Activity-Based Approaches to Travel Demand Analysis & Forecasting

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Activity-Based Approaches to Activity-Based Approaches to Travel Demand AnalysisTravel Demand Analysis

& Forecasting& Forecasting

GEOGRAPHY 111 & 211AGEOGRAPHY 111 & 211A

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OutlineOutline

BackgroundBackground Building BlocksBuilding Blocks Model Components, Data, and Model Components, Data, and

FunctionsFunctions ExamplesExamples

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BackgroundBackground

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Policy Analysis AreasPolicy Analysis Areas Land use-development policies (smart growth, new Land use-development policies (smart growth, new

urbanism)urbanism) Transit and pedestrian access and level of service Transit and pedestrian access and level of service

improvement projectsimprovement projects Parking policies (restrictions, pricing by time of day)Parking policies (restrictions, pricing by time of day) Congestion pricing & time-of-day incentives (HOT Congestion pricing & time-of-day incentives (HOT

lanes)lanes) Policies affecting work hours (compressed work week, Policies affecting work hours (compressed work week,

staggered work hours) staggered work hours) Ridesharing pricing and incentivesRidesharing pricing and incentives Telecommuting and related policies Telecommuting and related policies Individualized marketing strategies Individualized marketing strategies Health management (active living & transportation)Health management (active living & transportation)

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Rapidly Emerging Rapidly Emerging MovementMovement Smart Growth (EPA):Smart Growth (EPA):

Mix land usesMix land uses Take advantage of compact building designTake advantage of compact building design Create housing opportunities and choices for a range of Create housing opportunities and choices for a range of

household types, family size and incomeshousehold types, family size and incomes Create walkable neighborhoodsCreate walkable neighborhoods Foster distinctive, attractive communities with a strong Foster distinctive, attractive communities with a strong

sense of placesense of place Preserve open space, farmland, natural beauty, and critical Preserve open space, farmland, natural beauty, and critical

environmental areasenvironmental areas Reinvest in and strengthen existing communities & achieve Reinvest in and strengthen existing communities & achieve

more balanced regional developmentmore balanced regional development Provide a variety of transportation choices Provide a variety of transportation choices Make development decisions predictable, fair and cost-Make development decisions predictable, fair and cost-

effectiveeffective Encourage citizen and stakeholder participation in Encourage citizen and stakeholder participation in

development decisions development decisions SEE: http://www.newurbanism.org/pages/416429/index.htm

http://www.newurbannews.com/AboutNewUrbanism.html

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More Web ResourcesMore Web Resources

WWW.smartgrowth.orgWWW.smartgrowth.org http://www.vtpi.org/tdm/tdm24.htmhttp://www.vtpi.org/tdm/tdm24.htm http://http://

www.smartgrowthplanning.org/www.smartgrowthplanning.org/Techniques.htmlTechniques.html

www.nationalgeographic.com/earthpulse/sprawl/index_flash.html

We will discuss more of these aspects We will discuss more of these aspects in Land Use and Transportationin Land Use and Transportation

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Traditional Analysis Traditional Analysis AreasAreas

Demographic shifts (aging, household Demographic shifts (aging, household composition, labor force shifts)composition, labor force shifts) Changes in household size and composition, Changes in household size and composition,

employment and geographic distributionsemployment and geographic distributions Impacts of new infrastructure Impacts of new infrastructure

(completion of the NHS, Major (completion of the NHS, Major Investment Studies, corridor Investment Studies, corridor improvements, new major developments)improvements, new major developments) Travel times on OD pairs, congestion levels Travel times on OD pairs, congestion levels

at specific locations, contribution to at specific locations, contribution to emission inventory, NEPA & related studiesemission inventory, NEPA & related studies

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New IssuesNew Issues Homeland security preparedness – time of day Homeland security preparedness – time of day

presence at specific locations and travelingpresence at specific locations and traveling Condition of evacuation routes – best routes, Condition of evacuation routes – best routes,

fleet management, advisories to evacuating fleet management, advisories to evacuating populationpopulation

Behavior under emergencies (panic) – where do Behavior under emergencies (panic) – where do people go when a disaster strikes?people go when a disaster strikes?

Planning models for traffic operations – interface Planning models for traffic operations – interface with time of day traffic assignment, input to with time of day traffic assignment, input to traffic simulation modelstraffic simulation models

Special events management– International sport Special events management– International sport events (Olympics, World championships, Mundial events (Olympics, World championships, Mundial and related large gatherings)and related large gatherings)

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General Approach General Approach (valid for all models here)(valid for all models here)

We divide information and data into exogenous We divide information and data into exogenous and endogenousand endogenous

Endogenous are predicted within the model Endogenous are predicted within the model system we design (e.g., number of trips a person system we design (e.g., number of trips a person makes in a day)makes in a day)

Exogenous are given to us and we are not able Exogenous are given to us and we are not able to influence with our policies (e.g., World and to influence with our policies (e.g., World and National economy, fertility rates)National economy, fertility rates)

The distinction between exogenous and The distinction between exogenous and endogenous depends on the study/regional endogenous depends on the study/regional model development scope – the wider the model development scope – the wider the impacts we “cause” the more comprehensive impacts we “cause” the more comprehensive the model becomes and this increases the the model becomes and this increases the variables we need to “endogenize” variables we need to “endogenize”

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Motivation for ActivityMotivation for Activity

Social Spheres and the Four Fundamental Forces Underlying Human Activity

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In Essence we Model In Essence we Model InteractionsInteractions

