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.

The technology for AVs is imminent...

...policymaking is the likely bottleneck, not technology.

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.

SUPPLY MODELS

On a highway, AVs can increase capacity because they require less following distance.

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)

At intersections, we can do even better.

Dresner & Stone (2010)

At intersections, we can do even better.

Dresner & Stone (2010)

At intersections, we can do even better.

Dresner & Stone (2010)

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

DEMAND MODELS

Will AVs induce new demand?

How will they affect choice of destination? Parking? Transit?

vs.

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

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27

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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

REAL-TIME CONTROL

Improved signal operations

Dynamic lane reversal

Carrots and sticks...

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.

Conclusions

• AVs present tremendous opportunity, but must be carefully planned for.

• Emerging modeling techniques can help guide policy.

Conclusions

• AVs present tremendous opportunity, but must be carefully planned for.

• Emerging modeling techniques can help guide policy.

• The future is now!