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Agent-Based Microsimulation Modelling: Current

Capabilities, Future Prospects

Eric J. Miller, Ph.D.Professor, Department of Civil EngineeringDirector, Cities CentreUniversity of Toronto

EMME Users’ ConferenceMontreal, October 4, 2010

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Presentation Outline

• Criticisms of four-step, trip-based models

• Implications for improved models

• Categories of activity-based models

• Example of an activity-based model: TASHA

• Lessons learned

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Population & Employment

Forecasts

TransportationNetwork & ServiceAttributes

Link & O-D Flows,Times, Costs, Etc.

Four-Step, Trip-Based Models• The four-step, trip-based

approach has been the standard paradigm for urban travel demand modelling since the 1950’s.

• While clearly useful, it has been criticized from many perspectives, especially over the past 20 years.

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Criticisms of the four-step, trip-based approach• Aggregation issues

• Trips versus tours

• Trips versus activities

• Individuals versus households

• (Lack of) connection to auto ownership

• (Lack of) connection to residential & employment location choice

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Aggregation issues

• Spatial aggregation: trips are point-to-point, not centroid-to-centroid.

• Temporal aggregation: trips occur continuously over time, not chunked within arbitrary time periods.

• Socio-economic aggregation: trip-makers are very heterogeneous; their behaviour varies accordingly.

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Spatial aggregation

• Zone-based travel models have difficulty dealing with:– Short-distance trips– Walk/bicycle trips– Transit access/egress

• Transit LOS calculations are particularly sensitive to zone system design, etc.

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Transit access

Traffic Zone

Bus line

20 min

5 min10 min15 minBusstop

P(transit)

Walk time (min)

5 min 10 minZone Centroid

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Temporal aggregation

• Four-step models aggregate time into one or more discrete time periods. Each time period is analyzed separately.

• Difficult to:– Model peak-spreading

– Handle “spill-over” between time periods

• Computationally burdensome to model 24-hour travel.

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Socio-economic aggregation• Trip-maker attributes have a major affect on trip rates

by purpose, destination choice and mode choice.

• Matrix/zone/trip-based methods cannot keep track of individual trip-makers.

• Accounting for socio-economic heterogeneity using standard matrix-based data structures can add immensely to data requirements & computational effort.

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Travel behaviour varies with S-E attributes

-0.05 -0.04 -0.03 -0.02 -0.01

0 0.01

Fra

ction C

hange in P

auto

-0.5 0 0.5 1 1.5 2 Fraction Change in Auto Cost

Pa=0.10Pa=0.50Pa=0.90

Auto Cost ElasticityWorker Cat. 5 (DLIC , 2+ cars)

-0.4

-0.3

-0.2

-0.1

0

Trip E

lasticity

Work/School Other TripsTrip Purpose

Route

Transit

Total Trips

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Socio-Economic Aggregation Error

Income

P(transit|Income)

I1 I2Iavg

P1

P2

Pavg(Iavg, Pavg)

• Aggregate models estimated using average values will be biased (B1).• Inserting average values into a disaggregate model will yield biased results (B2).

P(transit|Iavg)B2

B1

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Trips versus tours

• We do not organize our lives in terms of individual trips but in terms of sequences/patterns of activities; these linkages can be important in determining travel behaviour.

• Non-work/school, non-home-based and ride-sharing are all difficult to model in a trip-based approach.

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time of day

x

y

home

work

shop

time of day

x

y

home

work

shop

travel betweenactivity locationsby transit

travel betweenactivity locationsby auto

Base Case 1: Shopping episode on the way home from work. Auto used for the daily activity pattern.

A transit improvement causes the person to shift to transit for the journey to/from work. In order to still go shopping, a new home-based auto-drive trip chain is generated. Auto usage & emissions will be under-estimated by a trip-based model.

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Base Case 2: Drop child at daycare on the way to/from work. Auto used for the daily activity pattern.

The transit improvement in this case does not result in a shift to transit for the journey to/from work, despite the good service provided for this journey, since the need to drop-off/pickup the child dominates the mode choice. Auto usage & emissions will be under-estimated by a trip-based model.

time of day

x

y

home

work

drop child at daycare

pickup childfrom daycare

time of day

x

y

home

work

drop child at daycare

pickup childfrom daycare

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Trips versus activities

• We travel to participate in activities, not for the sake of travel per se.