Human – Nature -> Environmental Human – Nature -> Environmental impacts (emissions, land use, etc)impacts (emissions, land use, etc)

Human - Built Environment -> Human - Built Environment -> Transportation system impacts Transportation system impacts (crowdedness, congestion, accidents)(crowdedness, congestion, accidents)

Human – Machine -> Driver behavior, Use Human – Machine -> Driver behavior, Use of information via internet, newspapers, of information via internet, newspapers, word of mouth, at bus stops, on the roadword of mouth, at bus stops, on the road

Human – Human -> Schedule Human – Human -> Schedule coordination in time and spacecoordination in time and space

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Implied AssumptionsImplied Assumptions

Even when we do not explicitly define the Even when we do not explicitly define the background model, we implicitly follow background model, we implicitly follow some sort of conceptual model of societysome sort of conceptual model of society

Any type of hierarchy assumes Any type of hierarchy assumes predetermined entities or some kind of predetermined entities or some kind of causality – example from demographycausality – example from demography

The unit of analysis and level of The unit of analysis and level of aggregation also imply we assume the most aggregation also imply we assume the most important relations are at the level we use important relations are at the level we use – this will become clearer later in this class – this will become clearer later in this class

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Aggregation levelsAggregation levels

Micro = individuals and households (in traffic Micro = individuals and households (in traffic a vehicle)a vehicle)

Meso = a group of individuals (segments or Meso = a group of individuals (segments or geographic area – in traffic analysis it is a geographic area – in traffic analysis it is a traffic stream or a platoon)traffic stream or a platoon)

Macro = an entire city, a region, country, and Macro = an entire city, a region, country, and so forthso forth

Appropriate level depends on the specific Appropriate level depends on the specific policy application, conceptual model of policy application, conceptual model of society we use, the process we simulate but society we use, the process we simulate but also data availability and time/budget also data availability and time/budget (usually higher aggregation lower the cost)(usually higher aggregation lower the cost)

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Model EvolutionModel Evolution Regional simulation evolution:Regional simulation evolution:

In the 1950s and 1960sIn the 1950s and 1960s Divide a large city (Detroit, Chicago) into a few Traffic Analysis Zones (20-30) and study a 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)network of the highest level of highways (Interstates) Most interesting movement from and to the CBDMost interesting movement from and to the CBD Objective: find how many lanes a ring road needsObjective: find how many lanes a ring road needs

In the 1970s and 1980sIn the 1970s and 1980s Divide a city into hundreds of Traffic Analysis Zones (500-600) and study a network of some 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 transitcollectors, arterials, and all higher levels highways as well as transit All kinds of movements included (suburb to suburb emerged as key aspect)All kinds of movements included (suburb to suburb emerged as key aspect) Objective: divert traffic from cars driven alone to all other modesObjective: divert traffic from cars driven alone to all other modes

In the 1990sIn the 1990s Divide a city into thousands of Traffic Analysis Zones (500-600) and study a network of some local 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 transitroads, collectors, arterials, and all higher levels highways as well as transit All kinds of movements included (suburb to suburb emerged as key aspect)All kinds of movements included (suburb to suburb emerged as key aspect) Objective: examine all kinds of policies from parking management to new constructionObjective: examine all kinds of policies from parking management to new construction

In the 2000sIn the 2000s Individuals, households, and parcels (residential and commercial)Individuals, households, and parcels (residential and commercial) More complex behavioral models (tours, time of day models, integration with other models)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 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 individual behavior, household as a decision making unit, expansion of the policy envelope to include car ownership, new vehicle technologies, information provision, and envelope to include car ownership, new vehicle technologies, information provision, and interface with traffic simulation interface with traffic simulation

- Land Use strategies designed to decrease the use of cars is also emerging as a demand - Land Use strategies designed to decrease the use of cars is also emerging as a demand

management tool management tool

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Complexity Example by Complexity Example by Cambridge Systematics for Cambridge Systematics for

PSRCPSRC

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SimplificationSimplification We try to identify blocks of decisions that We try to identify blocks of decisions that

have something in commonhave something in common Most often we consider temporal Most often we consider temporal

orderingordering We also distinguish between the domain We also distinguish between the domain

within which an individual chooses from within which an individual chooses from options versus the household as a options versus the household as a decision making unitdecision making unit

We need some sort of sequential system We need some sort of sequential system to make our job tractable – this sequence to make our job tractable – this sequence can be a hierarchycan be a hierarchy

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Hierarchy ExampleHierarchy ExampleLife 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 Hierarchies are convenientconvenient

Simplification of real worldSimplification of real world Allow to focus on decision within each Allow to focus on decision within each

temporal domaintemporal domain All lower level (shorter term) All lower level (shorter term)

relationships are conditional on the relationships are conditional on the previous level -> specific ways to create previous level -> specific ways to create modelsmodels

Care to reflect relationships -> feedbacksCare to reflect relationships -> feedbacks Example: Car ownership and travelExample: Car ownership and travel

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Car Ownership & TravelCar Ownership & Travel

Buy a car

Travel more often and longer distances Accumulate miles

Car gets old

Replace the car

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

Get a job - moneyGet a better job – make more money

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Building BlocksBuilding Blocks

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Definitions 1Definitions 1

HomeWork

OriginDestination

Trip

Home

WorkRide share parking lot

Stage 1

Stage 2

-A trip with two stages

-What happens if I go for breakfast at a restaurant by the “ride share parking lot” ?

