Activity-Based Approaches to Travel
Demand Analysis
& Forecasting
GEOGRAPHY 111 & 211A
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Outline
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Background
Building Blocks
Model Components, Data, and Functions
Examples
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Background
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Policy Analysis Areas
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Land use-development policies (smart growth, new urbanism)
Transit and pedestrian access and level of service improvement
projects
Parking policies (restrictions, pricing by time of day)
Congestion pricing & time-of-day incentives (HOT lanes)
Policies affecting work hours (compressed work week, staggered
work hours)
Ridesharing pricing and incentives
Telecommuting and related policies
Individualized marketing strategies
Health management (active living & transportation)
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5
Rapidly Emerging Movement
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Smart Growth (EPA):
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Mix land uses
Take advantage of compact building design
Create housing opportunities and choices for a range of household types,
family size and incomes
Create walkable neighborhoods
Foster distinctive, attractive communities with a strong sense of place
Preserve open space, farmland, natural beauty, and critical environmental
areas
Reinvest in and strengthen existing communities & achieve more
balanced regional development
Provide a variety of transportation choices
Make development decisions predictable, fair and cost-effective
Encourage citizen and stakeholder participation in development decisions
SEE: http://www.newurbanism.org/pages/416429/index.htm
http://www.newurbannews.com/AboutNewUrbanism.html
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More Web Resources
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WWW.smartgrowth.org
http://www.vtpi.org/tdm/tdm24.htm
http://www.smartgrowthplanning.org/Techniqu
es.html
www.nationalgeographic.com/earthpulse/sprawl
/index_flash.html
We will discuss more of these aspects in Land
Use and Transportation
7
Traditional Analysis Areas
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Demographic shifts (aging, household
composition, labor force shifts)
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Changes in household size and composition,
employment and geographic distributions
Impacts of new infrastructure (completion of
the NHS, Major Investment Studies, corridor
improvements, new major developments)
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Travel times on OD pairs, congestion levels at
specific locations, contribution to emission
inventory, NEPA & related studies
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New Issues
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Homeland security preparedness – time of day presence at
specific locations and traveling
Condition of evacuation routes – best routes, fleet management,
advisories to evacuating population
Behavior under emergencies (panic) – where do people go when
a disaster strikes?
Planning models for traffic operations – interface with time of
day traffic assignment, input to traffic simulation models
Special events management– International sport events
(Olympics, World championships, Mundial and related large
gatherings)
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General Approach
(valid for all models here)
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We divide information and data into exogenous and
endogenous
Endogenous are predicted within the model system we
design (e.g., number of trips a person makes in a day)
Exogenous are given to us and we are not able to
influence with our policies (e.g., World and National
economy, fertility rates)
The distinction between exogenous and endogenous
depends on the study/regional model development
scope – the wider the impacts we “cause” the more
comprehensive the model becomes and this increases
the variables we need to “endogenize”
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Motivation for Activity
Social Spheres and the Four Fundamental Forces Underlying Human Activity
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In Essence we Model Interactions
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Human – Nature -> Environmental impacts
(emissions, land use, etc)
Human - Built Environment -> Transportation system
impacts (crowdedness, congestion, accidents)
Human – Machine -> Driver behavior, Use of
information via internet, newspapers, word of mouth,
at bus stops, on the road
Human – Human -> Schedule coordination in time and
space
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Implied Assumptions
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Even when we do not explicitly define the background
model, we implicitly follow some sort of conceptual
model of society
Any type of hierarchy assumes predetermined entities
or some kind of causality – example from demography
The unit of analysis and level of aggregation also imply
we assume the most important relations are at the level
we use – this will become clearer later in this class
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Aggregation levels
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Micro = individuals and households (in traffic a vehicle)
Meso = a group of individuals (segments or geographic
area – in traffic analysis it is a traffic stream or a
platoon)
Macro = an entire city, a region, country, and so forth
Appropriate level depends on the specific policy
application, conceptual model of society we use, the
process we simulate but also data availability and
time/budget (usually higher aggregation lower the
cost)
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Model Evolution
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Regional simulation evolution:
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In the 1950s and 1960s
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In the 1970s and 1980s
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Divide a city into hundreds of Traffic Analysis Zones (500-600) and study a network of some collectors, arterials, and all higher
levels highways as well as transit
All kinds of movements included (suburb to suburb emerged as key aspect)
Objective: divert traffic from cars driven alone to all other modes
In the 1990s
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Divide a large city (Detroit, Chicago) into a few Traffic Analysis Zones (20-30) and study a network of the highest level of
highways (Interstates)
Most interesting movement from and to the CBD
Objective: find how many lanes a ring road needs
Divide a city into thousands of Traffic Analysis Zones (500-600) and study a network of some local roads, collectors, arterials,
and all higher levels highways as well as transit
All kinds of movements included (suburb to suburb emerged as key aspect)
Objective: examine all kinds of policies from parking management to new construction
In the 2000s
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Individuals, households, and parcels (residential and commercial)
More complex behavioral models (tours, time of day models, integration with other models)
Trends: Decreasing size of zones and increasing numbers of zones, closer examination of individual behavior,
household as a decision making unit, expansion of the policy envelope to include car ownership, new vehicle
technologies, information provision, and interface with traffic simulation
- Land Use strategies designed to decrease the use of cars is also emerging as a demand management tool
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Complexity Example by Cambridge
Systematics for PSRC
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Simplification
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We try to identify blocks of decisions that have
something in common
Most often we consider temporal ordering
We also distinguish between the domain within
which an individual chooses from options versus
the household as a decision making unit
We need some sort of sequential system to make
our job tractable – this sequence can be a
hierarchy
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Hierarchy Example
Life Course Decisions – immigration, home
ownership, place to live, education, job/career, family
Long term – residence location, job location, schools for children
Medium term – driver’s license, car ownership
Yearly – public transportation pass/membership, vacation,
enrolment in work related and recreational organized activities
Monthly – pay mortgage and what else ????
