making the most of long-range models for av/cv planning

Automated Personal Mobility Environment (APME)

Driver-Assisted

Monitored Fleet

Private Common Use Shared Fleet

Technology Level 3+ Level 4 Level 4 Level 4 Driver Driver required

to take over System monitor required

No driver required

No driver required

Typical Use

Automation-available and automation-only areas; requires driver to vehicle control transition

Public transit, shuttle services on fixed routes

Private ownership, vehicle sharing restricted to small group of authorized users; auto occupancy equivalent to current levels

Common-use subscription or general on-demand services; shared vehicles and shared rides

Poten7al Opera7ng Environments

Capacity Enhancing AV/CV User Op7miza7on •  TV, Radio •  Traffic Apps Close Environment Op7miza7on •  CACC •  Platooning •  Lane Assignment User Level Network Assist •  Departure Time Assist •  Route Assist •  Lane Assist Demand Responsive Infrastructure •  TMC Signal Adjust Automated Personal Mobility Environment •  Departure-‐Time Control •  Route-‐Based Speed HarmonizaBon •  Dynamic SignalizaBon •  Vehicle-‐Use OpBmizaBon

Increasing Network Co

ntrol

Ø  AV/CVs and infrastructure Ø  Personal communicaBons

and Internet of Things Ø  Shared economy and

changes in acBvity paJerns

•  Improving Safety/Reliability •  CoordinaBng Traffic Flow •  Removing the Driving Task

AV/CV Impacts Travel Behavior by:

Making the Most of Long-Range Models for AV/CV Planning

Thomas A. Williams, Research Scientist, Texas A&M Transportation Institute (t-williams@tti. tamu.edu)

Hao Pang, Graduate Assistant Researcher, Texas A&M Transportation Institute ([email protected])

Research Sponsored by: Research Conducted by:

Kevin Hall, Research Scientist, Texas A&M Transportation Institute ([email protected])

AV/CV Forecas7ng Challenge

TxDOT Research Project 0-‐6848: TransportaBon Planning ImplicaBons of

Automated/Connected Vehicles

AV/CV Modeling Alterna7ves

Modeling Results

Modeling: Other Impacts

Automated/Connected Vehicle technology (AV/CV) is expected to have significant impacts on travel

behavior. The potenBal transforma7ve nature of these technologies to alter or influence future travel behavior and demand is quite significant.

Accepted approaches to planning and implemenBng transportaBon systems will be

challenged. Uncertainty regarding legacy systems, such as fixed-‐route transit operaBons also exists.

Scenarios are being envisioned where AV/CV may drama7cally increase capacity. AV/CV may have unintended consequences, such as altering land use paPerns, and have deep impacts to the choices surrounding mobility.

Work is progressing on traffic simula7on models to model AV/CV vehicle

interacBon. AcBvity-‐based models may provide another framework where personal transport choices may be modeled in greater detail needed to determine AV/CV impacts. However, a large majority of the metropolitan

planning organizaBons (MPOs) in the United States sBll uBlize tradi7onal three-‐ or four-‐step trip-‐based models.

How can exis7ng planning tools be used to iniBally address or understand possible outcomes of AV/CV technologies unBl observed data and new demand modeling systems are implemented to address this latest technological innovaBon in personal travel? This team tested various modificaBons of trip generaBon, distribuBon, mode

choice, and assignment to indicate poten7al long range impacts of AV/CV.

AON CAMPO 2040 scenario, all-‐or-‐nothing assignment Baseline

Base CAMPO 2040 scenario Baseline

S1 CAMPO 2040 + add a lane for Expressways and above Shoulder running, lane width

S2 Increase all freeway links to 4000 vphpl Platooning, headway, accel/decel

S3 Increase arterials by 10% vphpl Coordinated arrivals, headway, accel/decel

S4 ProporBonally move the transit trips to SOV and HOV (2 and 3+ ) RoboTaxi, APME, parBal shared

S5 Move all transit trips to SOV only RoboTaxi, APME, 100% private

S6 Move transit trips to HOV only RoboTaxi, APME, 100% shared

AV/CV long-‐range modeling experiments using Capital Area Metropolitan Planning OrganizaBon (CAMPO) modeling system

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

Base S1 S2 S3 S4 S5 S6

AM VMT by V/C Ra7o

0 -‐ 0.5

0.5 -‐ 1

1 and above

0

0.5

1

1.5

2

2.5

Base S1 S2 S3 S4 S5 S6

Travel Time Index

VHT_AM / VHT_FF_AM

Growth AllocaBon (Land Use) Urban Form (Internal Trip Capture) Time of Day Trip Rate and Frequency Trip Length Mobile PopulaBons Freight Trucks Delivery and Commercial Intercity Travel

2010 2015 2020 2025 2030 2035 2040 2045 2050 2055

Types of Inaccuracies In Models: Modeling Uncertainty Error = Lack of CalibraBon Data Modeling Error = StaBsBcal EsBmaBon Error Forecast Error = Error in Input Forecast Data