1. 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

2. MOTIVATION

3. 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

4. CTR Research Overview Autonomous Technology Connected Technology
CARSTOP project
Adoption
Societal Effects
Planning

5. 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

6. Automated Vehicles and Transportation
Technology
Infrastructure
Traveler
Behavior

7. Two Types of Technology
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

8. Regular Traffic Conditions
PRESENT DAY
Regular Traffic Conditions

9. PRESENT DAY
Icy Patch

10. PRESENT DAY
Incident

11. Lane blocking, traffic slow down
PRESENT DAY
Lane blocking, traffic slow down

12. Congestion buildup, late lane changes
PRESENT DAY
Congestion buildup, late lane changes

13. Congestion propagation to frontage, ramp backed up
PRESENT DAY
Congestion propagation to frontage, ramp backed up

14. Regular Traffic Conditions
V2V
Regular Traffic Conditions

15. V2V
Icy Patch

16. Incident: Information propagation
V2V
Incident: Information propagation

17. Preemptive lane changing, freeway exit
V2V
Preemptive lane changing, freeway exit

18. Re-optimization of signal timing, upstream detours
V2I
Re-optimization of signal timing, upstream detours
INCIDENT AHEAD TAKE DETOUR

19. Regular Traffic Conditions
Autonomous
Regular Traffic Conditions

20. Autonomous
Icy Patch

21. Avoidance of icy patch, no incident
Autonomous
Avoidance of icy patch, no incident

22. Traffic slowdown, late lane changing, congestion
Autonomous
Traffic slowdown, late lane changing, congestion

23. Autonomous + V2X
Icy Patch

24. Avoidance of icy patch, no incident
Autonomous + V2X
Avoidance of icy patch, no incident

25. Information propagation, preemptive lane changing, freeway exit
Autonomous + V2V
Information propagation, preemptive lane changing, freeway exit

26. Re-optimization of signal timing, upstream detours
Autonomous + V2I
Re-optimization of signal timing, upstream detours
INCIDENT AHEAD TAKE DETOUR

27. Shared Autonomous Vehicles (SAV) vs. Private Ownership
Chauffeuring household members
Shared Autonomus Vehicles (SAV)
Acquired by mobility providers (Uber, Lyft, car2go…)
Travelers purchasing transportation
$/trip
$/mile
$/minute

28. Impacts on the Transportation Network and on the Environment
PRIVATELY OWNED AV
Subsided fares for social inclusion
SHARED AV
High empty-vehicle-miles traveled
Reduced AV owners’ value of travel time
Low empty-vehicle-miles traveled
Fares control value of travel time
Cancel any network operation gain due to AV platooning
Increased energy consumption
Network operation gain due to AV platooning
Reduced energy consumption
Increased congestion
Low congestion

29. 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

30. 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
So, will Cost be the main policy tool to influence behavior?
Time less of a consideration

31. 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

32. 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)

33. In Summary ABM should… SimAGENT does it all and more…
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…

34. RESEARCH QUESTION

35. 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.

36. 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.

37. Behavioral framework

38. 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.

39. Individual preferences for ownership and sharing AVs

40. data

41. 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

42. 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

43. 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

44. 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%

45. Behavioral Model RESULTS

46. 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%
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% --
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/mi2)
-5.59%
44.86%
-5.96%

47. Prediction of the Geographic distribution of demand

48. 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).

49. 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.

50. Interested in AV Ownership only
Number of people
Average: people/census tract
Max: people/census tract
Std. Dev.: people/census tract

51. Interested in AV Sharing Only
Number of people
Average: people/census tract
Max: people/census tract
Std. Dev.: people/census tract

52. Interested in Both Options
Number of people
Average: people/census tract
Max: 1,294.0 people/census tract
Std. Dev.: people/census tract

53. Conclusions

54. 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

55. 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.