Weekly – some kinds of shopping, visiting family/friends
Daily – when to leave home, where to go, what transportation mode
to use, with whom to do things
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Hierarchies are convenient
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Simplification of real world
Allow to focus on decision within each temporal
domain
All lower level (shorter term) relationships are
conditional on the previous level -> specific
ways to create models
Care to reflect relationships -> feedbacks
Example: Car ownership and travel
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Car Ownership & Travel
Get a job - money
Get a better job –
make more money
Buy a car
Travel more often
and longer distances
Replace the car
Accumulate miles
Car gets old
Feedback
from travel to
car
ownership –
but also
access to job
opportunities
All decisions are at different time points and they are
conditional on past decisions
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Building Blocks
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Definitions 1
Activities
-In home stay
Trip
Work
Home
-Work
-Eat meal
Destination
Origin
Stage 2
Stage 1
Home
Ride
share
parkin
g lot
Work
-A trip with two stages
-What happens if I go for
breakfast at a restaurant
by the “ride share
parking lot” ?
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Basic Definitions 2
Home
Work
Tour or Trip Chain
Tour or Trip Chain
-Five trips
UCEN
-Two tours (two trip chains)
Grocery
store
-First tour = 3-trips, homebased, 2 stops
-Second tour = 2 trips, workbased, 1 stop
Note: Some applications identify main tour and secondary tours
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University of Toronto Example
ILUTE
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Taxonomy from Another Viewpoint
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Trip based
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Tour based or trip chains
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Classify trips into a small set of categories
Explain variations based on a set of explanatory variables (age, gender, employment)
Develop procedures to convert these trips into vehicles per hour on highways
Activity generation accounting for trip chains
Tour formation models
Many choices linked through conditional probabilities (using some sort of Nested
Logit model - later)
Synthetic schedules
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Agents building schedules
Regression models of schedules
Cellular automata models (TRANSIMS) – kind of stochastic simulation
Production systems – an integrated system of rules
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Simple 4-step model
(Trip Based)
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The 4-step Model
Convert real world
into Traffic Analysis
Zones – Then
convert highways
and traffic analysis
zones into a set of
nodes and links
building a graph
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Improved 4-step
From Rossi Seminar
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Overview
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Some limitations of 4-step and other older models
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Zones are too large aggregates – ecological fallacy
Does not incorporate the reason for traveling – the activity at the end
of the trip
Main motivation is the purpose as an activity location (places for
leisure, work, shopping)
Trips are treated as if they were independent and ignores their spatial,
temporal, and social interactions
Heavy emphasis on commuting trips and Home-based trips
Limited policy sensitivity (TAZs are hard to use in policy analysis)
Limited ability to incorporate environment and behavioral context
Was not envisioned as a dynamic framework of travel behavior
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Activity-Based Approach(es)
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Activity-Based Approach
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Think and model activities first (the motivation)
Consider interactions among activities and agents (people)
Derive travel as a result of activity participation (derived demand)
Consider linkages among activities and trips (interactions)
Demand for activities <-> time allocation
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By definition a dynamic relationship with feedbacks
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Let’s talk about the ways you follow to schedule
activities
Most approaches imply thinking in terms of temporal hierarchies
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Let’s talk about what causes what is in your
schedules
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The June Ma Model
Demographic
Forecasting
Person
Characteristics
Policy Changes
Household
Socioeconomics
Activity Pattern
on Previous Day
Short-term
transition
Activity
Pattern
Travel Pattern
on Previous Day Short-term
Travel
Pattern
Long-term
transition
Activity Pattern
in Previous Year
Travel Pattern
Long-term in Previous Year
transition
transition
Long-term
Activity &
Travel
Planning
(LATP)
Activity Time Allocation
Transportation
Network &
Activity
Distribution
Long-Term
Activity-Travel
Environment
- Frequencies by activity type
- Home departure time
- Daily time budget
- Activity type, duration, and location
- Travel time and mode
Planned Activity List
Instantaneous
Activity-Travel
Environment
Daily Scheduling
- Activity type, duration, and location
- Travel time and mode
Schedule Updating
External component
Legend:
- Addition
- Deletion
- Re-sequence
Daily
Activity &
Travel
Scheduling
(DATS)
Component not modeled
in the proposed system
Component modeled
in the proposed system
Schedules for all People in the Region
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Activity Patterns (Schedule)
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A sequence of activities, or a schedule, defines a path in space
and time
What defines an activity pattern?