• It is argued that in order to understand travel we need to understand activity participation:– Timing/frequency of activities by type (trip

generation).– Location of activities (trip destination).

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Household-Level ModelsHousehold-level models are required to “properly” deal with many system components:

• housing location/type choice• automobile ownership• demographics/household structure/lifecycle stage• activity/travel scheduling

Households:• share resources among household members• constrain member behavior• condition member decision-making• generate activities

Household

Person 1 Person 2

Requests for resources,availability for tasks

Allocationof resources,assignment oftasks

Pers1 Pers 2 Car 1

Request forcar

Time

Allocation ofthe car to agiven person

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Auto ownership

• Auto ownership/availability has a major impact on mode choice.

• 4-step models often assume that auto ownership is an exogenous input.

• Improved urban form and improved transit services can reduce auto ownership and, hence, auto usage.

• Incorporating auto ownership in a trip-based, individual-based model system is difficult.

0

20

40

60

80

100

120

140

160

180

1 2 3 4+

Number of Vehicles per Household

Daily

VKT

per

GTA

Hou

seho

ld

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0-1000 1000-2000 2000-3000 3000-4000 4000-5000 5000-6000 6000+

Zonal Household Density (households/sq. km.)

Numb

er of

Vehic

les pe

r Hou

seho

ld

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Dynamics

VKT

TimeBaseYear

ForecastHorizon

HistoricalTrend

TrendProjection

Dynamic, path-dependentresponse to policyinitiatives

Static equilibriumprojection

Future system states are emergent outcomes of path-dependent processes. System equilibrium rarely exists.

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Summary of 4-step critique

• Virtually all of the criticisms of the 4-step paradigm involve recognition that the context within which travel decisions are made is critical: the details concerning who is deciding to travel under what circumstances determine the travel outcomes.

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Critique summary, cont’d

• Key issues that must be addressed:– Heterogenity– Non-linearity– Tour-based– Dynamics, path dependencies– Behavioural processes

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Heterogeneity

• Heterogeneity of decisions-makers and context combined with non-linearity of decision processes requires a disaggregate approach.

• Heterogeneity cannot be efficiently handled by matrix-based approaches – at some point, list-based approaches become much more efficient.

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Matrices Vs. ListsThe current 4-stage model for the Greater Toronto-Hamilton Area (GTHA) has 1717 zones, 5 worker classes, 4 occupation groups and 4 age categories for HBW mode choice calculations. The HBW matrix-based AM-peak mode choice model therefore requires :

1717x1717x5x4x4 = 235,847,120 evaluations of a nested logit model.

In 2006 there were approximately 2 million workers making morning peak-period trips within the GTHA.

It is far more efficient to construct a list of the 2 million workers and their attributes and compute their mode choices directly.

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Lists & Disaggregate Models• Adopting a list-based approach can significantly

improve computational efficiency.

• It also represents a major step towards an agent-based approach.

• Efficiency grows as the complexity of the system/agents being modelled grows (e.g., modelling both persons and households).

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Person ListPerson Age Sex Educ. Occ. Emp. ….ID Code Code Status….1207 36 M 4 1 FT ….….1354 32 F 5 2 PT ….….9623 6 F 1 -1 -1 ….….

Household ListHhld No. of No. of ….ID Persons Cars….663 3 2 ….….

Job ListJob Occ. Salary ….ID Code….623 2 $50K ….….9745 1 $65K ….….

Dwelling Unit ListDU Zone Price ….ID….345 2670 $245K ….….

School ListSch Type Zone ….ID….23 Primary 2669 ….….

Partial View of the ILUTE System State, Time T

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Tour-based models

• The only way to understand the relationship of one trip to another (and how one trip decision conditions/influences another) is by adopting a tour-based approach. NHB trips start to make some sense, conditioning of mode choices (e.g., cars need to return to the driveway), etc.

• Tours, in turn can only be explained in a behavioural sense by framing them within an activity-based, activity pattern/scheduling framework (tours to do what/when?).

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Dynamics• Activity patterns and tours are conditioned by the

order/timing in which decisions are made about what to do when. “High priority” / “inflexible” activities tend to be scheduled first, thereby providing constraints on subsequent decisions/trips. Travel occurs in “real time”; decisions at each point in time determine what actually happens at that point in time and condition subsequent decisions/actions.

