Autonomous vehicle ownership and sharing: a demand forecasting approach for the Puget - PowerPoint Presentation

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Autonomous vehicle ownership and sharing: a demand forecasting approach for the Puget Sound Region and beyond

Dr. Chandra Bhat

Co-authors

:

Patricia

S. Lavieri, Sebastian Astroza, Felipe Dias, Venu M. Garikapati, and Ram M. Pendyala

Slide2

MOTIVATION

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The Context

Autonomous Vehicles: Vehicles that are able to guide themselves from an origin point to a destination point desired by the individual

Individual yields full control to artificial intelligence technology

Individual decides an activity-travel plan (or tour-specific information)

The plan is keyed into the car’s intelligence system

The car (or an external entity connected to the car) decides on a routing and circuit to complete the plan

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CTR Research

OverviewAutonomous TechnologyConnected TechnologyCARSTOP project

Adoption

Societal Effects

Planning

Slide5

Did you know?

All New 2018 Vehicles Must Have A Back-Up Camera

Roughly 200 people are killed each year and another 14,000 are injured in so-called

backover

accidents, when drivers reverse over another person without noticing him or her.

20 Automakers (99% of the US market) have agreed to make automatic braking standard by 2022

IIHS estimates that automated braking at full penetration would have prevented 700,000 crashes in 2013 (13% of all crashes)

“The data show that the Tesla vehicles crash rate dropped by almost 40 percent after

Autosteer

installation.” – NHTSA Report

Tesla expects a 90% drop with Autopilot 2

Tesla is selling insurance in Australia and Hong Kong

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Automated Vehicles and Transportation

Technology

Infrastructure

Traveler

Behavior

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Self-Driving

Vehicle (e.g., Google)

Connected Vehicle

AI located within the vehicle

AI wirelessly connected to an external communications network

“Outward-facing” in that sensors blast outward from the vehicle to collect information without receiving data inward from other sources

“Inward-facing” with the vehicle receiving external environment information through wireless connectivity, and operational commands from an external entity

AI used to make autonomous decisions on what is best for the individual driver

Used in cooperation with other pieces of information to make decisions on what is “best” from a system optimal standpoint

AI not shared with other entities beyond the vehicle

AI shared across multiple vehicles

A more “Capitalistic” set-up

A more “Socialistic” set-up

Two Types of Technology

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Regular Traffic Conditions

PRESENT DAY

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Icy Patch

PRESENT DAY

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Incident

PRESENT DAY

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Lane blocking, traffic slow down

PRESENT DAY

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Congestion buildup, late lane changes

PRESENT DAY

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Congestion propagation to frontage, ramp backed up

PRESENT DAY

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Regular Traffic Conditions

V2V

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Icy Patch

V2V

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Incident: Information propagation

V2V

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Preemptive lane changing, freeway exit

V2V

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Re-optimization of signal timing, upstream detours

INCIDENT AHEAD TAKE DETOUR

V2I

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Regular Traffic Conditions

Autonomous

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Icy Patch

Autonomous

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Avoidance of icy patch, no incident

Autonomous

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Traffic slowdown, late lane changing, congestion

Autonomous

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Icy Patch

Autonomous + V2X

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Avoidance of icy patch, no incident

Autonomous + V2X

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Information propagation, preemptive lane changing, freeway exit

Autonomous + V2V

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Re-optimization of signal timing, upstream detours

INCIDENT AHEAD TAKE DETOUR

Autonomous + V2I

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Shared Autonomous Vehicles (SAV) vs. Private Ownership

Private

ownership

Chauffeuring household members

Shared Autonomus Vehicles (SAV)

Acquired by mobility providers (Uber, Lyft, car2go…)

Travelers purchasing transportation

$/trip

$/mile

$/minute

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Impacts on the Transportation Network and on the Environment

High empty-vehicle-miles traveled

Cancel any network operation gain due to AV platooning

Increased congestion

Reduced AV owners’ value of travel time

PRIVATELY OWNED AV

Increased energy consumption

Low empty-vehicle-miles traveled

Network operation gain due to AV platooning

Low congestion

Fares control value of travel time

SHARED AV

Reduced energy consumption

Subsided fares for social inclusion

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Possible AV impacts on Land-Use Patterns

Live and work farther away

Use travel time productively

Access more desirable and higher paying job

Attend better school/college

Visit destinations farther away

Access more desirable destinations for various activities

Reduced impact of distances and time on activity participation

Influence on developers

Sprawled cities?