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Total amount of time outside home
Number of trips per day and their type
Allocation of trips to tours
Allocation of tours to particular HH members
Departure time from home
Arrival time at home in the evening
Activity duration
Activity location
Mode of transportation
Travel party
What else?
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Time versus Space patterns
Spatial pattern
Temporal pattern
activities
y
W
L
L
S
H
W
H
S
Real path
Simplified path
Activities:
H … Home
time
x
W … Work
L … Leisure
S … Shopping
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Time versus Space patterns
Spatial pattern
Temporal pattern
activities
y
W
L
L
S
H
W
H
S
Real path
Simplified path
Activities:
H … Home
x
W … Work
Each activity = one episode
time
A trip is an episode too
L … Leisure
S … Shopping
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Time
Activities in Time and Space
Ondrej Pribyl
Visualization
W
H
L
S
Activities:
H … Home W … Work
L … Leisure S … Shopping
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Elements in Models
•
•
•
•
•
Activity Frequency Analysis
Activity Duration and Time Allocation
Departure Time Decision
Trip chaining and stop pattern formation
All these models used together produce a
synthetic schedule
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Constraint Based models
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Time-geography and Situational approaches in the 1970s
Attempt to show dependencies between particular trips
Based on Time Geography research in Lund School,
Sweden, and a seminal paper by Hägerstrand (1970)
“Why are people participating in activities? “
to satisfy basic needs, such as survival and self-realization
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Why call it a constraints-based model?
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Not all activities can be placed into a schedule at all
times.
There are different types of constraints:
Capability constrains – maximum car speed,
minimum required hours to sleep, …
 Coupling constraints – meeting of a workgroup, …
 Authority constraints –opening hours, speed
limit, …
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Effect of constraints in a time-spatial
projection
Time
Capability constraints
Authority constraints
W
H
L
S
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Interaction within a family
(example of coupling constraints)
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The coding of activities:
1 – Work (W)
 2 – Work-related business (WRB)
 3 – Education (Educ)
 4 – Shopping (S)
 5 – Personal business (P)
 6 – Escort (E)
 7 – Leisure (L)
 8 – Home (H)
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Interaction within a family
10
Mother
H
5
E
L
E
E
P
0
0
5
10
15
20
25
15
20
25
10
H
Father
S
5
H
Educ
W
0
0
5
10
10
Daughter
8 years
H
5
0
Educ
0
5
10
15
20
25
0
5
10
15
20
25
10
Daughter
5 years
5
0
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Example 1 from CentreSIM
Husband
Date
Begin
End
Activity
With Whom
For Whom
Time
Time
11:00
11:10
Walked to bank
Husband
Self
11:10
11:20
Banking
Husband and
Family
Bank
January 30
Wife
Person
Employee
11:20
11:25
Returned to Work
Husband
Self
11:25
11:55
Went for Walk
Husband
Self
11:00
11:10
Walked with wife to Credit Union
Wife
Both of us
11:10
11:20
Credit Union Transaction
Wife
Both of us
11:20
11:55
Finished walk with wife
Wife
Both of us
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Example 2 from CentreSIM
Person
Date
Begin
End
Time
Time
8:30
8:45
Activity
With Whom
For Whom
Go to Church
Husband and
Family
Daughter
8:45
10:30
Attended Church
Family
Bank
Wife
Employee
April 13
Husband
Husband and
10:30
10:40
Went to Wal-Mart
Self
Father
10:40
10:50
At Wal-Mart
Self
Father
10:50
11:00
Went to Father’s
Self
Father
11:00
11:10
Return Home
Self
Self
9:00
9:10
Went to Church
Wife and
Family
Daughter
9:10
11:50
Attended Church
Wife and
Family
Daughter
11:50
12:00
Returned Home
Wife and
Daughter
Family
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Constraint-Based Models –
Computational Approach
Constraints
Needs
Set of activities
Combinatorial
algorithms
Set of possible
schedules
Trips
Duration
Travel time
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The participation in particular activities
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Ingredients for Activity-Based
Models
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Ingredients of Activity-Based
Models
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Data on time use-allocation (Demand for
Service): Information collected from persons
on their current use of their time to participate
in out-of-home and at-home activities and for
travel from one activity location to another
(called time allocation).
