1_6_Michael Clarke_Futura Oceania Moving to Activity Modeling

7/31/2019 1_6_Michael Clarke_Futura Oceania Moving to Activity Modeling

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An Activity-Based Model forCube Voyager


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Agenda

Background & motivation

Structure of the model

Scripting features Application case study


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One (extremely common) method of forecastingtravel demand

Trip ends (productions and attractions) aregeneratedbased upon socio-economic anddemographic factors

These are distributedbetween zones basedupon aggregate travel costs

Logit models are used to split person tripsbetween different travel modes

Trips by mode are factored by time of day andassigned to specific network paths

Modern versions of this process feedback costsfrom assignment to earlier steps

The Four-Step Modeling Process

TripGeneration

TripDistribution

Mode

Choice

NetworkAssignment


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With person-trips as the unit of analysis:

No interactions between trips made in the same trip chain

No interactions between trip chains made during the same day

No interactions between the trips made by people in the same household

Spatial aggregation of trips: Trip origins and destinations modeled as if they are located at the same point in

space

Demographic aggregation:

All households within a given zone are treated as identical or segmented along afew dimensions

Temporal aggregation:

Only a few periods of the day are considered

Proportion of trips made in each period treated as constant

Limitations of Trip Based Models


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Activity Based Models

Early recognition that travel is a derived demand derived from a persons desire to engage in activities that are spatially

separated

Focus of the model should be on the underlying behavior: What peoplewant to do, not where people want to go

Early attempts at implementing tour based models

San Francisco Bay Area, The Netherlands, Boise Idaho, Stockholm, NewHampshire, Italy

Current implementations of activity-based travel demand model systems in

USA Portland, San Francisco, New York City, Columbus, Atlanta

Underway: Baltimore, Jacksonville, Chicago, San Diego


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Auto OwnershipModel

Activity Day-Pattern Choice

Tour Generation& Time-of-Day

JointMode/Destination

Choice

Alternative to four-step modeling approach

Popular in the academic transportation researchcommunity and becoming more common in practice(although still much less than 4-step)

Disaggregate simulation using synthetic populations

based upon micro-data

Complete tours, or chains of trips, are analyzed, ratherthan individual trips

e.g. Home > Work > Shop > Home

Activity location and scheduling models

Mode choice applies to entire tour

Ideally suited for dynamic traffic assignment and meso-simulation

Activity and Tour Based Modeling


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Motivation for the Work

What?

An activity-based microsimulation model implemented completely in CubeVoyager scripting language (no external code)

Why?

A learning tool for (potential) model users

A forecasting tool for small/medium applications

A test bed for model developers


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Most existing activity-based models are custom programs written byconsultants in third-party programming languages

Examples: Java, C++, C#, Python, R

Steep learning curve to develop & maintain

Relatively difficult to scale the model to match resources

Using Voyager instead offers significant advantages:

Understandable to non-programming modelers = easy to learn & use

Easily scalable (using Cube Cluster for distributed processing)

Data models, not object models = less complex code Model structure is transferable, not agency- or consultant-specific

Why code in a Cube Voyager script?


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The Model System Structure

1. Population synthesizer

2. Zonal accessibility measures

3. Activity and travel simulator

4. Travel aggregator

5. Traffic assignment

6. Feedback loop / equilibration

For background on theoretical development of model structure see:

Bowman, John L. and Mark A. Bradley (2005)Disaggregate treatment ofpurpose, time of day and location in an activity-based regional travelforecasting model, European Transport Conference, October 2005,

Strasbourg, France.
http://jbowman.net/papers/2005.Bowman_Bradley.Disaggr_treatment_of_purp_time_loc.pdfhttp://jbowman.net/papers/2005.Bowman_Bradley.Disaggr_treatment_of_purp_time_loc.pdf


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Uses household and personrecords from PUMS 5%microdata

Uses Census Table CTPP1-75,by TAZ

HH size(1, 2, 3, 4+)

HH income

(0-15K, 15-30K, 30-50K, >75K) Draws households randomly

from PUMA to match marginaldistribution in each TAZ

Simple Population Synthesizer


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6 highway variables

SOV distance, time and toll

HOV distance, time and toll

5 transit variables

Walk access/egress time

First wait time

Transfer time

In-vehicle time

Fare

4 time periods AM peak, Midday, PM peak, Off-peak

Highway and transit networks


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Aggregate mode/destination choice logsums: 3 travel purposes

Work (total employment)

School (K-12 enrollment)

Other (retail employ. + serviceemploy./2)

4 times of day

AM peak, Midday, PM peak, Off-peak

2 directions

Traveling away from zone, returning to

zone 2 car availability situations

With SOV available, without SOVavailable

Zonal accessibility measures


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Main loop on households Household car ownership model

Loop on people in household

Full day tour/trip activity pattern choice Loop on tours in the day

Tour time of day choice models(both directions)

Tour main mode and destination choice

Loop on trips in the tour Intermediate stop location choice model

Trip mode choice(usually same as tour mode)

Write trip record(with tour, person and HH info)

Activity and Travel Simulator


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Aggregates records to create tripmatrices by:

4 time periods

AM peak, Midday, PM peak, Off-peak

4 modes

SOV, HOV, transit, walk

Flexible to allow other

breakdowns, e.g.: Separate assignment by income class

Travel Aggregator


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Uses CUBE Voyager highwayassignment

3 separate assignments: AM

peak, Midday, PM peak

Off-peak LOS uses uncongestedspeeds.

Traffic assignment


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Coded by Victor Siu & Ken Vaughn

Used the Cubetown demo networks

and zonal files

System of 25 zones and highway,

transit networks, based on an area of

Fargo, ND

Synthetic population of 70,006

persons, based on 1990 CTPP datafor similar zones

Ran 4 full iterations with assignment

Initial application in Cubetown


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15 min. per full iteration on 3 GHZ PC

Travel/activity simulator run time mainly proportional tosize of synthetic population

Traffic assignment proportional to square of number ofzones

4 full iterations with 500,000 people would take 7 hours +,depending on number of zones

Observation: script could be optimized to run faster

Initial Performance


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Re-implementation using DBI

Simple population synthesizer

Enhanced integration:

Cube Cluster

Model Catalog

Geodatabase inputs

GIS Mapping

Cube Reports

Added to 5.1 Cubetowndemonstration model set

2009 Update to Activity-Based Model


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Get it at www.citilabs.com/tutorials.html

Model Catalog

Tutorial
http://www.citilabs.com/tutorials.htmlhttp://www.citilabs.com/tutorials.html


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Application Structure in Cubetown 5.1


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Use of DBI vs. RECI:

70-80% Runtime Reduction Factor (RRF)

Two cores vs. one:

55-65% RRF

Four cores vs. one:

30-40% RRF

Performance gains from additional cores will depend upon

structure and size of population, zone system

Update: Performance Improvements


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As a learning tool A quick and easy way to learn about the

properties of activity-based microsimuation

Sensitivity tests on a wide range of policies

Reporting on several levels and variables(network, trip, tour, person, household)

Practical context for advanced Cube Voyager functions

Further development

Further standardize data model & parameters

Explore benefits of multi-dimensional arrays in 5.1


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As a forecasting tool Provides many advantages over 4-step

The framework is feasible for small and medium-sized regions.

You can always integrate custom programs withCube Voyager (e.g. for large regions) if preferred

Further development

Calibrate and validate on region-specific data

Transfer to other regions(structure and many parameters should be transferable)

Continue to improve run-time performance


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Helping You to Create a Better Future

1_6_Michael Clarke_Futura Oceania Moving to Activity Modeling

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