building and learning activity-based models

Building and Learning Activity-Based Models

Erik Sabina Jennifer Malm

Suzanne ChildressJohn 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 – 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

• 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

• 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

What (example) - non-motorized mode choice

• Blend point-based and skim distances

• Point locations for households and jobs

• Intersection, retail, mixed use densityHow – programmed all utility function

variablesWhy – can’t apply model without it!

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

interactionsHow – programmed all utility function

variablesWhy – know what model can/can’t do

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

//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

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)

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

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 issuesOops! 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-estimateHigh School Location Choice

Row Parm ID Variable Student Grade HH Income Est. Std. error T-stat1 3 Zone in School District 9-12 1.513 0.156 9.72 12 Mode Choice Logsum 9-12 0.064 0.070 0.93 16 Sibling's School Zone 9-12 3.794 0.365 10.44 20 Piecewise Linear Distance <2 miles 9-12 -0.435 0.131 -3.35 21 Piecewise Linear Distance 2 - 6 miles 9-12 -0.702 0.044 -16.06 23 Piecewise Linear Distance >6 miles 9-12 -0.189 0.019 -10.0

Row Parm ID Est. Std. error T-stat4 0 SIZE MULTIPLIER 0.83 0.072 11.63Row Parm ID Student Type HH Income Est. Std. error T-stat

5 103 Zonal Education Employees 9-12 -3.978 0.573 -6.946 112 Number of Households in the Zone 9-12 -7.695 0.925 -8.317 150 Public High School Enrollment 9-12 <$75K 0.000 0.000 0.008 151 Public High School Enrollment 9-12 >$75K 0.000 0.000 0.009 152 Public High School Enrollment 9-12 Refused 0.000 0.000 0.00

10 153 Private High School Enrollment 9-12 <$75K 0.290 0.277 1.0511 154 Private High School Enrollment 9-12 >$75K 0.546 0.354 1.5412 155 Private High School Enrollment 9-12 Refused -5.000 0.000 0.0013 172 Service Employment 9-12 -8.451 1.392 -6.07

Summary StatisticsNumber of Observed Choices 603Number of Estimated Parameters 13Log Likelihood with Coeffs= 0 -4783Final Log likelihood -1238Rho-Squared 0.7411Adjusted Rho-Squared 0.7384

Size Variables

Non-Size Variables

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

What (examples) – • Model component design• Upgrade key functions (e.g.

MakeChoice)• Enhance distributability

( modifications to“plumbing” code)How – designed/wrote most code- The “coach” modelWhy – open source versus vendor

again!

Redesign Software (example)

What we did: estimation

Component NameSynthetic Sample Generator Tour Primary Destination

ChoiceRegular Workplace Location

Regular School Location Tour Main Mode Choice

Auto Ownership

Area Type Tour Time of Day Choice

Parking Cost Intermediate Stop Generation

Daily Activity Pattern Intermediate Stop Location

Trip Mode ChoiceExact Number of Tours Trip Departure Time

Work Tour Destination Type

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 databaseWanted to use industry-standard toolsLed by strong IT department

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

Erik Sabina, esabina@drcog.orgJennifer Malm, jmalm@drcog.orgSuzanne Childress, Schildress@drcog.org- Denver Regional Council of GovernmentsJohn Bowman, J

ohn_L_Bowman@alum.mit.edu- Bowman Research and ConsultingDeVon Culbertson,

DevonCulbertson@yahoo.com- DeVon Culbertson, LLC