Requirements for AMD Modeling A Behavioral Perspective

Venu Garikapati

National Renewable Energy Laboratory

May 18, 2017

Princeton SmartDrivingCars Summit

What is an Automated Mobility District (AMD)

An AMD is a campus-sized implementation of the connected/automated vehicle technology to realize the full benefits of a

fully electric automated mobility service within a confined region on district.

AMD

Characteristics of an AMD

Fully automated and driverless cars

Service constrained to a dense area

Mix of on-demand and fixed route services

Multi-modal access within/at the perimeter

What are early use-case implementations for AMDs?

• Residential Communities

• University Campuses

• Business Campuses

• Military Bases

• Others…

Application Contexts

Larger Metropolitan Area

Intra- Districts Impacts

•Mobility & energy of AMD fleet

•Land use changes

Inter-Regional Impacts

•Modal choice

•Route choice

•Activity choice

Boundary Issues / Effects

•Mode transfer / parking

•Boundary services

•TNCs, car sharing / rental

AMD

Intra

Inter-area

Boundary

Bo

un

dary

Bo

un

dar

y

AMD Impact Perspectives

Application Context: University Submodel

• Developed for Mid-regional Council of Governments in Albuquerque, New Mexico• ~900,000 individuals from 337,771 households

• University of New Mexico and Central New Mexico Community College • 42,000 students and 18,000 faculty staff

• Separate submodel developed to account for travel patterns of university population• Submodel has its own trip generation, location choice, and mode choice components

(informed by a university travel survey)

• Interacts with the regional travel demand model

University Submodel: Highlights

1

• Model components classified by

• Affiliation (student, faculty, staff); Living arrangement (on/off campus)

2• Separate treatment of intra-campus and non-intra-campus travel

3• A parking infrastructure measure is built into the mode choice model to

reflect the impact of parking on mode shares

𝑈𝑛𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦 𝑃𝑎𝑟𝑘𝑖𝑛𝑔 𝐴𝑡𝑡𝑟𝑎𝑐𝑡𝑖𝑜𝑛 𝐹𝑎𝑐𝑡𝑜𝑟 ∝𝑪𝒂𝒑𝒂𝒄𝒊𝒕𝒚 𝑝𝑒𝑟 𝐶𝑎𝑝𝑖𝑡𝑎

𝐺𝑒𝑛𝑒𝑟𝑎𝑙𝑖𝑧𝑒𝑑 𝑪𝒐𝒔𝒕 𝑜𝑓 𝑃𝑎𝑟𝑘𝑖𝑛𝑔 𝑜𝑛 𝐶𝑎𝑚𝑝𝑢𝑠

Understanding the Adoption of AVsAre We Here Yet?

Understanding the Ownership and Sharing of Autonomous Vehicle (AV) Technologies

• Growing interest in modeling consumer preferences for adoption and use of autonomous vehicles

• Identifying AV adoption rates and ownership models critical in modeling AMDs

• Future AV use likely impacted by history of vehicle ownership and usage, socio-economic attributes, and mobility options

• Impacts of AVs on VMT and energy consumption?

Understanding the Ownership and Sharing of Autonomous Vehicle Technologies

Two lifestyle factors, namely,

green lifestyle (GL), and

technology savviness (TS) included in the models developed

Modeling Requirements for AMDs

• Impact of AMDs on travel patterns of individuals (increase/decrease)Trip Frequency

• Separate treatment for intra- and inter-district travel

• Accurately depicting the ‘boundary transfers’Location Choice

• Impact on vehicle ownership

• Incorporation of an on-demand shuttle service modeMode Choice

• Accurately simulating ‘automated’ vehicles

• Capturing interaction among different modes

• Evaluating and monitoring mobility and energy/emission

Traffic Assignment

At a Minimum!

Network Modeling Requirements for AMDs

Model Type Road network Transit Pedestrians Parking Signals Other needs

Traditional 4 step model 3 2 1 1 2 ?

CAVs-private 3 1 3 3 3 ?

TNC(Uber/Lyft) 3 1 3 3 3 ?