Activities

-In home stay

-Work

-Eat meal

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Basic Definitions 2Basic Definitions 2

Home Work

Grocery store

UCEN

Tour or Trip Chain Tour or Trip Chain

-Five trips

-Two tours (two trip chains)

-First tour = 3-trips, home-based, 2 stops

-Second tour = 2 trips, work-based, 1 stop

Note: Some applications identify main tour and secondary tours

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University of Toronto University of Toronto ExampleExample

ILUTE

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Taxonomy from Another Taxonomy from Another ViewpointViewpoint

Trip basedTrip based Classify trips into a small set of categoriesClassify trips into a small set of categories Explain variations based on a set of explanatory variables (age, Explain variations based on a set of explanatory variables (age,

gender, employment)gender, employment) Develop procedures to convert these trips into vehicles per hour on Develop procedures to convert these trips into vehicles per hour on

highwayshighways

Tour based or trip chainsTour based or trip chains Activity generation accounting for trip chainsActivity generation accounting for trip chains Tour formation modelsTour formation models Many choices linked through conditional probabilities (using some sort Many choices linked through conditional probabilities (using some sort

of Nested Logit model - later)of Nested Logit model - later)

Synthetic schedulesSynthetic schedules Agents building schedulesAgents building schedules Regression models of schedulesRegression models of schedules Cellular automata models (TRANSIMS) – kind of stochastic simulationCellular automata models (TRANSIMS) – kind of stochastic simulation Production systems – an integrated system of rulesProduction systems – an integrated system of rules

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Simple 4-step modelSimple 4-step model(Trip Based)(Trip Based)

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The 4-step ModelThe 4-step ModelConvert 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-stepImproved 4-step

From Rossi Seminar

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OverviewOverview

Some limitations of 4-step and other older Some limitations of 4-step and other older modelsmodels

Zones are too large aggregates – ecological fallacyZones are too large aggregates – ecological fallacy Does not incorporate the reason for traveling – the activity at the Does not incorporate the reason for traveling – the activity at the

end of the tripend of the trip Main motivation is the purpose as an activity location (places for Main motivation is the purpose as an activity location (places for

leisure, work, shopping) leisure, work, shopping) Trips are treated as if they were independent and ignores their Trips are treated as if they were independent and ignores their

spatial, temporal, and social interactionsspatial, temporal, and social interactions Heavy emphasis on commuting trips and Home-based tripsHeavy emphasis on commuting trips and Home-based trips Limited policy sensitivity (TAZs are hard to use in policy analysis)Limited policy sensitivity (TAZs are hard to use in policy analysis) Limited ability to incorporate environment and behavioral Limited ability to incorporate environment and behavioral

contextcontext Was not envisioned as a dynamic framework of travel behavior Was not envisioned as a dynamic framework of travel behavior

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Activity-Based Activity-Based Approach(es)Approach(es)

Activity-Based Approach Activity-Based Approach Think and model activities first (the motivation)Think and model activities first (the motivation) Consider interactions among activities and agents (people) Consider interactions among activities and agents (people) Derive travel as a result of activity participation (derived Derive travel as a result of activity participation (derived

demand)demand) Consider linkages among activities and trips (interactions)Consider linkages among activities and trips (interactions)

Demand for activities <-> time allocationDemand for activities <-> time allocation By definition a dynamic relationship with feedbacksBy definition a dynamic relationship with feedbacks

Let’s talk about the ways you follow to Let’s talk about the ways you follow to schedule activitiesschedule activities

Most approaches imply thinking in terms of temporal Most approaches imply thinking in terms of temporal hierarchieshierarchies

Let’s talk about what causes what is in Let’s talk about what causes what is in your schedulesyour schedules

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Long-term Activity &

Travel Planning(LATP)

Daily Activity &

Travel Scheduling

(DATS)

Activity Time Allocation- Frequencies by activity type- Home departure time- Daily time budget- Activity type, duration, and location- Travel time and mode

Daily Scheduling- Activity type, duration, and location- Travel time and mode

Schedule Updating- Addition- Deletion- Re-sequence

Schedules for all People in the Region Legend:

External component

Component not modeled in the proposed system

Component modeled in the proposed system

Activity Pattern

TravelPattern

Activity Pattern in Previous Year

Travel Patternin Previous Year

Long-termtransition

Long-termtransition

Activity Pattern on Previous Day

Travel Patternon Previous Day

Short-termtransition


Polic

y C

hang

esPerson

Characteristics

Household Socioeconomics

Long-TermActivity-Travel

Environment

Instantaneous Activity-Travel

Environment

TransportationNetwork &

ActivityDistribution

Demographic Forecasting

Planned Activity List

The June Ma Model

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Activity Patterns Activity Patterns (Schedule)(Schedule)

A sequence of activities, or a schedule, defines a A sequence of activities, or a schedule, defines a path in space and timepath in space and time

What defines an activity pattern? What defines an activity pattern? Total amount of time outside homeTotal amount of time outside home Number of trips per day and their typeNumber of trips per day and their type Allocation of trips to toursAllocation of trips to tours Allocation of tours to particular HH membersAllocation of tours to particular HH members Departure time from homeDeparture time from home Arrival time at home in the eveningArrival time at home in the evening Activity durationActivity duration Activity locationActivity location Mode of transportationMode of transportation Travel partyTravel party What else?What else?

