Development and Application of a Model to Estimate Driverless Autonomous

Vehicle Trips

Innovations in Travel Modeling, June 2017

Authors:

Steve Ruegg, WSPSteve.Ruegg@wsp.com

Jonathan Ehrlich, Metropolitan CouncilJonathan.Ehrlich@metc.state.mn.us

1. Scenarios2. Twin Cities Model Application3. Estimating Owned/Shared Mix4. Assignment of AV trips5. Feedback and integration with the planning model6. Examples of Types of Results7. Additional Research

Presentation Outline

—Ownership Model, All AVs are privately Owned—Sharing Model, All AVs are publicly-available—Mixed, Some owned, some shared AVs—Partial Implementation, Mixed AVs with traditional

non-autonomous vehicles

1. Scenarios for AV Use

1. Cost2. Auto Availability3. Capacity and Flow Model4. Driverless Vehicle Movements

1. Ownership Scenario2. Sharing Scenario

2. Model Adjustments for AV Scenarios

— Parking Costs— Auto Operating Costs— Value of Time

— What are these costs?— Relatively easy to implement within

a model.— May need to stratify costs between

traditional and AVs, Driverless and Occupied.

— Some policy assumptions needed, e.g., tolling

2.1 Cost Assumptions

2.2 Auto Availability Adjustment for AVs

— AVs will allow access to autos for populations that previously did not have access:—Elderly and disabled—Children—Low income (partially)—Auto-deficient households

— Model Adjustments—Adjust inputs so that 95% of Households above lowest Income

(>25k) have sufficient autos to serve adult population. Adjust to 50% for lowest income group.

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2.3 Capacity Adjustment

— AV use will increase capacity by—Ability to maintain shorter headways on freeways and express

ways—AV’s have the ability to mitigate the effects of congestion on

travel time

— Model Adjustments – Owned & Shared Scenarios—Increase capacity by 50% for freeways and expressways—Increase capacity by 10% for Arterials—Modify the relationship between volume and speed to be more

“forgiving” with regard to demand

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AVO

NLY

2.3 Capacity Adjustment for AVs

9

0

500

1,000

1,500

2,000

2,500

3,000

Cap

acity

(Veh

/HR

/Lan

e)

BASE AV

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2.3 Adjusting Volume-Delay Functions

2.4.1 Driverless Vehicle Movements for the Ownership Scenario, Using Activity-Based Model Outputs— Consider all model-estimated vehicle trips for each household,

including origin, destination, start and end times— Create an AV, and connect household vehicle trips sequentially

through the day— Consider time necessary for each driverless trip, and compare with

available time— In some cases consider intermediate parking— Continue to create new AVs until all household trips are served

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8am 10am NOON 2pm 4pm 6pm 8pm 10pm MIDNIGHT

Homer

Marge

Bart

Lisa

Maggie

2.4.1 Driverless Vehicle Movements for the Ownership Scenario

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AV1AV2

2.4.1 Service Algorithms for AVs, Ownership Scenario

— Household Members availability based on location and time— Choice of intermediate parking location compared with home

location if there is more than 30 min wait.— A score is computed for trips to home and the “best” intermediate

parking location. Based on total time.— Parking availability based on a user-supplied share of undeveloped

land— Remote parking demand constrained by capacity

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2.4.1 Example:Owned Vehicle, Household 195302Home Zone 2881

26 Occupied Trips3 vehiclesVehicle 1: 9 DL tripsVehicle 2: 4 DL tripsVehicle 3: 2 DL trips

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2.4.1 Outputs for Ownership Model

— Selected Households – may be a subset of region— Driverless trip records – includes

— Household ID— Vehicle ID— Origin and Destination Zones— Start and End times

— Number of AVs required by household — Number of AVs in intermediate parking, by zone and by time of day

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2.4.2 Driverless Vehicle Movements for the Shared Vehicle Scenario

— Same principal as used for ownership scenario – except all occupied vehicle trips are open to being served

— Search pattern for next available trip seeks to minimize driverless trip time and dwell time between services

— User specifies a minimum and maximum allowable dwell times— User specifies maximum allowable driverless trip time— Result is a set of driverless vehicle trip records, and a log of each

vehicle’s movements throughout the day— Segmentation of input is permitted to allow for parallel processing

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2.4.2 Example:Shared Vehicle 6316

30 Occupied Trips29 Driverless Trips

292 Occupied Miles115 Driverless Miles

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3. Estimating Ownership or Sharing by Household for the Mixed scenario

— Using the 100% shared scenario, Identify vehicles that are used 7 or fewer times/day

— Compute for each household the average number of trips by shared autos used

— For households served inefficiently by shared autos, tag these as “ownership” households.