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Ingredients (continued)
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Data on activity opportunities and locations
(Supply of Service): Information collected
from places where people can actually pursue
activities, including home. It also includes other
attributes of activity participation such as
availability, access, cost, etc.
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Ingredients (continued)
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Person and household time use (activity and
travel) profiles: These are the models of time
allocation that function the same way as the
typical UTPS-like models for travel albeit in a
much more complex form and providing more
detailed information for analysts and planners.
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Ingredients (continued)
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An evolutionary engine (from t to t+x): Clearly the
“snapshot” approach, a single time point in the distant
future, to forecasting is surpassed. Alternate future
scenarios are much more useful to decision makers
because of the general trends they show rather than for
their exact values of the forecast parameters. Some
sort of mechanism that makes a region to evolve over
time through the different stages of sociodemographic,
and demand-supply changes is needed to depict the
paths of, for example, traffic changes and reveals the
instances at which policy intervention is needed. One
such engine is called microsimulation.
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Ingredients (continued)
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Interface with other forecasts: The charge of forecasting
regional needs is not limited to transportation. Economic
development, housing, water supply, sewage systems, and
recreation facilities are some other important areas that interface
with transportation and they are within the planning domain of
regional councils. Forecasts are also provided for these areas
using a variety of methods (e.g., sociodemographic forecasting
by cohort-based methods, housing needs by micro-economic
methods, and economic development by macro-economic
models). All these methods need to be interfaced together to at
least provide consistent forecasts.
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Data Requirements
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Data Needs
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Demand Side:
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Longitudinal and geographic information on time use/allocation
(activities, travel, opportunity locations, activity participation
durations, and so forth)
Sociodemographics (age, gender, employment status, occupation,
and so forth).
Knowledge of opportunities and level of service offered to
people by the activity locations and the system that brings either
people to the activities (transportation) or the activities to people
(telecommunication).
Use of technology and information (e.g., use of personal
computers)
Household resource availability (e.g., car ownership, housing
characteristics, telecommunication equipment ownership, etc.)
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Data Needs (continued)
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Supply Side Data
Spatial and non-spatial inventory of activity
opportunities (e.g., shopping and teleshopping
availability by time of day)
 Daily, day-of-the-week, and seasonal opportunity
windows (e.g., periods during which specific
activities can be pursued)
 Networks of spatial and non-spatial activity
opportunities (e.g., transportation and
telecommunications networks)
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Model Components
55
Components – Part 1
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Sociodemographics and time use profiles: These are functions
that are able to depict how different people use their time differently.
Household members’ activity allocators: Task allocation within a
household is one of the major determinants of behavior. These are
the functions that show which activities are associated with which
member of a given household. These allocators could be also
extended to other social groups to reflect tasks and associated
activities when people are members of organized or spontaneous
groups (e.g., a firm and its employees, a neighborhood and its
residents).
Activity & travel equations: These are the equations and routines
that map specific activity pattern behaviors to specific travel
behavior).
Spatio-temporal models of supply: This is a set of functions that
perform the same mapping of time-use to sociodemographics in the
demand side and are needed in supply to correlate geography with
activity opportunity and ultimately predict the desirability of
56
locations.
Components part 2
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Residence-workplace relocation and time use: In the U.S.
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Telecommunications-information and time use:
changing jobs and/or residence is a frequent phenomenon. In
this process people go through stages of “cognitive
disengagement” from the previous workplace and/or residence
and phases of “cognitive engagement” with the new workplace
and/or residence. As a result their activity and travel patterns go
through changes that should be captured by the activity-based
travel forecasting system.
Telecommunications are used today either intentionally or
unintentionally to affect the ways people spend their time. For
example, telecommuting has been proposed as a method to
mitigate traffic congestion. In this forecasting system, models
that represent the use of telecommunications and information
by people to participate in activities and travel should also be
included.
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Components Part 3
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Lifecycle-lifestyle changes and time use: Lifecycle and associated lifestyle
are important determinants of time use allocation by individuals and their
households. The changes in lifecycle and concomitant changes in time use
allocation need to also be reflected in the forecasting system in a similar way
as it is done in travel demand.
Seasonal and day-of-the-week time use profiles: Time use may change
dramatically within a week (e.g., a weekday versus weekend) but also based on
seasons (e.g., consider the shopping and related activities people pursue
during the period of Thanksgiving to Christmas in the U.S.). Models need to
incorporate these fluctuations if forecasting is to be done for these periods of
time that are usually excluded from the traditional UTPS-like procedures.