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Time versus Space Time versus Space patternspatterns

Spatial patternSpatial pattern Temporal patternTemporal pattern

Activities:H … Home W … Work L … Leisure S … Shopping

H

W

L

S

timex

y

H

W

L

S

Real path

Simplified path

activities

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Time versus Space Time versus Space patternspatterns

Spatial patternSpatial pattern Temporal patternTemporal pattern


H

W

L

S

timex

y

H

W

L

S

Real path

Simplified path

activities

Each activity = one episode

A trip is an episode too

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Activities in Time and Activities in Time and SpaceSpace

x

y

Tim

e

HW

L

S


Ondrej Pribyl Visualization

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Elements in ModelsElements in Models

• Activity Frequency AnalysisActivity Frequency Analysis• Activity Duration and Time AllocationActivity Duration and Time Allocation• Departure Time DecisionDeparture Time Decision• Trip chaining and stop pattern Trip chaining and stop pattern

formationformation

• All these models used together All these models used together produce a synthetic schedule produce a synthetic schedule

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Constraint Based modelsConstraint Based models

Time-geography and Situational approaches in Time-geography and Situational approaches in the 1970sthe 1970s

Attempt to show dependencies between Attempt to show dependencies between particular tripsparticular trips

Based on Time Geography research in Lund Based on Time Geography research in Lund School, Sweden, and a seminal paper by School, Sweden, and a seminal paper by Hägerstrand (1970) Hägerstrand (1970)

““Why are people participating in activities? “Why are people participating in activities? “- to satisfy basic needs, such as survival and self-to satisfy basic needs, such as survival and self-

realizationrealization

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Why call it a constraints-Why call it a constraints-based model?based model?

Not all activities can be placed into a Not all activities can be placed into a schedule at all times. schedule at all times.

There are different types of constraints:There are different types of constraints: Capability constrains Capability constrains – maximum car – maximum car

speed, minimum required hours to sleep, …speed, minimum required hours to sleep, … Coupling constraintsCoupling constraints – meeting of a – meeting of a

workgroup, …workgroup, … Authority constraints Authority constraints –opening hours, –opening hours,

speed speed limit, …limit, …

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Effect of constraints in a time-Effect of constraints in a time-spatial projectionspatial projection

x

y

Tim

e

HW

L

S

Capability constraintsCapability constraints

Authority constraints

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Interaction within a family Interaction within a family (example of coupling (example of coupling

constraints)constraints) The coding of activities:The coding of activities: 1 – Work (W)1 – Work (W) 2 – Work-related business (WRB)2 – Work-related business (WRB) 3 – Education (Educ)3 – Education (Educ) 4 – Shopping (S)4 – Shopping (S) 5 – Personal business (P)5 – Personal business (P) 6 – Escort (E)6 – Escort (E) 7 – Leisure (L)7 – Leisure (L) 8 – Home (H)8 – Home (H)

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Interaction within a familyInteraction within a family

0 5 10 15 20 250

5

10

0 5 10 15 20 250

5

10

0 5 10 15 20 250

5

10

0 5 10 15 20 250

5

10

Mother

Father

Daughter 8 years

Daughter 5 years

H E

L

P E E

H

W

S H

Educ

H Educ

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Example 1 from Example 1 from CentreSIMCentreSIM

Person Date Begin

Time

End

Time

Activity With Whom For Whom

11:00 11:10 Walked to bank Husband Self

11:10 11:20 Banking Husband and

Bank

Employee

Family

11:20 11:25 Returned to Work Husband Self

Wif

e

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

Hus

band

Janu

ary

30

11:20 11:55 Finished walk with wife Wife Both of us

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Example 2 from Example 2 from CentreSIMCentreSIM

Person Date Begin

Time

End

Time

Activity With Whom For Whom

8:30 8:45 Go to Church Husband and

Daughter

Family

8:45 10:30 Attended Church Husband and

Bank

Employee

Family

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

Wif

e

11:00 11:10 Return Home Self Self

9:00 9:10 Went to Church Wife and

Daughter

Family

9:10 11:50 Attended Church Wife and

Daughter

Family

Hus

band

Apr

il 13

11:50 12:00 Returned Home Wife and

Daughter

Family

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Constraint-Based Models –Constraint-Based Models – Computational ApproachComputational Approach

Constraints

Combinatorial algorithms

Set of possible schedules

TripsNeeds

Set of activities

Duration

Travel time

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The participation in particular The participation in particular activitiesactivities

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Ingredients for Activity-Ingredients for Activity-Based ModelsBased Models

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Ingredients of Activity-Ingredients of Activity-Based ModelsBased Models

Data on time use-allocation Data on time use-allocation (Demand for Service): (Demand for Service): Information Information collected from persons on their collected from persons on their current use of their time to current use of their time to participate in out-of-home and at-participate in out-of-home and at-home activities and for travel from home activities and for travel from one activity location to another one activity location to another (called time allocation). (called time allocation).

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Ingredients (continued)Ingredients (continued)

Data on activity opportunities Data on activity opportunities and locations (Supply of Service):and locations (Supply of Service): Information collected from places Information collected from places where people can actually pursue where people can actually pursue activities, including home. It also activities, including home. It also includes other attributes of activity includes other attributes of activity participation such as availability, participation such as availability, access, cost, etc. access, cost, etc.

