Building and learning activity based models
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Building and Learning Activity-Based Models. Erik Sabina Jennifer Malm Suzanne Childress John Bowman DeVon Culbertson. Erik’s recommendations (and the price you’ll have to pay). Get into the critical path Though it will put the hurt on schedule Software is the hardest part

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Building and Learning Activity-Based Models

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Building and learning activity based models

Building and Learning Activity-Based Models

Erik Sabina

Jennifer Malm

Suzanne Childress

John Bowman

DeVon Culbertson


Erik s recommendations and the price you ll have to pay

Erik’s recommendations (and the price you’ll have to pay)

  • Get into the critical path

    • Though it will put the hurt on schedule

  • Software is the hardest part

    • Steal someone else’s if you can

    • Modelers must program WELL!

    • The custom versus vendor conundrum

  • Estimate a few models

    • Some basic skills are indispensible

    • Why let a few consultants have all the fun?


Expertise we started with

Expertise we started with

  • Discrete choice model estimation

    • Decent theory, limited practice

  • Programming/software development

    • Very strong in IT, modelers good programmers

  • Math, stats, econ, etc.

    • Pretty strong across the board

  • Trip-based modeling

    • Very strong


Levels of knowledge

Levels of Knowledge

  • Sensitivities

  • Limitations and approximations

  • Fix/update variables

  • Re-calibrate

  • Re-estimate some components

  • Re-design the theoretical structure

  • Re-design or extend sw structure


Model sensitivities

Model sensitivities

What (example) - non-motorized mode choice

  • Blend point-based and skim distances

  • Point locations for households and jobs

  • Intersection, retail, mixed use density

    How – programmed all utility function variables

    Why – can’t apply model without it!


Limitations and approximations

Limitations and approximations

What (examples) -

  • Little: simpler logsums skims

  • Little: Twice O-D, rather than O-D + D-O

  • Big: sequential TOD and mode choice

  • Big: Implicit intra-household interactions

    How – programmed all utility function variables

    Why – know what model can/can’t do


Fix update variables

Fix/update variables

What (examples) –

  • Simple: changes to input data/ GISDK

  • Simple once you get used to it - change SQL Server table / variables in C#

  • Pretty hard - understand how component works (older sibling school, logit solver)

    How – wrote GISDK/ C# model components

    Why – changes to variables over time are very common


Variable example

Variable example

//Additional created in code variable, Older Sibling's school district

if (householdID != currentHouseholdID)

{

currentHouseholdID = householdID;

//Clear Sibling's school zone variable

foreach (string choice in OlderSiblingSchoolZoneChoice)

{

this.UtilityFunctionParameters.AlternativeSpecificVariables[choice]["OldSibSchZone"].Value = 0;

}

OlderSiblingSchoolZoneChoice.Clear();

}

else

{

//take choice and add a 1 to the OldSibSchZone variable in the choosen zone.

tempList = choosenAlternative.Split(' ');

zoneID = int.Parse(tempList[1]);

if (universityStudent != 1) //Doesn't matter where an older sib university student went to school

{

if (zoneID != 0)

{

this.UtilityFunctionParameters.AlternativeSpecificVariables[choosenAlternative]["OldSibSchZone"].Value = 1;

}

}

}


Variable changes in database

Variable changes in Database


Recalibrate

Recalibrate

What (example) - work location choice model calibrated five years ago is not matching new ACS journey to work data. Model has too few people working at home.

What knobs can you turn?

Some Examples:

1. Change coefficients to variables related to working at home.

2. Trace back issues to the land use or economic forecasts.

How – checked model estimation reports, programmed variables and model input code

Why – frequent “tweaks” expected!


Recalibrate 2

Recalibrate (2)

Another example -

Observed boardings on light rail are lower than modeled boardings.

What knobs can you turn?

Possibilities:

1. Mode choice coefficients / alternative specific constants.

2. Too few university students on light rail?

Trips too short, so walk/bike mode share too high.

Adjust school location choice model: coefficients on the distance to school variables.


Re estimation

Re-estimation

What (example) –added variables to school location choice:

  • Zone in School District

  • Older Sibling’s School Zone

    How – estimated several models ourselves:

  • Estimation software syntax

  • Various levels of theoretical knowledge

  • The “expert coach” approach

    Why – expecting frequent “tweaks” again!


Re estimate data issues

Re-estimate: data issues

Oops! You need to re-estimate. The colors on the picture signify where education employment was geo-coded but there was no school enrollment geo-coded.


Re estimate high school location choice

Re-estimateHigh School Location Choice


Redesign model

Redesign model

What (examples) -

  • Swap out a component

  • Estimate joint models

  • Explicit joint tour formation

  • Daily schedule interaction

  • Activity generation and assignment

    How – thorough study of your design (among other things!)

    Why – next round of model upgrades


Redesign software

Redesign software

What (examples) –

  • Model component design

  • Upgrade key functions (e.g. MakeChoice)

  • Enhance distributability ( modifications to“plumbing” code)

    How – designed/wrote most code

  • The “coach” model

    Why – open source versus vendor again!


Redesign software example

Redesign Software (example)


What we did estimation

What we did: estimation


What we did software

What we did: software

Wrote our own (bigtime!)

  • That coach model again

    Had to learn a lot to do it:

  • Logit math

  • OO/C#/.Net programming

  • SQL-Server database

    Wanted to use industry-standard tools

    Led by strong IT department


Future training

Future training

  • Explicit intra-hh interaction

    • Many flavors

  • Selection of choice set from a larger set

  • “Doubly constrained” location choice

    • Enforced quota

    • Shadow price

  • Ties to DTA

  • Enhancements in population synthesis


Authors

Authors

Erik Sabina, [email protected]

Jennifer Malm, [email protected]

Suzanne Childress, [email protected]

  • Denver Regional Council of Governments

    John Bowman, [email protected]

    - Bowman Research and Consulting

    DeVon Culbertson, [email protected]

    - DeVon Culbertson, LLC


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