Yoram Shiftan
Transportation Research Institute,
Technion - Israel Institute of Technology

The use of activity-based models for policy
analysis

Advances in ABM
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Emerging data
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Old challenges
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New challenges
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Travel demand is derived from demand for
activities.

People face time and space constraints that limit
their activity schedule choice.

Activity and travel scheduling decisions are made
in the context of a broader framework:
 Conditioned by outcome of longer term processes.
 Scheduling process interacts with the transportation
system.
Tours
Schedule
Space
Trips
Space
H
Space
H
H
W
W
W
S
H
H
H
D
H
Time
S
H
H: Home
W: Work
H
H
H
D
Time
Time
S: Shop
W
S
D: Dinner out
S
D
D
Pre-Toll
Schedule
Potential Responses to Toll
(a) Change
(b) Change
Mode & Pattern Time & Pattern
Space
(Home)
Space
(c) Work at
Home
Space
Space
Car
Car
Bus
Work
Work
Work
Shop
Car Shop
Time
Time
= Peak Period
Shop
Time
Time
Car
Shop

Secondary effects - adjustment to the activity pattern that
have to be made in response to the primary effect

A more realistic presentation of trip purposes

More detailed travel data: by tour, by trip, by individual, and
by various variable for equity issues and other purposes
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Better input requires for externalities evaluation
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Ability to deal with “induced demand”

Measure of overall accessibility
Pre-Toll
Schedule
Potential Responses to Toll
(a) Change
(b) Change
Mode & Pattern Time & Pattern
Space
(Home)
Space
(c) Work at
Home
Space
Space
Car
Car
Bus
Work
Work
Work
Shop
Car Shop
Time
Time
= Peak Period
Shop
Time
Time
Car
Shop

Ability to analyze data by various categories:
 Income level
 Auto ownership
 Residential location
 VMT
 Travel by mode and time of day
 Fraction of cold/hot starts
 Time and location of starts
 More accurate estimate of emissions
 Exposure measures
 Speed/Acceleration/Driving Profile
 Travel by Vehicle Class and Model

Highway Project

Transit Project
 travel time savings per
 fewer passengers will
vehicle will be less than
estimated
 vehicle kilometers of
travel will be more than
estimated
 emissions and other
externalities will be
higher than estimated
 benefits for new riders
are ignored
enjoy the improved
service and accessibility
 revenue will be lower
than estimated
The Assumption of Fixed Demand
on Users’ Benefits
D
S1
a
e
S2
b
h
g
f
c
Bias from the Assumption of Fixed
Demand
Fixed Demand Bias by V/C and Demand Elasticity
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
0.25
0.50
0.75
1.00
1.25
V/C
E=(-0.25)
E=(-0.5)
E=(-0.75)
1.50
Extending the Framework
Household decisions
Urban development
Work place
Residential
choice
Auto
ownership
Kids
errands
Shopping
behavior
Parking
Transit
ABA
Activity participation (location,
sequence, scheduling, mode)
Driver’s decisions (route, parking)
Transportation system performance
Value of Accessibility calculation process
CSn 
1
 max j (U nj j )
n
dU nj
dU nj
n 

dYn
dCn
E (CSn ) 
1
ABAn 

1
n 
J
ln(  e
j 1
1

J
ln(  e
Vnj
Vnj
j 1
)C
)



Provide details on tours not just trips
Provide better output for externality calculations
They are disaggregate, therefore can provide
detailed travel data:
 By socio-economic
 By auto ownership
 By type of tours
 By type of trips (cold/hot starts)


Ability to deal with Induced demand
Provide accessibility measure to feed into long term
choice decision models, and to economic evaluation

Computation power continues to increase

Advance in Econometric enables better behavioral
representation/realism

Advanced in data collecting methodologies contribute to
improve data

Integrating with land use models

Integration with longer-term decisions

Explicit integration with micro-simulation/dynamic
assignment

parking choices and constrained parking
equilibrium

Deriving activity demand from happiness/lifestyle

Activity scheduling

Meaning of activities

Priority of activities/urgency

Social network (ride-sharing)

Models of household interaction

Car allocation

More detailed definition of activities

In-home and out-of-home activity trade-off

Fine level of time resolutions

Representing time as continuous variable

Fine level of spatial resolution.

Multi day/weekly ABM

Seasonality/Special events

Non-residents/visitors

Learning models: day to day/Spatial learning

Improvement of accessibility variables

Flexible model structure

Choice set generation

Rule-based vs. RUM models

Need for much more detailed and higher resolution data.
New data collection techniques .