Impacts on community/regional planning and urban design

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Impacts on Mode Choice

Automated vehicles

combine the advantages of

public transportation

with that of

traditional private vehicles

Catching up on news

Texting friends

Reading novels

Flexibility

Comfort

Convenience

What will happen to

public transportation

?

Also automated vehicles may result in lesser walking and bicycling shares

Time

less of a consideration

So, will Cost

be the main policy tool to influence behavior?

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Impacts on Mode Choice

Driving personal vehicle more convenient and safe

Traditional transit captive market segments now able to use auto (e.g., elderly, disabled)

Reduced reliance/usage of public transit?

However, autonomous vehicles may present an opportunity for public transit and car sharing

Lower cost of operation (driverless) and can cut out low volume routes

More personalized and reliable service - smaller vehicles providing demand-responsive transit service

No parking needed – kiss-and-ride; no vehicles “sitting” around

20-80% of urban land area can be reclaimed

Chaining may not discourage transit use

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Central Role of Time Use

Notion of time is central to activity-based modeling

Explicit modeling of activity durations (daily activity time allocation and individual episode duration)

Treat time as “continuous” and not as “discrete choice” blocks

Activity engagement is the focus of attention

Travel patterns are inferred as an outcome of activity participation and time use decisions

Continuous treatment of time dimension allows explicit consideration of time constraints on human activities

Reconcile activity durations with network travel durations (feedback processes)

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In Summary

ABM should…

Capture the central role of

activities

,

time

, and

space

in a

continuum

Explicitly recognize

constraints

and

interactions

Represent

simultaneity

in behavioral choice processes

Account for

heterogeneity

in behavioral decision hierarchies

Incorporate

feedback processes

to facilitate integration with land use and network models

SimAGENT

does it all and more…

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Slide34

RESEARCH QUESTION

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Objective

To quantify the potential initial demand for AV technology in the Puget Sound Region (PSR) of the State of Washington in the United States.

Forecasting process was divided into two phases:

Behavioral framework for AV adoption interest

based on latent psychological constructs such as environmental consciousness, technology-savviness, and AV-apprehensiveness.

We used Census data of the same region to generate a synthetic population for each Census Block Group and then

we predicted how the different demands would be geographically distributed across the PSR.

This prediction is then translated into a visualization of the results.

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About adoption

Two forms of adoption were considered:

AV ownership and

AV sharing.

Individuals were grouped into one of four different categories:

interested in AV ownership,

interested in AV sharing,

interested in both options, and

not interested in AV adoption.

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Behavioral framework

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Modeling Individual preferences for ownership and sharing AVs

Consumer interest in the adoption and use of AVs

Impacts of

individual lifestyle preferences,

attitudinal factors, and

current use of disruptive transportation services.

Modeling methodology based on the GHDM approach

Data:

2014 and 2015 Puget Sound Regional Travel Study.

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Individual preferences for ownership and sharing AVs

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data

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Data

Survey with 1800 individuals in the Puget sound Region

AV

interest

Not interested in AV sharing or AV ownership (68.5%)

Interested in AV sharing only (7.6%)

Interested in AV ownership only (8.5%)

Interested in AV sharing and AV ownership (15.4%)

51% reside in low-density neighborhoods

12% reside in zero-vehicle households and 39% resided in one-vehicle households

14% used a ride-sourcing service at least once in their lifetime

9.2% used a car-sharing service at least once in their lifetime

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Lifestyle Variables

Green lifestyle

Frequency of transit usage

Importance of a walkable neighborhood and being close to activities in choice of home location

Importance of being close to public transit in choice of home location

Importance of being within a 30-minute commute to work in choice of home location

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Lifestyle Variables

Tech-savviness

Smartphone ownership

Do not have and do not plan to buy a smartphone (28%)

Do not have but plan to buy a smartphone (4%)

Have a smartphone (68%)

Frequency of usage of smartphone apps for travel information

Frequency of usage of GPS

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Concerns about Autonomous Cars