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Dynamics, cont’d

• Learning / adaptation

• Memory / history

• Social networks / social influence

• Habit / experimentation

All are important in determining travel behaviour at any point in time.

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Activity-based process models• The need to participate in activities is what drives

travel demand: the primary benefit of travel is the accomplishment of out-of-home activities that generate income, utility, benefits, etc.

• Travel itself is an activity that requires time & money to undertake (like other activities), and, like other activities, generates (dis)utility in its execution.

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Implications

• Need a disaggregate, tour-based model of activity participation and travel.

• Implementation mechanisms:– Microsimulation– Agent / object based models

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Microsimulation

“Micro” implies a highly disaggregated model:• spatially• socio-economically (representation of actors)• representation of processes

“Simulation” implies:• numerical• dynamic (time dimension explicit)• stochastic• end state is “evolved” rather than “solved for”

t = t0

Synthesis of Base Sample

For t = t0

Endogenous Changes to

Sample during this t

DisaggregateBehavioral Model

Behavior/System Stateat (t + t)

Exogenous Inputsthis t

t = t + t

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Object-Oriented, Agent-Based Models• The Object-Oriented Programming (OOP) paradigm is the industry standard for modelling complex process.

• Objects have identity, state and behaviour.

• OOP ideal for microsimulation applications.

• OOP is more than a programming style, it provides a “language” for conceptualizing activity/travel models and a clear development process for moving from conceptualization to implementation.

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Agent-Based Modelling

Person 1

Agenda Schedule

Person 1

Agenda Schedule

Household Dwelling Unit Zone

Worker Job Firm

Building

Agenda

Vehicle

AgendaSchedule

An intelligent object is an agent. (“an object with attitude” – Paul Waddell). Agents:• perceive the world around them• make autonomous decisions• act into the world

Agents provide an efficient, highly extensible framework for modelling human socio-economic activity.

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Modelling Daily Activity & Travel

Many “activity-based” travel models currently exist worldwide. These can be loosely divided into two primary types:

• Tour-based models• Activity-scheduling models

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Tour-based models• Tour-based models are the most common form of

currently operational models.

• Key characteristics:– Focus on predicting the most common forms of daily

tours.– Heavy reliance on deeply nested logit models (RUM).– Disaggregate, microsimulation based– Generally not truly agent-based (or truly activity-based).

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Tour-based models cont’d• Several examples exist in the US and elsewhere.

• Originally derived from the seminal work of Bowman & Ben-Akiva in Portland (although the Portland model currently is not in operational use).

• Operational or near-operational models include:

– San Francisco

– Columbus, Ohio

– New York City (NYMTC) (trip-, not tour-based, but microsimulation)

– Atlanta

– Denver

– Los Angeles (under development)

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Activity-scheduling models• This class of models focuses on predicting out-of-home activities

and the associated travel required to execute these activities.• More truly “activity-based”. Tours emerge out of the scheduling

of activities.• Typically (but not always) explicitly agent-based.• Microsimulation-based.• A variety of modelling methods used (RUM, rule-based, etc.).• Typically quasi-operational (used in various policy studies but not

yet generally mainstream operational).

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Activity-scheduling models, cont’d• Examples include:

– ALBATROSS (Arentze & Timmermans, The Netherlands)

– TASHA (Roorda & Miller, Canada)– PCATS (Kitamura, Japan)– FAMOS (Pendyala, USA)– CEMDAP (Bhat, USA)

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TASHATASHA (Travel/Activity Scheduler for Household Agents) hasbeen developed at the University of Toronto. A validated version of the model is now operational.

