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Modeling for an Automated Vehicle World
Stephen BoylesAssistant Professor
Civil, Architectural & Environmental EngineeringThe University of Texas at Austin
March 2, 2015D-STOP Symposium
Austin, TX
What do we mean by “automated”?
This talk is about future possibilities of fully-autonomous vehicles which do not require human interaction at all.
Why is everybody talking about AVs?
Enormous opportunities for improving mobility, reducing congestion, improving safety, etc.
So, there’s no time like the present to plan.
While AVs present great opportunities, it is not clear that these opportunities will be realized.
?
Talk outline:
1. How can we model impacts of AVs on congestion?
2. How can we model impacts of AVs on traveler choices?
3. What are the new opportunities for traffic control?
4. Implications for transportation modeling in the future
Transportation models can roughly be
grouped into “supply” and “demand”
“Supply” side
Traffic jamsSignalized controlContraflow lanes
“Demand” side
Route choiceTraveler information
Mode choice
Autonomous vehicles interact very heavily with both of these.
“Supply” side
Traffic jamsSignalized controlContraflow lanes
“Demand” side
Route choiceTraveler information
Mode choice
These two “sides” interact with each other heavily, and an equilibrium concept is often used to reconcile them.
Travel choices determine congestion, but congestion affects travel choices.
In traffic flow theory, this will change the shape of the “fundamental diagram” relating vehicle density to flow.
0
1000
2000
3000
4000
5000
6000
7000
8000
0 50 100 150 200 250 300
Flo
w (
veh
/hr)
Density (veh/mi)
0.25 0.5
1 1.5
Levin & Boyles (2015, under review)
Levin & Boyles (2014)
These high-detail simulation models can also be approximated for use in models with hundreds of intersections.
Reservation-based intersections in DTA Model
Trip generation
Productions and attractions
Trip distribution
Person-trips per origin-destination
Mode choice
Origin-destination trips per mode
Traffic assignment
Routes and flows at user equilibrium
feedback
Transit
Park at destination• Parking fee
Return to origin• Fuel costs
logitmodel
minimum cost
Personal vehicle
Person trips
Downtown Austin network– 88 zones
– 634 nodes
– 1574 links
– 62836 trips
– 84 bus routes
10 value of time classes
Levin & Boyles (2015)
Levin & Boyles (2015)
20
21
22
23
24
25
26
27
28
29
0 2 4 6 8 10
Avg
. lin
k tr
ave
l tim
e (
sec)
Number of classes with autonomous vehicles
Effects on traffic
Levin & Boyles (2015)
Effects on transit
14000
15000
16000
17000
18000
19000
20000
21000
0 2 4 6 8 10
Tran
sit
de
man
d (
pe
rso
n t
rip
s)
Number of classes with autonomous vehicles
In the future, we may see the distinction between “operations” and “planning” start to blur.
Planning: Long-term time horizon; future
forecasts or scenarios; alternatives
analysis and project rankings
Operations: Present-day modeling; real-
time observation and control; travel
information provision
The future: Future planning and
policy analysis accounting for
real-time control technologies
Conclusions
• AVs present tremendous opportunity, but must be carefully planned for.
• Emerging modeling techniques can help guide policy.