Type of concern

Not concerned

Somewhat unconcerned

Neutral/

doesnot

know

Somewhat concerned

Very concerned

Equipment and system safety

6.9%

4.4%

22.2%

26.9%

39.6%

System and vehicle security

8.4%

5.0%

26.2%

26.8%

33.7%

Capability to react to the environment

6.2%

3.2%

18.9%

22.8%

48.9%

Performance in poor weather or other unexpected conditions

6.3%

4.3%

21.5%

26.5%

41.4%

Legal liability for drivers or owners

6.4%

4.2%

24.3%

27.4%

37.7%

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Behavioral Model RESULTS

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Elasticities

Variable

Not interested

AV sharing

AV ownership

Both

Bachelor's degree (base: less than Bachelor’s degree)

-2.33%

15.68%

4.94%

1.20%

Graduate degree (base: less than Bachelor’s degree)

-2.91%

21.77%

4.94%

1.20%

Age 18 to 24 (base: ≥ 65 years)

-14.86%

24.24%

-42.86%

118.18%

Age 25 to 44 (base: ≥ 65 years)

-16.08%

12.12%

-10.71%

109.09%

Age 45 to 64 (base: ≥ 65 years)

-1.22%

12.12%

--

0.91%

Annual income < $25,000 (base: > $75,000)

6.62%

-10.67%

-20.00%

-11.45%

Annual income $25-35,000 (base: > $75,000)

3.09%

1.33%

-14.12%

-6.25%

Annual income $35-75,000 (base: > $75,000)

2.94%

-12.00%

-12.94%

--

Worker (base: non-worker)-4.23%20.31%

18.06%6.67%

Kids under 5 years old (base: no kids)2.17%-6.62%

1.41%2.31%Kids 5-17 years old (base: no kids)

3.04%-7.94%

2.09%3.30%Experienced carsharing (base: never)

4.29%---40.96%

--

Experienced ridesourcing (base: never)

-9.86%

92.31%

-17.07%

18.75%

High density household census block (base: <3,000 hh/mi

2

)

-5.59%

44.86%

--

-5.96%

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Prediction of the Geographic distribution of demand

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Population synthesis

We need disaggregate household and person socio-demographic data for entire population of Puget Sound Region.

We generated a synthetic population

by expanding the disaggregate sample data to mirror CENSUS aggregate distributions of household and person variables of interest.

Software:

POPGEN 1.1

(developed by Arizona State University).

Slide49

Visualization

Three maps in ArcGIS that show

number of people interested in

AV ownership only,

AV sharing only, and

both options (ownership and sharing).

Maps show the predicted interest

in each Census tract.

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Interested in AV Ownership only

Number of people

Average:

323.5 people/census tract

Max:

761.2 people/census tract

Std. Dev.:

110.8 people/census tract

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Interested in AV Sharing Only

Number of people

Average:

285.4 people/census tract

Max:

685.1 people/census tract

Std. Dev.:

102.4 people/census tract

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Interested in Both Options

Number of people

Average:

585.9 people/census tract

Max:

1,294.0 people/census tract

Std. Dev.:

187.2 people/census tract

Slide53

Conclusions

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Conclusions

Results show:

Individuals with green lifestyle preferences and who are tech-savvy

are more likely to adopt car-sharing services, use ride-sourcing services, and embrace autonomous vehicle-sharing in the future.

Younger and more educated urban residents

are more likely to be early adopters of autonomous vehicle technologies, favoring a sharing-based service model.

Individuals who currently eschew vehicle ownership, and have already experienced car-sharing or ride-sourcing services

, are especially likely to be early adopters of AV sharing services.

GHDM can be used to predict adoption of autonomous vehicle technologies

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Conclusions (cont.)

The map resulting from our analysis is an important resource for the different stakeholders involved in planning and implementing the future of transportation:

Transportation authorities can identify areas of interest for analyzing AV deployment impacts under alternative future scenarios.

Mobility providers of ride-sourcing services and

carsharing

services can identify effective spatial strategies for deploying shared AV systems.

Based on behavioral transferability assumptions →

Model may be transferred

to produce initial

forecasts of AV adoption interest for different cities in the country.