need to get back to the dee-oh-tee offices for a staff
meeting.
First, tell me more about cluster analysis.
Jill (points to screen): Sure…
Jill (v.o): Cluster analysis is a statistical method that finds underlying patterns in the
data.
Cluster analysis gathers together the days that have similar attributes – minimizing
the differences among days in the same cluster – and at the same time maximizing differences
among days in different clusters.
Brenda: Very nice.
But this is just sample data.
What about the Marine Causeway?
Paul (v.o.): Here’s what we found looking at Marine Causeway network data – five clusters
of weekdays that differ based on demand, weather, and incident frequency and severity.
Brenda (v.o): I see the system travel times vary significantly based on these conditions.
Paul (v.o.): Absolutely.
You can see that different combinations of these factors have different effects, reflecting
the good days and bad commute days in the network.
[isolated head and shoulders shot of Brenda from Jill and Paul’s vantage point]
Brenda (laughs, bitterly): Yes, I certainly hear about it on bad days.
Luckily, it’s not every day.
Jill (v.o.): Here’s a way of visualizing the clusters as a virtual dartboard.
Each cluster is represented by a color-coded rectangle.
The size of the rectangle reflects the frequency of occurrence.
The days with the worst congestion are on the right side of the dartboard…
Brenda (v.o., interrupts): …the days I’m most likely to be asked for some explanation
for all the misery.
Paul (v.o.): The exact causes vary day to day.
But demand, weather, and incidents seem to be the primary factors.
Brenda: (brighter) The dartboard makes sense to me.
The high demand cluster makes me think of the first day of school in September.
Everyone is late to work that day – and calls my office to complain!
Jill: The two alternatives under study in the effort have different impacts and potential
benefits in each of the clusters.
Brenda: I can see that, good point.
What happens next?
These historical data can’t discriminate between the two key alternatives under consideration.
Paul: The simulation models will help identify strategies that work best under each travel
condition.
If you give the OK, I will start calibrating targets for simulation model for each cluster.
Brenda: You have my approval.
Please get started right away.
How does calibration work under the new guidance?
Jill (v.o.): Calibration targets are built around representative days drawn from each
cluster.
The targets are based on the time-dynamic patterns of each key measure, called variation
envelopes.
Brenda (v.o.): Why use a representative day?
Why not just take the average?
Paul (v.o.): The representative day is close to the average within-cluster travel conditions.
But most importantly, it is an actual day, with clear cause-and-effect relationships
between specific causes of congestion and related patterns of delay.
Jill (v.o.): For our sample data, here’s how to visualize the representative days.
They lie close to the cluster average, or centroid, in each case.
Brenda (v.o.): I get it.
Five clusters with five representative days.
How closely does your simulation have to track the observed data from the representative
day?
Jill (v.o.): We calculate how much variance there is in the performance measures within
short intervals as congestion rises and falls.
A calibrated model conforms statistically based on two bands derived from the observed
data, one thick and one thin.
Paul (v.o.): Here’s the dynamic travel time pattern in one cluster for a key route, through
the Komodo tunnel.
Each line in the graph shows the rise and fall of morning rush period travel time on
a specific day.
Brenda (v.o.): One of these days is the representative day, right?
Paul (v.o.): Correct.
It’s the olive-green line that peaks around 33 minutes at seven fifteen and finishes the
morning at just over 20 minutes.
Jill (v.o.): The thick band controls for outliers.
We build the band around the representative day to capture 95% of the variation observed
in each time slice.
Jill (v.o.): The thin band contains two-thirds of the variation at each time slice.
The width is simply the standard deviation.
The thick band is roughly twice the width of the thin band.
Brenda: Hence the names.
Thick band, thin band.
I get it.
Paul (v.o.): Here is are the variation envelops for the ko-mow-doe tunnel route.
The red line is the representative day.
The wider green lines are the thick one-point-nine-six-sigma values.
The narrower purple lines are the thin one sigma bands.
Brenda (v.o.): What about the other route, across the Victory Island Bridge?
Paul (v.o.): I’ll produce a different set of variation envelopes for that route.
The model should produce conforming system dynamics for both routes simultaneously.
Brenda: Will this take a long time?
Paul: I don’t think so.
I have the data so creating the envelopes is easy.
For calibration, I will be concentrating on key measures and time-dynamics.
The federal guidance has a set of four criteria to indicate when a model is calibrated.
Our reliability analysis last year suffered when I was focused on matching network-wide
counts derived as an average from data collected over several years.
It was nearly impossible!
Brenda: I like it.
I’d like to take a copy of your slides along to my staff meeting.
I can use this information to show progress and educate them about the new federal approach.
This seems to be going a lot better than last time.
Jill: Great meeting.
Thanks, Brenda!
Paul: Hooray!
I’ll get calibratin’ right away.
I hope to have the simulation models dancing to the rhythm of system in no time…
Maria (standing near elevators): In the last two segments of our series, will see if Paul
and Jill successfully calibrate their models and apply more detailed elements of the Federal
Highway Administration’s update to its Guidance on Traffic Microsimulation.
See you in Episode 4!