Long-term trends in time use: In addition to the usual source of
information for transportation models (e.g., models from data collected on a
representative day or data spanning a few years), we also need models that
depict longer term trends. For example, to estimate models representing the
changing roles and resulting time allocation between men and women and
respective roles in society.
58
Examples
FAMOS – Florida Activity Mobility
Simulator
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60
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63
64
65
66
67
Examples
ALBATROSS
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70
Examples
CEMDAP
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Ondrej Pribyl – PHD
dissertation (2004)
Uses a Time Use Survey
75
Model Calibration Phase
INPUT DATA
ALGORITHM
OUTPUT
Step 1:
Household
activity patterns
Find groups in data
(Cluster analysis)
Cluster
assignment
Step 2:
Derive likelihood of
participation in
particular activities
(probabilistic tables)
Derived
probabilities
Step 3:
Household and personal
socio-demographics
Derive decision trees
to link the found groups to
socio-demographic
characteristics
(CHAID analysis)
Derived
decision trees
76
Simulation Phase
INPUT DATA
Household and personal
socio-demographics
(to be estimated)
ALGORITHM
OUTPUT
Get a household
from the data set
Step 4:
Derived
decision trees
Assign the household
to a cluster
(household assignment)
Step 5:
Derived
probabilities
Simulate the
daily pattern for
the first person
(activity assignment)
Simulate the entire
daily pattern for
other individuals
Simulated activity
patterns for all adults
in the testing data set
77
Activity Profiles
- Percentage of Population Participating in
Given Activity
Observed
Simulated
78
Evaluation of Activity Profiles
– Mean Square Error for Particular Activity
Types
0,03
Average MSE
0,02
0,01
0
H_A
H_S
W_A
W_S
M_A
M_S
Activity types
D_A
D_S
T
MEAN
79
Differences of time spent in
activities during a day – observed
versus simulated patterns
Differences
minutes percentage
Cluster
1
2
3
4
5
6
7
H_A
36 ( 14
-35 ( -25
-10 ( -10
-32 ( -30
-3 ( -3
10 ( 4
0 ( 0
Cluster
1
2
3
4
5
6
7
H_A
5 ( 5
10 ( 10
-19 ( -12
-20 ( -17
8 ( 15
-9 ( -5
1 ( 1
Cluster
H_A
)
)
)
)
)
)
)
)
)
)
)
)
)
)
1
1
1
0
1
0
0
H_S
( 75
( 74
( 72
( 100
( 82
( 0
( 0
)
)
)
)
)
)
)
H_S
-2 ( -12 )
-15 ( -11 )
31 ( 34 )
-16 ( -163 )
-24 ( -40 )
-3 ( -9 )
-20 ( -30 )
H_S
One adult household
M_S
M_A
W_S
W_A
-11 ( -71 ) 0 ( 0 ) -2 ( -9 ) 0 ( 0 )
-1 ( -36 ) 0 ( 0 ) -1 ( -4 ) 0 ( 0 )
24 ( 13 ) 0 ( 0 ) 3 ( 26 ) 0 ( 0 )
18 ( 97 ) 0 ( 0 ) -2 ( -12 ) 0 ( 0 )
16 ( 11 ) 0 ( 0 ) 0 ( -1 ) 0 ( 0 )
-9 ( -21 ) 0 ( 0 ) 10 ( 100 ) 0 ( 0 )
0 ( 0 ) 0 ( 0 ) 0 ( 0 ) 0 ( 0 )
Two adult household, full time - person 1
M_S
M_A
W_S
W_A
3 ( 2 ) 0 ( 100 ) -6 ( -44 ) 0 ( 65 )
2 ( 23 ) 0 ( 0 ) -4 ( -35 ) 4 ( 32 )
3 ( 24 ) 1 ( 12 ) 0 ( -4 ) 3 ( 86 )
33 ( 22 ) 0 ( 100 ) 6 ( 74 ) 0 ( 0 )
16 ( 10 ) 0 ( 0 ) -3 ( -38 ) 1 ( 57 )
11 ( 29 ) 0 ( 0 ) -1 ( -6 ) 0 ( 13 )
16 ( 12 ) 0 ( 100 ) 1 ( 20 ) 1 ( 44 )
Two adult household, full time - person 2
M_S
M_A
W_S
W_A
D_A
-7 ( -41
-17 ( -30
-5 ( -36
2 ( 1
1 ( 10
-7 ( -67
0 ( 0
D_A
-11 ( -63
-3 ( -16
-13 ( -73
-5 ( -49
1 ( 9
-2 ( -9
3 ( 21
D_S
( 0
( 0
( 0
( 0
( 0
( 0
( 0
)
)
)
)
)
)
)
)
)
)
)
)
)
)
0
0
0
0
0
0
0
)
)
)
)
)
)
)
D_S
0 ( 0 )
3 ( 35 )
-3 ( -42 )
-1 ( -515 )
0 ( -15 )
1 ( 49 )
-5 ( -92 )
-19
50
-14
14
-16