It is an activity-based, agent-based, microsimulation model of weekday activity/travel in the Greater Toronto-Hamilton Area (GTHA). Key features include:

• Household-based• Activity scheduling• Treatment of tours and modes• Treatment of time• Flexibility in development and application

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Key Features 1: Household-BasedWorld

HouseholdsEpisode

DistributionsSpatial

Representation

Persons

PersonProjects

PersonProjectAgenda

IndividualActivity

Episodes

PersonSchedule

HouseholdProjectAgenda

Joint ActivityEpisodes

ZonesDistance

MatrixTravel Time

Matrices

HouseholdProjects

TravelEpisodes

Individual &Joint Activity

Episodes

Persons exist within households. This allows TASHA to deal explicitly with:• Vehicle allocation• Ridesharing• Joint activities/trips• Serve-dependent activities/trips

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Vehicle Allocation within TASHA

Work ShopPerson 1

ShoppingPerson 2

SchoolPerson 3

Person 1 Person 2 Person 3

Choose allocationwith highest totalhousehold utility

3 Conflicting With-Car Chains

3 Possible Vehicle Allocations

Allocation 1

Allocation 2

Allocation 3

Work ShopPerson 1

ShoppingPerson 2

SchoolPerson 3

Person 1 Person 2 Person 3

Choose allocationwith highest totalhousehold utility

3 Conflicting With-Car Chains

3 Possible Vehicle Allocations

Allocation 1

Allocation 2

Allocation 3

TASHA assigns household vehicles to drivers based on overall household utility derived from the vehicle usage. Drivers not allocated a car must take their second-best mode of travel.

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Household Ridesharing Options in TASHA

Home

Joint Activity

Joint TripHome

Joint Activity

Joint Trip

Home

Passenger’sActivity

Serve Passenger Trip

Passenger

Home

Passenger’sActivity

Serve Passenger Trip

Passenger

Home

Joint Activity 1

Joint Activity 2

JointTrip

JointTrip

JointTrip

Home

Joint Activity 1

Joint Activity 2

JointTrip

JointTrip

JointTrip

Home

Work

Joint Activity

JointTrip

Transit

Transit

Drive

Home

Work

Joint Activity

JointTrip

Transit

Transit

Drive

Home

Passenger’sActivity

Serve Passenger Trip

Serve Passenger Trip

Passenger

Home

Passenger’sActivity

Serve Passenger Trip

Serve Passenger Trip

Passenger

Home

Passenger’sActivity

Driver’sActivity

Serve Passenger Trip

DriveDrive

Passenger

Home

Passenger’sActivity

Driver’sActivity

Serve Passenger Trip

DriveDrive

Passenger

Within-household ridesharing is explicitly handled within TASHA. Drivers will “offer” rides to household members if a net gain in household utility is obtained and feasibility criteria are met.

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Joint Activities

…. ….

Day n

Person 1

…. ….

Day n

Person 2

Joint ShoppingActivity:Duration: 2 hrsLocation: The Mall

Search for feasiblejoint time slot

ILUTEServe Dependents

Daycare

At-Home

At-Home

Child’s Schedule

At-Home

At-Home

Adult 1 Schedule

At-Home

Adult 2 Schedule

Work

Shopping

Take child to/from daycare

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Key Features 2: Activity Scheduling

Project 1• episode 1.1• episode 1.2• ….

Project 2• episode 2.1• episode 2.2• ….

Project N• episode N.1• episode N.2• ….

Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7

…

TASHA is an activity scheduling model in which individual activity episodes are generated and then explicitly scheduled. Out-of-home activity patterns and their associated trip-chains (tours) are thus “built from scratch” rather than selected from a pre-specified set of feasible patterns. Thus, travel patterns dynamically adjust to changes in transportation level of service, activity system “supply”, changes in household and personal constraints and needs, etc.

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PDF

ActivityFrequency

ActivityFrequency

JointPDF

StartTime

FeasibleStart Times

StartTime

JointPDF

Duration

FeasibleDurations

(a) Draw activityfrequency frommarginal PDF

(b) Draw activity starttime from feasibleregion in joint PDF

(c) Draw activityDuration fromfeasible region injoint PDF

Activity Episode Frequency, Start Time and Duration Generation

At – Home

Work

Work

Shop 1 Shop 2

Other

Other

Work Project

School Project

Other Project

Shopping Project

Shop 1 At-homeOther Shop 2Person Schedule

= “Gap” in Project Agenda = Activity Episode = Travel Episode

At-home At-home

:

:

Scheduling Activity Episodes into a Daily Schedule

TASHA generates the number of activity episodes from a set of “projects” that a person (or household) might engage in during a typical weekday. It also generates the desired start time and duration of each episode.It then builds each person’s daily schedule, adjusting start times and durations to ensure feasibility.Travel episodes are inserted as part of the scheduling process.