— This resulted in about 45% of households owning AVs, 55% of households using shared AVs

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0

20,000

40,000

60,000

80,000

100,000

120,000

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48

Num

ber o

f Veh

icle

s

Trips/Day

Distribution of Autonomous Vehicles by Number of Daily Trips

Average Driverless trips/veh=10.0

3. Mixed Scenario: Map of Zones by Share of Households Owning AVs

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4. Assignment of Driverless Vehicles

— Added driverless vehicles as an additional class— Model information available to plot where AVs would dwell when not

in use.— End of Day re-positioning— Wealth of MOE’s available for both occupied and driverless vehicles— Feedback ensures that congestion imposed by driverless vehicles

influences other behavior

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5. Autonomous Vehicle Model Flowchart – Twin Cities ABM

TOURCAST

11 PERIOD HIGHWAY ASSIGNMENT

AVDL ESTIMATION

4 PERIOD HIGHWAY AND TRANSIT ASSIGNMENT

HIGHWAY AND TRANSIT SKIMS

AVDL TRIPS

OCCUPIED VEHICLE TRIPS

HIGHWAY SKIMS

Model Feedback

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6. Examples of Results that are Available

1. Vehicle Fleet Size Estimates2. Trip Length Frequency Distribution3. Efficiency of Use by Shared AVs4. VMT by Level of Service by Scenario5. End of Day Vehicle Re-positioning Map

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6.1 Vehicle Fleet Requirements

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0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

NoBuild Owned AVs Shared AVs Mixed AVs Partial Mixed

VEH

ICLE

S/H

OU

SEH

OLD

AV NON-AV

6.2 Ownership Scenario Driverless Trips by Vehicle

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

TRIP

S

TIME (MIN)

Driverless AV Trip Length Freq Distributions by time -- OWNERSHIP

6-Midnight5-6pm4-5pm3-4pm2-3pm9am-2pm8-9am7-8am6-7amMidnight to 6AMPerson-Veh Trips

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0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

TRIP

S

TIME (MIN)

Driverless AV Trip Length Freq Distributions by time -- SHARED

6-Midnight5-6pm4-5pm3-4pm2-3pm9am-2pm8-9am7-8am6-7amMidnight to 6AMPerson-Veh Trips

6.2 Shared Scenario Driverless Trips by Vehicle

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6.3 Efficiency of Use: Shared Scenario Driverless Trips by Vehicle

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0%2%4%6%8%

10%12%14%16%18%20%

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44

SHAR

E O

F D

RIV

ERLE

SS T

RIP

S

DRIVERLESS TRIPS PER AV

Driverless Trips Per AV: Shared and Mixed ScenariosSHARED MIXED

Change Average Trips/Vehicle from 10.0 to 18.1

6.4 VMT by Level Of Service

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0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000

14,000,000

16,000,000

18,000,000

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 > 1.55VOLUME/CAPACITY RATIO

VMT by LOSNoAVs SHARED OWNERSHIP MIXED MIXED PARTIAL

6.5 Shared Vehicle Repositioning – Shared Scenario3.6M VMT, 64K VHT

6.5 Shared Vehicle Repositioning – Mixed Scenario0.9M VMT, 15K VHT

— AV Driving Characteristics— Vehicle Capital Cost for

each scenario— Vehicle Operating Cost for

each scenario— Behavioral Changes for

— Former non-drivers— Activity pattern changes as

a result of AVs

7. Additional Research

Isaac, Lauren, “Driving Towards Driverless: A Guide For Government Agencies. WSP|Parsons Brinckerhoff. 2016. Page 13.

Questions?