-4
0
T
(
(
(
(
(
(
(
-95
50
-50
30
-47
-18
0
)
)
)
)
)
)
)
3
2
-2
-1
1
1
2
T
(
(
(
(
(
(
(
10
7
-10
-2
3
2
6
)
)
)
)
)
)
)
80
D_A
D_S
T
Evaluation of Time Spent in
Activities

Correlation coefficient


CC
0.956
Regression analysis

R-square
0.914
81
4
Comparison of the average number of
activities in the observed and simulated
Comparison of the average number of activities in the observed and
simulated
patterns
patterns
Number of activities
3,5
Observed patterns
Simulated patterns
3
2,5
2
1,5
1
0,5
0
H_A
H_S
W_A
W_S
M_A
M_S
D_A
D_S
T
Activity types
82
Pearson Chi-square Statistics

Hypothesis test on similarity of the frequency of number
of activities in the observed and simulated patterns
Entire day
Morning peak hours
Afternoon peak hours
12pm-12am 6am – 7am 7am – 8am 5pm – 6pm 6pm – 7pm
2
Test statistics - total χ
0.636
0.521
0.283
0.517
0.586
8
7
7
7
7
Critical value*
15.51
14.07
14.07
14.07
14.07
Asymptotic significance
0.9996
0.9994
0.99996
0.9993
0.9991
Degrees of freedom
* Critical value is computed for level of significance alpha = 0.05.
83
June Ma, Ph.D. (1997)
Uses a panel survey and a two day
travel diary
84
The June Ma Model
Demographic
Forecasting
Person
Characteristics
Policy Changes
Household
Socioeconomics
Activity Pattern
on Previous Day
Short-term
transition
Activity
Pattern
Travel Pattern
on Previous Day Short-term
Travel
Pattern
Long-term
transition
Activity Pattern
in Previous Year
Travel Pattern
Long-term in Previous Year
transition
transition
Long-term
Activity &
Travel
Planning
(LATP)
Activity Time Allocation
Transportation
Network &
Activity
Distribution
Long-Term
Activity-Travel
Environment
- Frequencies by activity type
- Home departure time
- Daily time budget
- Activity type, duration, and location
- Travel time and mode
Planned Activity List
Instantaneous
Activity-Travel
Environment
Daily Scheduling
- Activity type, duration, and location
- Travel time and mode
Schedule Updating
External component
Legend:
- Addition
- Deletion
- Re-sequence
Daily
Activity &
Travel
Scheduling
(DATS)
Component not modeled
in the proposed system
Component modeled
in the proposed system
Schedules for all People in the Region
85
Decision Sequences
Choice of typical
activity pattern
Choice of typical
travel pattern
Home departure time
Daily time budget
Activity type
Activity type
Activity duration
Activity duration
Travel time
Travel time
Travel mode
Travel mode
86
Simulated Mean Values with Different
Daily Time Budget
Observed
Home departure time
Daily time budget
Simulated total time*
Total dur. of sub. act.
Total dur. of main. act.
Total dur. of out-of-home act.
Total dur. of in-home act.
Total travel time
Freq. of sub. act.
Freq. of main. act.
Freq. of out-of-home lei. act.
Freq. of trip chains
% other
% car
% carpool/vanpool
% non-motorized
*
**
Predicted
537.6
525.0
522.6
548.3
258.5
46.8
39.3
56.1
77.5
0.92
1.49
0.45
1.43
3.56
57.69
34.94
3.81
227.0
48.0
45.9
53.9
64.0
0.88
1.60
0.52
1.50
4.21
57.86
34.34
3.59
Baseline
554.0
536.8
475.7
109.6
84.5
53.5
25.2
39.3
1.02
2.36
0.67
0.93
5.02
54.34
37.54
3.08
Simulated
Random Budget
555.2
560.4
499.7
92.3
63.9
36.2
20.4
53.9
0.91
1.95
0.56
0.85
4.97
55.57
36.41
3.07
Simulated total time is the sum of all activity durations and travel times. It is equivalent to time budget observed in the simulation.
Time and durations are measured in minutes and frequencies in episodes.