Key Feature 3: Tour-Based Mode Choice

Chain c:1. Home-Work2. Work-Lunch3. Lunch-Meeting4. Meeting-Work5. Work-Home

m1m2

m3m4

m5

Non-drive option for Chain c

m1 = drive

Sub-Chain s:2. Work-Lunch3. Lunch-Meeting4. Meeting-Work

m2m3

m4

Non-drive for Sub-chain s

m2 = drivem3 = drivem4 = drive

Drive forSub-chain s

m5 = drive

Drive Option for Chain c

mN = mode chosen for trip N

TASHA’s tour-based mode choice model:• Handles arbitrarily complex tours and sub-tours. without needing to pre-specify the tours• Dynamically determine feasible combinations of modes available to use on tours. Modes can be added without changing the model structure.• Cars automatically are used on all trips of a drive tour.

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Key Feature 4: Treatment of Time• Models all out-of-home activities and trips for a 24-hour typical

weekday• 5 minute time increments are used for start times and

durations/travel times– Provides great temporal detail but is computationally very efficient

(integer storage & calculations)

• Trips can be aggregated to whatever level of temporal detail/categorization is required by the network assignment model

• Deals naturally with “peak-spreading”, etc.• Provides excellent detail for environmental impact analysis

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Key Feature 5: Flexibility• TASHA has been designed to be very flexible in terms of its

development and its application.• It has been developed using ordinary trip-based survey data for

the GTA (but it could also exploit activity-based survey data).

• It can be used as a direct replacement for the first 3 stages in a 4-step system, or integrated within a full microsimulation model system.

The data requirements for model development are no greater than other current models, including conventional trip-based models.

Usable in a variety of contexts, and facilitates the evolution of the model system over time from aggregate to microsimulation.

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Application in a conventional settingPop & Emp

by zone

Synthesize persons, hhlds& work/school locations

TASHA

Standard zone-based, staticroad & transit assignment

Standard 4-step zone-based inputs

Standard network assignment package

TASHA contains its own synthesis procedures to convert aggregate, zone-based inputs into disaggregated persons, etc. required for microsimulation

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Base Year Census Data,Other Aggregate Data

Synthesize Base Year Population,Employment, Dwellings, etc.

ILUTE Evolutionary EngineFor T = T0+1,T0+NT do:• Demographic Update• Building Stock Update

• Residential Housing• Commercial Floorspace

• Firm/Job Location Update• Household Composition Update• Work/school Participation & Location Update• Residential Location Update• Auto Ownership Update

Exogenous Inputs, Time T• In-migration• Policy changes• …

Dynamic Network Assignment Model(meso- or micro-scopic)

T0 = Base time pointT = Current time point being simulatedNT= Number of simulation time steps

Travel Models• Commercial Vehicle Movement Update• Activity/Travel Update (TASHA)

Application in an full microsimulation setting

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Current Status

• TASHA was developed using 1996 travel survey data for the GTHA.

• The activity scheduler has been validated against 2001 survey data.

• Interfaces with both EMME and MATSIM.

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2001 Validation Results

Figure 2.27Activity Frequency: 2001 Work

02468

10121416182022242628

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

ActivityStartTime: Time of Day_Hour

% o

f 24

-hr

Fre

qu

ency

TASHA

TTS

Figure 2.37Average Trip Distance: 2001 Work

0

4

8

12

16

20

24

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

ActivityStartTime: Time of Day_Hour

Ave

rag

e D

ista

nce

_Km

TASHA

TTS

Figure 2.32Activity Duration: 2001 Work

5

105

205

305

405

505

605

705

4 6 8 10 12 14 16 18 20 22 24 26 28

Hour

Du

rati

on

_m

inu

es

TASHA

TTS

Activity TASHA-Total Freq

TASHA-% Freq

TTS- Total Freq

TTS - % Freq

TASHA-AvgDist-km

TTS-AvgDist-km

Home 266345 45.48 286125 45.19 9.21 9.21Marketing 46414 7.93 54550 8.62 5.87 5.47Others 84914 14.5 100452 15.86 7.97 8.93School 42518 7.26 44190 6.98 6.42 5.45Work 145388 24.83 147860 23.35 13.56 13.04

Total 585579 633177

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Current status, cont’d• TASHA has been used for several environmentally

related studies in the GTHA. Currently it is being used to predict transportation energy use for the West Don Lands development in the Toronto Waterfront.