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Ingredients (continued)Ingredients (continued)

Person and household time use Person and household time use (activity and travel) profiles:(activity and travel) profiles: These are the models of time These are the models of time allocation that function the same allocation that function the same way as the typical UTPS-like models way as the typical UTPS-like models for travel albeit in a much more for travel albeit in a much more complex form and providing more complex form and providing more detailed information for analysts and detailed information for analysts and planners. planners.

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Ingredients (continued)Ingredients (continued) An evolutionary engine (from t to t+x):An evolutionary engine (from t to t+x):

Clearly the “snapshot” approach, a single time Clearly the “snapshot” approach, a single time point in the distant future, to forecasting is point in the distant future, to forecasting is surpassed. Alternate future scenarios are much surpassed. Alternate future scenarios are much more useful to decision makers because of the more useful to decision makers because of the general trends they show rather than for their general trends they show rather than for their exact values of the forecast parameters. Some exact values of the forecast parameters. Some sort of mechanism that makes a region to sort of mechanism that makes a region to evolve over time through the different stages evolve over time through the different stages of sociodemographic, and demand-supply of sociodemographic, and demand-supply changes is needed to depict the paths of, for changes is needed to depict the paths of, for example, traffic changes and reveals the example, traffic changes and reveals the instances at which policy intervention is instances at which policy intervention is needed. One such engine is called needed. One such engine is called microsimulation. microsimulation.

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Ingredients (continued)Ingredients (continued) Interface with other forecasts:Interface with other forecasts: The charge of The charge of

forecasting regional needs is not limited to forecasting regional needs is not limited to transportation. Economic development, housing, transportation. Economic development, housing, water supply, sewage systems, and recreation water supply, sewage systems, and recreation facilities are some other important areas that facilities are some other important areas that interface with transportation and they are within interface with transportation and they are within the planning domain of regional councils. the planning domain of regional councils. Forecasts are also provided for these areas using Forecasts are also provided for these areas using a variety of methods (e.g., sociodemographic a variety of methods (e.g., sociodemographic forecasting by cohort-based methods, housing forecasting by cohort-based methods, housing needs by micro-economic methods, and economic needs by micro-economic methods, and economic development by macro-economic models). All development by macro-economic models). All these methods need to be interfaced together to these methods need to be interfaced together to at least provide consistent forecasts. at least provide consistent forecasts.

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Data RequirementsData Requirements

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Data Needs Data Needs Demand Side:Demand Side:

Longitudinal and geographic information on time Longitudinal and geographic information on time use/allocation (activities, travel, opportunity locations, use/allocation (activities, travel, opportunity locations, activity participation durations, and so forth)activity participation durations, and so forth)

Sociodemographics (age, gender, employment status, Sociodemographics (age, gender, employment status, occupation, and so forth).occupation, and so forth).

Knowledge of opportunities and level of service Knowledge of opportunities and level of service offered to people by the activity locations and the offered to people by the activity locations and the system that brings either people to the activities system that brings either people to the activities (transportation) or the activities to people (transportation) or the activities to people (telecommunication). (telecommunication).

Use of technology and information (e.g., use of Use of technology and information (e.g., use of personal computers)personal computers)

Household resource availability (e.g., car ownership, Household resource availability (e.g., car ownership, housing characteristics, telecommunication housing characteristics, telecommunication equipment ownership, etc.) equipment ownership, etc.)

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Data Needs (continued)Data Needs (continued)

Supply Side DataSupply Side Data Spatial and non-spatial inventory of Spatial and non-spatial inventory of

activity opportunities (e.g., shopping and activity opportunities (e.g., shopping and teleshopping availability by time of day)teleshopping availability by time of day)

Daily, day-of-the-week, and seasonal Daily, day-of-the-week, and seasonal opportunity windows (e.g., periods during opportunity windows (e.g., periods during which specific activities can be pursued)which specific activities can be pursued)

Networks of spatial and non-spatial Networks of spatial and non-spatial activity opportunities (e.g., transportation activity opportunities (e.g., transportation and telecommunications networks) and telecommunications networks)

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Model ComponentsModel Components

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Components – Part 1Components – Part 1 Sociodemographics and time use profiles: Sociodemographics and time use profiles: These are These are

functions that are able to depict how different people use functions that are able to depict how different people use their time differently. their time differently.

Household members’ activity allocators:Household members’ activity allocators: Task Task allocation within a household is one of the major allocation within a household is one of the major determinants of behavior. These are the functions that determinants of behavior. These are the functions that show which activities are associated with which member show which activities are associated with which member of a given household. These allocators could be also of a given household. These allocators could be also extended to other social groups to reflect tasks and extended to other social groups to reflect tasks and associated activities when people are members of associated activities when people are members of organized or spontaneous groups (e.g., a firm and its organized or spontaneous groups (e.g., a firm and its employees, a neighborhood and its residents).employees, a neighborhood and its residents).

Activity & travel equations:Activity & travel equations: These are the equations These are the equations and routines that map specific activity pattern behaviors to and routines that map specific activity pattern behaviors to specific travel behavior). specific travel behavior).

Spatio-temporal models of supply:Spatio-temporal models of supply: This is a set of This is a set of functions that perform the same mapping of time-use to functions that perform the same mapping of time-use to sociodemographics in the demand side and are needed in sociodemographics in the demand side and are needed in supply to correlate geography with activity opportunity supply to correlate geography with activity opportunity and ultimately predict the desirability of locations. and ultimately predict the desirability of locations.