87
Sajjad Alam, MS, 1996
(simplified model of the PennState
campus life)
88
Used Activity Diary to Derive
Time of Day Profiles
89
Time Spent on Activities (%)
Activity Participation - Students
100
90
80
70
60
50
40
30
20
10
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
L
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.3
0.0
0.0
0.0
0.0
0.6
0.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
K
0.0
1.0
1.2
0.3
0.0
0.0
0.0
5.2
8.4
10.0
6.4
8.3
11.3
9.0
10.7
11.2
9.6
10.4
9.9
8.4
6.8
8.0
3.5
2.8
J
8.4
2.7
0.0
0.0
0.0
0.0
0.0
0.5
1.7
0.6
1.3
1.5
2.2
1.0
0.6
0.1
1.3
4.6
7.3
15.1
12.0
10.3
11.1
12.9
I
7.7
5.6
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.4
2.6
1.8
1.1
2.0
2.6
2.8
2.1
3.6
5.7
12.4
18.9
23.7
22.2
18.2
H
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.6
1.2
1.3
0.0
0.0
1.2
0.2
0.0
0.0
0.0
0.0
0.5
1.9
1.3
0.0
0.0
G
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.6
0.0
0.2
0.0
1.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
F
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.4
0.0
1.4
1.3
0.0
0.2
0.2
1.3
4.0
5.4
2.4
2.4
0.0
1.0
0.0
0.0
E
0.2
0.0
0.0
0.0
0.0
0.0
0.0
0.1
1.0
0.5
1.3
1.2
3.0
2.9
4.3
4.1
4.2
3.7
5.2
7.9
3.5
4.2
4.3
1.3
D
20.5
10.9
7.1
2.2
1.3
1.3
0.0
3.7
20.2
36.3
51.0
52.1
47.3
44.0
45.1
45.5
45.1
31.8
33.4
32.9
35.2
31.2
31.9
26.6
C
1.3
1.0
0.0
0.0
0.0
0.0
0.0
1.0
5.7
15.6
16.6
20.8
17.5
24.9
25.6
27.7
24.0
17.6
9.7
7.5
6.5
6.8
6.6
2.6
B
0.0
1.6
0.0
0.0
0.0
0.0
0.6
5.8
6.0
9.6
6.2
7.9
14.4
11.6
6.9
3.4
2.5
16.9
16.5
8.3
8.2
4.7
1.7
0.0
A
61.9
77.2
90.4
96.2
97.4
97.4
98.1
82.5
54.7
24.8
10.8
5.1
2.5
3.3
3.5
3.3
5.6
5.9
10.0
4.7
7.1
8.9
18.6
35.7
Tim e Segm ent (Hour)
90
Time Spent on Activities (%)
Activity Participation - Faculty
100
90
80
70
60
50
40
30
20
10
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
L
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.4
0.0
0.0
0.0
0.0
0.0
0.0
0.4
0.0
K
0.0
0.3
0.0
0.0
0.0
0.3
2.6
16.8
12.9
5.4
2.8
6.8
8.8
6.9
6.4
6.6
11.3
19.7
10.4
8.2
6.1
4.2
1.3
1.4
J
0.7
0.0
0.0
0.0
0.0
0.0
2.9
2.0
1.2
0.0
1.6
0.4
1.6
1.8
0.7
0.0
2.0
4.9
8.6
23.4
16.9
19.1
16.7
7.8
I
0.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.7
0.0
0.0
0.7
3.4
4.8
4.8
5.2
6.9
3.9
1.2
H
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.8
0.0
0.0
0.0
0.0
0.5
1.4
3.8
5.7
2.1
0.8
0.0
G
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.4
0.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
F
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.6
3.3
0.3
0.1
0.3
0.0
0.0
0.9
3.0
2.7
2.5
1.6
1.8
0.0
0.0
0.0
E
0.0
0.0
0.0
0.0
0.0
2.3
3.1
8.3
6.0
0.5
0.4
0.4
0.0
0.0
0.0
0.8
3.0
20.8
17.8
15.0
14.8
11.5
2.3
2.3
D
0.0
0.0
0.0
0.0
3.1
3.1
3.1
3.1
3.1
2.1
0.0
1.2
0.1
0.0
2.1
3.1
3.1
0.5
0.0
3.6
4.7
2.0
1.6
0.0
C
1.6
0.8
0.0
0.0
0.0
2.3
8.7
18.5
62.3
84.4
94.7
89.0
62.4
84.9
88.6
84.8
75.9
31.5
13.1
13.4
26.4
24.6
21.8
8.9
0.0
0.0
B
0.0
0.0
A
97.0
99.0 100.0 100.0
0.0
1.0
8.7
17.8
5.1
2.4
0.0
1.2
25.7
4.9
0.5
0.0
0.4
7.4
28.6
11.5
0.4
1.6
1.6
0.0
96.9
90.9
70.8
33.4
7.9
0.4
0.0
0.9
0.4
0.8
1.8
3.4
0.7
8.6
12.6
14.7
17.8
28.1
49.7
78.4
Tim e Segm ent (Hour)
91
Time Spent on Activities (%)
Activity Participation - Staff
100
90
80
70
60
50
40
30
20
10
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
L
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
K
0.0
0.0
0.0
0.0
0.0
0.0
6.0
31.9
7.0
4.0