SAVs/aTaxi 3 1 3 3 3 ?

PRT 3 2 1 1 1 ?

The ideal model 3 3 3 3 3 ?

Other AMD options ? ? ? ? ?

Legend3 2 1

Extremely Important Important Not Important

Existing Tools to Model AMDs

ToolEmerging Mobility

Simulation Capabilities

Trip-Based demand

Activity-Based

demand

Multi-ModalCapability

Resolution Open source

VISSIM CAV, MaaS Yes Yes Yes Microscopic No

SUMOCAV, Automated

ShuttleYes Yes Yes Microscopic Yes; C++

POLARIS CAV, MaaS No Yes Yes MesoscopicYes; C++, Python

MATSim SAV, aTaxi No Yes Yes Microscopic Yes; Java

QRS II NA Yes No Yes Macroscopic No

MassMotion NA NA NANo, only

pedestrianMicroscopic No

AIMSUN NA Yes No YesMicroscopic/mesoscopic

No

TRANSIMS NA No Yes Yes Microscopic Yes; C++

Where we are Where we want to be

Existing tools primarily emphasize on

• The road network, with minimal to no consideration for ped/bike/transit

• Privately owned vehicles, but do not model shared economies

• Limited capabilities to model AMDs

• Models built from traditional travel surveys

• Any others…

Need modeling tools that

• Consider the interaction between different modal alternatives

• Capture private as well as shared economies in vehicles

• Model the impact of AMDs on travel behavior

• Are built from field deployments of emerging transportation tech

• Can quantify the energy and emission benefits

• Any others…

AMD Modeling

Bridj Is Dead, But Microtransit Isn'tSomeone will find a way to make these hybrid ride-

hailing/bus services work.--The AtlanticMay 3, 2017

Thanks!

University Parking Attraction Factor (UPAF)

• Parking attraction factor is derived for UNM/CNM

𝑈𝑃𝐴𝐹 ∝𝑪𝒂𝒑𝒂𝒄𝒊𝒕𝒚 𝑝𝑒𝑟 𝐶𝑎𝑝𝑖𝑡𝑎

𝐺𝑒𝑛𝑒𝑟𝑎𝑙𝑖𝑧𝑒𝑑 𝑪𝒐𝒔𝒕 𝑜𝑓 𝑃𝑎𝑟𝑘𝑖𝑛𝑔 𝑜𝑛 𝐶𝑎𝑚𝑝𝑢𝑠

• The generalized parking cost is a composite factor that considers• Average cost of parking in a lot (per day)

• Distance of the lot from the central UNM zone (UNM main campus zone)

• Capacity of the lot

Modeling Framework

Present Choices

Use of

Car-sharing

Use of

Ride-sourcing

Vehicle Ownership

Household Location

Future Intention Based on Current Interest

Interest in AV:

1) Sharing

2) Owning

3) Both

4) None

Socio-demographics

Green Lifestyle

Tech Savviness

User Preferences for Car-sharing and Ride-sourcing Services

• With the advent of Transportation Network Companies (TNCs), ownership

of vehicles might become relic of past

• Pay by the minute or pay by trip

• Possible benefits include reduced need for parking infrastructure

• Potential impacts on daily-activity and long

distance travel patterns?

• Similar in theory to mobility benefits from AMDs

User Preferences for Car-sharing and Ride-sourcing Services

Greater proclivity to use ride-sourcing and car-sharing services

• Young (18-34 years)

• Well-educated (bachelor's degree or higher)

• Employed (Full/Part-time)

• Individuals who are single and own a smartphone

Less proclivity to use ride-sourcing and car-sharing services

• Older age (55 years or above)

• Presence of children in the household

• Households who currently own multiple vehicles

• Individuals who do not own a smart phone

Directions for Future Research

• Models and frameworks informed from a full scale implementation of AMDs

• Need to answer questions/assumptions pertaining to AMDs • Adoption rates

• Impacts on travel behavior and energy consumption

• Operational attributes (frequency, fleet, and ridership)

• Standard set of metrics that serve as a benchmark to design and deploy AMDs

• Study hub and spoke development of a network of AMDs