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Components part 2Components part 2 Residence-workplace relocation and time use: Residence-workplace relocation and time use: In In

the U.S. changing jobs and/or residence is a frequent the U.S. changing jobs and/or residence is a frequent phenomenon. In this process people go through phenomenon. In this process people go through stages of “cognitive disengagement” from the stages of “cognitive disengagement” from the previous workplace and/or residence and phases of previous workplace and/or residence and phases of “cognitive engagement” with the new workplace “cognitive engagement” with the new workplace and/or residence. As a result their activity and travel and/or residence. As a result their activity and travel patterns go through changes that should be captured patterns go through changes that should be captured by the activity-based travel forecasting system.by the activity-based travel forecasting system.

Telecommunications-information and time use:Telecommunications-information and time use: Telecommunications are used today either Telecommunications are used today either intentionally or unintentionally to affect the ways intentionally or unintentionally to affect the ways people spend their time. For example, telecommuting people spend their time. For example, telecommuting has been proposed as a method to mitigate traffic has been proposed as a method to mitigate traffic congestion. In this forecasting system, models that congestion. In this forecasting system, models that represent the use of telecommunications and represent the use of telecommunications and information by people to participate in activities and information by people to participate in activities and travel should also be included.travel should also be included.

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Components Part 3Components Part 3 Lifecycle-lifestyle changes and time use:Lifecycle-lifestyle changes and time use: Lifecycle and Lifecycle and

associated lifestyle are important determinants of time use associated lifestyle are important determinants of time use allocation by individuals and their households. The changes in allocation by individuals and their households. The changes in lifecycle and concomitant changes in time use allocation need to lifecycle and concomitant changes in time use allocation need to also be reflected in the forecasting system in a similar way as it is also be reflected in the forecasting system in a similar way as it is done in travel demand.done in travel demand.

Seasonal and day-of-the-week time use profiles:Seasonal and day-of-the-week time use profiles: Time use may Time use may change dramatically within a week (e.g., a weekday versus change dramatically within a week (e.g., a weekday versus weekend) but also based on seasons (e.g., consider the shopping weekend) but also based on seasons (e.g., consider the shopping and related activities people pursue during the period of and related activities people pursue during the period of Thanksgiving to Christmas in the U.S.). Models need to incorporate Thanksgiving to Christmas in the U.S.). Models need to incorporate these fluctuations if forecasting is to be done for these periods of these fluctuations if forecasting is to be done for these periods of time that are usually excluded from the traditional UTPS-like time that are usually excluded from the traditional UTPS-like procedures.procedures.

Long-term trends in time use:Long-term trends in time use: In addition to the usual source of In addition to the usual source of information for transportation models (e.g., models from data information for transportation models (e.g., models from data collected on a representative day or data spanning a few years), we collected on a representative day or data spanning a few years), we also need models that depict longer term trends. For example, to also need models that depict longer term trends. For example, to estimate models representing the changing roles and resulting time estimate models representing the changing roles and resulting time allocation between men and women and respective roles in society. allocation between men and women and respective roles in society.

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ExamplesExamples

FAMOS – Florida Activity FAMOS – Florida Activity Mobility SimulatorMobility Simulator

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ExamplesExamples

ALBATROSSALBATROSS

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ExamplesExamples

CEMDAPCEMDAP

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Ondrej Pribyl – PHD Ondrej Pribyl – PHD dissertation (2004)dissertation (2004)

Uses a Time Use SurveyUses a Time Use Survey

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Model Calibration PhaseModel Calibration Phase

Household and personal socio-demographics

Derive decision trees to link the found groups to

socio-demographic characteristics

(CHAID analysis)

Household activity patterns

Derive likelihood of participation in

particular activities(probabilistic tables)

Find groups in data(Cluster analysis)

Cluster assignment

Derived probabilities

Derived decision trees

Step 1:

Step 2:

Step 3:

INPUT DATA ALGORITHM OUTPUT

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Simulation PhaseSimulation Phase

Household and personal socio-demographics

(to be estimated)

Derived probabilities

Get a household from the data set

Derived decision trees

Assign the household to a cluster

(household assignment)

Simulate thedaily 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

INPUT DATA ALGORITHM OUTPUT

Step 4:

Step 5:

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Activity ProfilesActivity Profiles-- Percentage of Population Percentage of Population

Participating in Given ActivityParticipating in Given Activity

Observed Simulated

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Evaluation of Activity Profiles Evaluation of Activity Profiles – Mean Square Error for – Mean Square Error for Particular Activity TypesParticular Activity Types

0

0,01

0,02

0,03

H_A H_S W_A W_S M_A M_S D_A D_S T MEAN

Activity types

Ave

rag

e M

SE

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Differences of time spent in Differences of time spent in activities during a day – activities during a day –