2.9
6.3
12.3
4.6
5.1
6.3
20.0
29.2
9.2
8.0
5.9
4.9
3.5
0.1
J
1.6
0.0
0.0
0.0
0.8
1.6
0.0
0.0
0.8
0.0
0.7
1.6
4.9
0.0
0.0
0.0
1.1
5.2
9.4
18.2
27.0
33.0
25.0
16.8
I
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
1.0
1.2
0.4
1.6
1.6
1.6
1.8
8.2
11.9
7.8
10.7
6.1
1.6
H
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.3
0.3
0.0
0.0
0.1
2.6
0.0
0.0
0.3
0.0
0.0
1.2
5.9
6.4
0.8
0.0
0.0
G
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.8
0.3
0.3
1.8
2.9
0.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
F
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.7
1.0
0.8
2.4
4.4
0.0
0.4
0.0
0.5
3.2
3.7
8.0
4.5
3.3
2.5
1.6
E
0.0
0.0
0.0
3.3
0.0
2.0
7.1
9.4
5.7
4.0
2.3
0.8
0.0
0.0
0.0
0.7
10.7
25.0
31.7
32.5
38.3
27.0
14.2
4.9
D
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.2
3.3
0.3
0.0
0.7
1.6
1.2
0.0
0.0
0.0
1.6
0.0
0.0
C
0.0
0.0
0.0
0.0
0.0
0.8
4.5
24.1
76.9
86.6
89.3
79.9
46.2
88.7
92.9
89.9
62.2
13.3
5.3
4.1
4.9
4.6
3.5
0.0
B
0.0
0.0
0.0
A
98.4 100.0 100.0
0.0
0.0
2.0
10.8
8.6
2.5
0.5
0.1
1.8
23.8
6.0
0.0
0.5
2.0
15.4
24.7
7.5
1.4
0.6
1.2
2.0
96.7
99.2
93.4
71.5
24.9
5.7
3.7
2.0
2.0
0.8
0.0
0.0
0.0
0.1
5.7
6.5
4.0
3.8
13.4
44.0
72.8
Tim e Segm ent (Hour)
92
Assembled




Administrative records
Building characteristics
Developed attractiveness indicators (a
gravity/distance model)
A method to sequence activity participation
93
Dynamic Presence on Campus
94
Dynamic Presence on Campus
95
Dynamic Presence on Campus
96
Dynamic Presence on Campus
97
Dynamic Presence on Campus
98
Dynamic Presence on Campus
99
Dynamic Presence on Campus
100
Dynamic Presence on Campus
101
Dynamic Presence on Campus
102
Dynamic Presence on Campus
103
Dynamic Presence on Campus
104
Dynamic Presence on Campus
105
Dynamic Presence on Campus
106
Dynamic Presence on Campus
107
Dynamic Presence on Campus
108
Dynamic Presence on Campus
109
Dynamic Presence on Campus
110
Dynamic Presence on Campus
111
Dynamic Presence on Campus
112
Dynamic Presence on Campus
113
Dynamic Presence on Campus
114
Dynamic Presence on Campus
115
Dynamic Presence on Campus
116
Dynamic Presence on Campus
117
Combination of
These Ideas = Centre SIM
(by J. Kuhnau, J. Eom, and M. Zekkos)







Build a network and facility information from 1997 to
2000
Use business/establishment data
Build and verify zonal system and information therein
Expand Alam approach to the entire county
Identify major new developments and network
changes in 2000 to 2020
Provide a base model and validate it
No new data collection for Kuhnau – Eom and
Zekkos modify routines using new data
118
Simplified time of day activity-location-travel
119
Zone Presence and Travel Demand Output
for Time Segment 8:00 – 9:00 AM
120
Zone Presence and Travel Demand Output
for Time Segment 12:00 – 1:00 PM
121
Zone Presence and Travel Demand Output
for Time Segment 4:00 – 5:00 PM
122
Zone Presence and Travel Demand Output
for Time Segment 8:00 – 9:00 PM
123
More recent with Goods Movements (V/C)
(Jinki Eom MS)
124
Web Resources & Examples





http://www.trbforecasting.org/activityBasedApproaches.html
See also: http://www.trbforecasting.org/innovativeModels.html
See also: http://www.trbforecasting.org/integratedModels.html
A report from practitioners:
http://www.trb.org/Conferences/TDM/
A report from academics/researchers:
http://term.kuciv.kyoto-u.ac.jp/iatbr06/
125