Looking to the Future: Predictions of AutomatedVehicle Impacts

Stephen D. Boyles

April 1, 2016

Introduction

How do connected autonomous vehicle (CAV) technologies affect trafficflow?

CAV technologies:• Reduced reaction times from adaptive cruise control• More precise maneuverability• Short-range wireless communications

Potential effects on traffic:• Reduced following headways — greater road capacity• More efficient intersection control — greater intersection capacity

Introduction

How do connected autonomous vehicle (CAV) technologies affect trafficflow?

CAV technologies:• Reduced reaction times from adaptive cruise control• More precise maneuverability• Short-range wireless communications

Potential effects on traffic:• Reduced following headways — greater road capacity• More efficient intersection control — greater intersection capacity

Outline

1 Flow model2 Intersection model3 Effects of AVs on traffic networks4 Unintended consequences: interactions with choices

More efficient components do not always make a more efficientsystem.

Outline

1 Flow model2 Intersection model3 Effects of AVs on traffic networks4 Unintended consequences: interactions with choices

More efficient components do not always make a more efficientsystem.

Flow model

How do reduced reaction times affect flow?• Greater road capacity from reduced following headways

I Kesting et al. (2010); Schladover et al. (2012)• Greater flow stability

I Li & Shrivastava (2002); Schakel et al. (2010)

• Greater backwards wave speed (rate of congestion wave propagation)

Car following model based on reaction time• Based on safe following headway for a given speed• Yields maximum safe speed for given density

Flow model

How do reduced reaction times affect flow?• Greater road capacity from reduced following headways

I Kesting et al. (2010); Schladover et al. (2012)• Greater flow stability

I Li & Shrivastava (2002); Schakel et al. (2010)

• Greater backwards wave speed (rate of congestion wave propagation)

Car following model based on reaction time• Based on safe following headway for a given speed• Yields maximum safe speed for given density

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

Reaction time (s)

qmax = uf 1uf∆t+`

w = `∆t

uf free flow speed` car length

∆t reaction time

qmax capacityw backwards wave speed

0

1000

2000

3000

4000

5000

6000

0 50 100 150 200 250 300

Flo

w (

vph

)

Density (veh/mi)

0

0.25

0.5

0.75

1

AV proportion

qmax = uf 1uf∑

m∈M

kmk ∆tm+`

w = `∑m∈M

kmk ∆tm

uf free flow speed` car length

∆t reaction time

qmax capacityw backwards wave speedkmk proportion of class m

Multiclass cell transmission model

• Based on the CTM of Daganzo (1994, 1995)• Separates flow into AV and human vehicles• Consistent with hydrodynamic theory of traffic flow

ymi (t) = min

{nm

i−1(t), nmi−1(t)

ni−1(t)Qi(t),nm

i−1(t)ni−1(t)

wi(t)uf

(N −

∑m∈M

nmi (t)

)}

𝑥1𝑦1

𝑥2𝑦2

𝑥3𝑦3

𝑥4𝑦4

𝑥5𝑦5

𝑥6

Reservation-based intersection control

• Proposed by Dresner & Stone (2004, 2006)

1 Vehicles communicate with the intersection manager to request areservation

2 Intersection manager simulates request on a grid of space-time tiles3 Requests can be accepted only if they do not conflict

(a) Accepted (b) Rejected

Conflict region model

• Major limitation of reservations: microsimulation definition — nottractable for larger networks• Conflict region simplification: aggregate tiles into capacity-restricted

conflict regions• Tractable for dynamic traffic assignment

1

3

2

Arterial networks

Lamar & 38th Street Congress Avenue

• Greater capacity reduced travel times on all networks• Reservations increased travel time on Lamar & 38th St.

I Reservations disrupted signal progression and allocated more capacityto local roads, causing queue spillback on the arterial

Downtown Austin network

• Greater capacity resulted in 51% reduction in travel time• With reservations and AV reaction times, travel time reduction was

78%

Route choice effects

𝐴 𝐵 𝐶 𝐷 1

4 3

2

Link Free flow travel time (s) Capacity (vph)1, 4 30 2400

2 80 24003 60 1200

Demand from A to D: 2400 vphTraffic signal at C: 60 seconds 2→ 4, 10 seconds 3→ 4

Dynamic user equilibrium• Traffic signals: 2400 vph on [1,2,4]• Reservations: 2400 vph on [1,3,4]

Route choice effects

𝐴 𝐵 𝐶 𝐷 1

4 3

2

Link Free flow travel time (s) Capacity (vph)1, 4 30 2400

2 80 24003 60 1200

Demand from A to D: 2400 vphTraffic signal at C: 60 seconds 2→ 4, 10 seconds 3→ 4

Dynamic user equilibrium• Traffic signals: 2400 vph on [1,2,4]• Reservations: 2400 vph on [1,3,4]

Network throughput is reduced when intersection C isupgraded.

• “Improving” intersections makes certain routes more attractive, whichwill increase demand.• We should improve routes where we want to encourage travel, not to

reward people for what they are already doing.

Predicted effects on transit ridership

Predicted effects on travel speed

Includes additional VMT if vehicles can “drive empty” to park.

Conclusions

• CAVs, and associated technologies, are effective in some scenarios butnot in others.• There is a need for rigorous planning and forecasting — not just

intuition alone.• The time to do this planning is now!