Making the Most of Long-Range Models for Automated and Connected Vehicle Planning poster was presented at the 95th Annual Transportation Research Board (TRB) Annual Meeting in January 2016. Automated/Connected Vehicle technology (AV/CV) is expected to have significant impacts on travel behavior. The potential transformative nature of these technologies to alter or influence future travel behavior and demand is quite significant.

1. 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
Control
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 (h-pang@tti.tamu.edu)
Research Sponsored by: Research Conducted by:
Kevin Hall, Research Scientist, Texas A&M Transportation Institute (k-hall@tti.tamu.edu)
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