Some advances in data collection:






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Panel surveys
Multiple-day surveys
Revealed/Stated preference surveys
Efficient use of different source of data
Maximize use of existing data
Big Data
 Sophisticated processing algorithm
 Machine learning algorithms

Use of GPS/data-loggers/Apps
Funf in a box

Preferences, Hobbies, Needs, Abilities

Well being?

Detailed of home activities

Household member task substitution

The attractiveness of different locations

Substitution possibilities

Which activities are available where?

Constrains: operating hours

At what spatial resolution?

Activity based SP
Behavioral Realism and Computational Complexity
Behavioral
Realism
Computational
complexity
Benefits from Behavioural Realism
and Computational Simplicity
Total Model
Benefits
Behavioral
Realism
Computational
Simplicity
Total Model
Benefits
Behavioral
Realism
Computational
Simplicity
50
45
40
35
%
30
25
20
15
10
5
0
0.0-0.8
0.8-1.2
Utilization ratio
1.2-2.0

Time schedule – planning vs. construction

Skewed distribution of demand/cost/ construction time
of actual vs. estimated – indication of underlying
systematic bias.

The role of modeling in decision making

Interest groups

Institutional barriers (choose easy to implement/less
controversial projects)

Incentives for local governments to opt for high
investment projects

Policy needs continue to intensify

Technology continues to develop

Social changes

Attitude and preference changes

Emergence of various services:
 Megabus
 CitiBike
 Zipcar
 Uber
 Lyft

US Car sharing users has grown from 12K in 2002
to 900K in 2013.

Safety
 Increase speed/reduce travel time


Capacity increase, reduce headway and lane width
Reduce driver burden
 (stress/fatigue/productive time)

travel time budget, VOT

Cost
 increase - technology,
 reduce – sharing, insurance, parking, fuel efficiency
 Travel money budget



Parking requirements
New opportunities for young/elderly/disabled
Task allocation – the car take the kids to school

Reduce transit cost, allow more flexibility

Reduce car-sharing/ride-sharing barriers
 No need to walk to them/park them.
 Allow repositioning vehicles to better response to
demand.

New modes – public/private combination
 Can be both – as in the auto mate
 Privacy/flexibility/productive time use


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Value of travel time
Longer commute/other travel distances
Activity travel patterns change?
Time of travel – sleep at night…..
Access more desirable activities further away
Land use impacts
 Would value of agglomeration economy diminish?
Value of land?

Car type purchase
 Larger cars - conduct more activities while driving

Reduce walking and bicycling / health effect

North American car-sharing members reduce their
driver distance by 27%, with approximately 25% of
members selling a vehicle and another 25%
forgoing a vehicle purchase (Shaheen and Cohen,
2013).

This will be more attractive with autonomous
vehicles.


Potential behavioral shift following autonomous
vehicles received little attention so far.
What’s different:
 Demand
 Supply: capacity, safety, reliability

What can be captured with existing models
 Analogs of existing modes
 Would preference/attitudes change

What structural changes are necessary
 New modes, attributes, choice set, decisions
 Behavioral change

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How utility of different mode change
Substitution between private and public modes
transformation via changes at the levels of
institutions and societies
 The role of societal and cultural contextual
factors
 New mobility paradigm – the increasing link
between travel and new technologies, and the
primacy of social networks in influencing travel
decisions.

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The penetration phase



Ford (2012) – Shared autonomous taxi model (but
travelers had to walk to fix taxi stands)
Kornhauser (2013)/Burns et. al. (2013)- dynamic ride
sharing implications (focus on cost and benefits
estimates)
Fagnant and Kockelman (2014)
 How much new travel may be induced (from lower perceived
travel time cost and by those without drive licenses)
 BUT assumed trip rates and attraction rates

Walker (2014) – MTC
 4-8% increase in VMT for moderate scenarios
 15% increase in VMT for most extreme scenarios
 The
impacts can be numerous
 A lot of uncertainty!!!
 “California
could explicitly accept that
the future for which is it planning is
highly uncertain” (Wachs, 2012)

Activity based models are continuously improving
in providing better behavioral realism given
advances in data collection, computational power,
and econometric and simulation methods.

Do ABM respond to old policy making challenges?

Are they ready to new challenges: technology and
social changes?

Are they capable to provide the forecasts needed
for major investments in transportation?