observed versus simulated observed versus simulated patternspatterns

Cluster 1 36 ( 14 ) 1 ( 75 ) -11 ( -71 ) 0 ( 0 ) -2 ( -9 ) 0 ( 0 ) -7 ( -41 ) 0 ( 0 ) -19 ( -95 )2 -35 ( -25 ) 1 ( 74 ) -1 ( -36 ) 0 ( 0 ) -1 ( -4 ) 0 ( 0 ) -17 ( -30 ) 0 ( 0 ) 50 ( 50 )3 -10 ( -10 ) 1 ( 72 ) 24 ( 13 ) 0 ( 0 ) 3 ( 26 ) 0 ( 0 ) -5 ( -36 ) 0 ( 0 ) -14 ( -50 )4 -32 ( -30 ) 0 ( 100 ) 18 ( 97 ) 0 ( 0 ) -2 ( -12 ) 0 ( 0 ) 2 ( 1 ) 0 ( 0 ) 14 ( 30 )5 -3 ( -3 ) 1 ( 82 ) 16 ( 11 ) 0 ( 0 ) 0 ( -1 ) 0 ( 0 ) 1 ( 10 ) 0 ( 0 ) -16 ( -47 )6 10 ( 4 ) 0 ( 0 ) -9 ( -21 ) 0 ( 0 ) 10 ( 100 ) 0 ( 0 ) -7 ( -67 ) 0 ( 0 ) -4 ( -18 )7 0 ( 0 ) 0 ( 0 ) 0 ( 0 ) 0 ( 0 ) 0 ( 0 ) 0 ( 0 ) 0 ( 0 ) 0 ( 0 ) 0 ( 0 )

Cluster 1 5 ( 5 ) -2 ( -12 ) 3 ( 2 ) 0 ( 100 ) -6 ( -44 ) 0 ( 65 ) -11 ( -63 ) 0 ( 0 ) 3 ( 10 )2 10 ( 10 ) -15 ( -11 ) 2 ( 23 ) 0 ( 0 ) -4 ( -35 ) 4 ( 32 ) -3 ( -16 ) 3 ( 35 ) 2 ( 7 )3 -19 ( -12 ) 31 ( 34 ) 3 ( 24 ) 1 ( 12 ) 0 ( -4 ) 3 ( 86 ) -13 ( -73 ) -3 ( -42 ) -2 ( -10 )4 -20 ( -17 ) -16 ( -163 ) 33 ( 22 ) 0 ( 100 ) 6 ( 74 ) 0 ( 0 ) -5 ( -49 ) -1 ( -515 ) -1 ( -2 )5 8 ( 15 ) -24 ( -40 ) 16 ( 10 ) 0 ( 0 ) -3 ( -38 ) 1 ( 57 ) 1 ( 9 ) 0 ( -15 ) 1 ( 3 )6 -9 ( -5 ) -3 ( -9 ) 11 ( 29 ) 0 ( 0 ) -1 ( -6 ) 0 ( 13 ) -2 ( -9 ) 1 ( 49 ) 1 ( 2 )7 1 ( 1 ) -20 ( -30 ) 16 ( 12 ) 0 ( 100 ) 1 ( 20 ) 1 ( 44 ) 3 ( 21 ) -5 ( -92 ) 2 ( 6 )

23 7 25 -34 -166 0 0 -4 -25 0 59 1 8 0 100 -22 -107 )15 10 6 -4 -53 0 0 -4 -40 5 43 -7 -29 6 61 -20 -78 )20 35 41 -2 -2 -5 0 -3 -39 3 88 -15 -68 -5 -141 -27 -87 )34 -11 -70 10 8 0 0 -9 -153 0 100 -13 -118 -1 -362 -28 -99 )19 -20 -34 35 24 0 0 -1 -12 1 68 -7 -63 -1 -37 -22 -75 )50 -7 -21 -31 -186 0 0 -19 -145 0 11 -24 -80 1 48 -23 -75 )

0 59 0 4 0 0 0 27 0 8 0 26 0 33 0 30 0 )-4 6 18 -6 -19 1 0 0 5 3 46 4 27 1 17 -7 -36 )1 18 8 -22 -419 1 0 -3 -79 7 61 0 3 3 62 -4 -19 )11 -6 -9 -9 -88 0 0 2 14 5 73 -4 -24 0 4 -10 -36 )20 -15 -18 -6 -6 0 0 -1 -9 1 40 6 17 3 34 -7 -16 )

0 115 0 0 0 0 0 24 0 13 0 36 0 18 0 25 0 )52 -2 -8 -34 -118 0 0 -8 -53 3 50 -28 -225 0 -2 -43 -188 )-16 28 14 -14 -285 1 0 -2 -26 7 67 -1 -6 2 63 -12 -57 )27 11 13 -1 -9 0 0 -19 -203 5 74 -4 -26 1 60 -45 -253 )29 -18 -23 -17 -51 1 0 1 11 1 33 3 15 1 20 -19 -65 )

One adult household

Two adult household, full time - person 1

Two adult household, full time - person 2

T

H_A H_S W_A W_S M_A M_S D_A D_S T

M_A M_S D_A D_S H_A H_S W_A W_S

minutes percentageDifferences

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Evaluation of Time Spent Evaluation of Time Spent in Activitiesin Activities

Correlation coefficientCorrelation coefficient CCCC 0.9560.956

Regression analysisRegression analysis R-squareR-square 0.9140.914

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Comparison of the average Comparison of the average number of activities in the number of activities in the

observed and simulated patternsobserved and simulated patterns

0

0,5

1

1,5

2

2,5

3

3,5

4

H_A H_S W_A W_S M_A M_S D_A D_S T

Activity types

Nu

mb

er

of

ac

tiv

itie

s

Observed patternsSimulated patterns

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Pearson Chi-square Pearson Chi-square StatisticsStatistics

Hypothesis test on similarity of the Hypothesis test on similarity of the frequency of number of activities in the frequency of number of activities in the observed and simulated patternsobserved and simulated patterns

Entire day Morning peak hours Afternoon peak hours

12pm-12am 6am – 7am 7am – 8am 5pm – 6pm 6pm – 7pm

Test statistics - total χ2 0.636 0.521 0.283 0.517 0.586

Degrees of freedom 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

* Critical value is computed for level of significance alpha = 0.05.