• Currently being tested for transferability to Montreal and London, UK.

• A joint UofT – Georgia Tech study is using TASHA to investigate the statistical properties of large microsimulation models.

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In a “Business as Usual” scenario with respect toGTA growth and transit system investment, autousage is projected to grow faster than population;transit usage will grow at about half the rate ofpopulation.

% Change in Population & Employment, 1996-2031

55.8

88.2

0.010.020.030.040.050.060.070.080.090.0

100.0

1

Population

Employment

% Change in Daily Trips, 1996-2031 by Mode

68.8 67.6

34.3

105.7

61.9 66.6

101.2

42.5

64.2

0.0

20.0

40.0

60.0

80.0

100.0

120.0

1

Drive

Pass

Transit

GO-Rail

Walk

Cycle

Sch-bus

Taxi

Total

Pop. Growth Rate

% Change in Daily VKT & Emissions, 1996-2031

6875.0 78.3

65.0

95.2

75.1

0102030405060708090

100

1

VKT

CO2

CO

NOx

HC

Fuel

Pop. Growth Rate

TASHA Application: GTHA Growth & Transportation Impacts

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Environmental Modeling with TASHA• TASHA has been connected with:

– EMME/2 road & transit network assignment model (link speeds & volumes by hour of day)

– MOBILE6.2C emissions model (link emissions by type by link by time of day)

– CALMET meteorological model– CALPUFF dispersion model (pollutant

concentrations by zone by time of day)

Dynamic population exposure to pollution by zone by time of day.

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Persons& Households

Auto & TransitTravel Times/Costs

TASHA Activity/TravelScheduler

ActivityPatterns &

Trip Chains

Trips By Mode,Vehicle Type &

Time of Day

TransportationNetwork Model

VKT by FacilityType, etc.

Hot/Cold Soaks,Cold Starts, etc.

Emissions Model

Mobile SourceEmissions

DispersionModel

Locations ofPeople by

Time of Day

Exposure toPollution

Household AutoOwnership Model

Vehicle AllocationModel

Land Use Policies Vehicle TechnologyTransportation Policies

(Road pricing, carbon taxes, transit investment, etc.)

EXAMPLE INTERVENTIONS

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Auto Emissions by location and time of day

Link-based running emissions by time of day

Zone-based soak emissions by time of

day

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Dispersion of Emission Concentrations

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Zone NO2 Exposures

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TASHA-MATSIM

More recently TASHA has been linked with MATSIM, an agent-based micro/meso-scopic network simulator.

MATSIM allows us to keep track of individual agents as they travel through the network so we can accumulate their emissions (and, eventually, their exposure to pollutants).

It also provides us with rudimentary vehicle dynamics, allowing a more detailed calculation of vehicle emissions.

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Emissions by hour

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Emissions by hour

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Evolution of idle emissions

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Lessons Learned

• Microsimulation simplifies many calculations, since one has a much more precise representation of the problem being modeled.

• The household-based approach also both simplifies and facilitates many calculations:– Auto-availability, auto allocation– Ridesharing– Joint travel– Serve-dependents

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Lessons Learned, cont’d:

• Tour-based models provide the “proper” framework for:– Handling non-home-based travel– Modeling travel across time periods (peak / off-peak)– Consistent handling of auto usage

• Activity-based, microsimulation is ideal for environmental impact modeling

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Lessons Learned, cont’d

• These gains in model performance can come at very little additional complexity, data requirements or computational burden.

• 4-step models are complex, messy and computationally burdensome – we are just used to them!

• Agent-based microsimulation models are easier to explain to policy-makers and are both behaviourally and computationally “cleaner” in their structure.

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Lessons Learned, cont’d

• Activity-based models, however, do not “automatically solve” all problems:– Activity/trip generation still typically crude– Non-work/school destination choice a weak link in all model

systems, trip- or activity-based– Network assignment is still typically computationally

burdensome for 24-hour modeling

• BUT, the activity-based paradigm provides a much more suitable framework for addressing these issues than the 4-step approach!

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THANK YOU!