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June Ma, Ph.D. (1997)June Ma, Ph.D. (1997)

Uses a panel survey and a Uses a panel survey and a two day travel diarytwo day travel diary

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Long-term Activity &

Travel Planning(LATP)

Daily Activity &

Travel Scheduling

(DATS)

Activity Time Allocation- Frequencies by activity type- Home departure time- Daily time budget- Activity type, duration, and location- Travel time and mode

Daily Scheduling- Activity type, duration, and location- Travel time and mode

Schedule Updating- Addition- Deletion- Re-sequence

Schedules for all People in the Region Legend:

External component

Component not modeled in the proposed system

Component modeled in the proposed system

Activity Pattern

TravelPattern

Activity Pattern in Previous Year

Travel Patternin Previous Year

Long-termtransition

Long-termtransition

Activity Pattern on Previous Day

Travel Patternon Previous Day



Polic

y C

hang

esPerson

Characteristics

Household Socioeconomics

Long-TermActivity-Travel

Environment

Instantaneous Activity-Travel

Environment

TransportationNetwork &

ActivityDistribution

Demographic Forecasting

Planned Activity List

The June Ma Model

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Decision SequencesDecision Sequences

Daily time budget

Activity type

Activity duration

Travel time

Activity type

Activity duration

Travel time

Choice of typical activity pattern

Choice of typical travel pattern

Home departure time

Travel mode Travel mode

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Simulated Mean Values with Different Daily Time Budget

Observed

Predicted

Simulated

Baseline

Random Budget Home departure time

537.6

522.6

554.0

555.2Daily time budget

525.0

548.3

536.8

560.4 Simulated total time*

475.7

499.7Total dur. of sub. act.

258.5

227.0

109.6

92.3Total dur. of main. act.

46.8

48.0

84.5

63.9Total dur. of out-of-home act.

39.3

45.9

53.5

36.2Total dur. of in-home act.

56.1

53.9

25.2

20.4Total travel time

77.5

64.0

39.3

53.9Freq. of sub. act.

0.92

0.88

1.02

0.91Freq. of main. act.

1.49

1.60

2.36

1.95Freq. of out-of-home lei. act.

0.45

0.52

0.67

0.56Freq. of trip chains

1.43

1.50

0.93

0.85% other

3.56

4.21

5.02

4.97% car

57.69

57.86

54.34

55.57% carpool/vanpool

34.94

34.34

37.54

36.41% non-motorized

3.81

3.59

3.08

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.

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Sajjad Alam, MS, 1996Sajjad Alam, MS, 1996(simplified model of the (simplified model of the PennState campus life)PennState campus life)

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Used Activity Diary to Used Activity Diary to Derive Time of Day Derive Time of Day

ProfilesProfiles

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Activity Participation - Activity Participation - StudentsStudents

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Activity Participation - Activity Participation - FacultyFaculty

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Activity Participation - Activity Participation - StaffStaff

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AssembledAssembled

Administrative recordsAdministrative records Building characteristicsBuilding characteristics Developed attractiveness indicators Developed attractiveness indicators

(a gravity/distance model)(a gravity/distance model) A method to sequence activity A method to sequence activity

participationparticipation

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Dynamic Presence on Dynamic Presence on CampusCampus

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Combination of Combination of These Ideas = Centre SIMThese Ideas = Centre SIM

(by J. Kuhnau, J. Eom, and M. (by J. Kuhnau, J. Eom, and M. Zekkos)Zekkos)

Build a network and facility information Build a network and facility information from 1997 to 2000from 1997 to 2000

Use business/establishment dataUse business/establishment data Build and verify zonal system and Build and verify zonal system and

information thereininformation therein Expand Alam approach to the entire Expand Alam approach to the entire

countycounty Identify major new developments and Identify major new developments and

network changes in 2000 to 2020network changes in 2000 to 2020 Provide a base model and validate itProvide a base model and validate it No new data collection for Kuhnau – Eom No new data collection for Kuhnau – Eom

and Zekkos modify routines using new and Zekkos modify routines using new datadata

119Simplified time of day activity-location-travel

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Zone Presence and Travel Demand Output for Time Segment 8:00 – 9:00 AM

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Zone Presence and Travel Demand Output for Time Segment 12:00 – 1:00 PM

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More recent with Goods More recent with Goods Movements (V/C)Movements (V/C)(Jinki Eom MS)(Jinki Eom MS)

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Web Resources & Web Resources & ExamplesExamples

http://www.trb-forecasting.org/activihttp://www.trb-forecasting.org/activityBasedApproaches.htmltyBasedApproaches.html

See also: See also: http://www.trb-forecasting.org/innovhttp://www.trb-forecasting.org/innovativeModels.htmlativeModels.html

See also: See also: http://www.trb-forecasting.org/integhttp://www.trb-forecasting.org/integratedModels.htmlratedModels.html

A report from practitioners: A report from practitioners: http://www.trb.org/Conferences/TDhttp://www.trb.org/Conferences/TDM/M/

A report from A report from academics/researchers: academics/researchers: http://term.kuciv.kyoto-u.ac.jp/iatbr0http://term.kuciv.kyoto-u.ac.jp/iatbr06/ 6/

Documents

Activity-Based Approaches to Travel Demand Analysis & Forecasting