Evaluating Transportation Impacts of Forecast Demographic Scenarios Using Population

Synthesis and Data Simulation Joshua Auld

Kouros MohammadianTaha RashidiKermit Wies

The 12th TRB National Transportation Planning Applications ConferenceMay 19, 2009

Overview

Introduction

Population Synthesis

Forecasting Marginal Variables

Travel Data Simulation Model

Scenario Analysis

Conclusions

Introduction

Introduction

Travel Demand Forecasting:– Typically done at long time horizons (20, 30 year, etc.)– Need forecast demographics to forecast demand– Many ways to do so (expert opinion, trend lines, land-use

models, etc.)

Move to activity based models:– Require synthetic populations– Used as agents in the ABM simulation– Travel patterns of all agents summed to give demand

Data requirements for population synthesis– Household/Individual sample data – joint distribution– Marginal data – small area distributions of single variables

Introduction (continued) For forecast synthetic populations:

– Same data requirements as base year– Data often nonexistent, no data 30 years in future

Solutions for data problems:– Usually use base year sample directly as seed– Update base year marginals– This gives closest population distribution to base year that

matches forecast marginals

Forecasting marginals can be done in several ways– Full, integrated land-use model (UrbanSim, PECAS, etc.)– Proportional updating (assume same marginal distributions)

Common approach for many agencies

Introduction (continued) Our approach:

– Combine forecasting models, expert opinion / scenario analysis and proportional updating

Forecasting models:– Estimate marginal distributions for household size, number

of workers– based on limited information (number of households and

employees per zone)

Expert opinion/scenario analysis– For marginals of interest that are difficult to predict– Allow marginals to be varied by analyst– Easy-to-use scenario definition tool, direct manipulation of

marginal distributions

Useful where forecast information is limited

Objectives of Current Work

To demonstrate:– Use of a flexible population

synthesizer/scenario evaluation tool– Combined forecast population with data

transferability model – synthesize forecast travel attributes

– Demonstrate impact of forecast population changes on several travel demand variables

– NOT to make realistic travel demand/demographic predictions (left to planning agency)

Population Synthesis Program

Base Population Synthesis Program

Link sample data geography to marginal data

Choose up to six control variables

Define the categories (link btw. sample data and marginal data

Apply weighting

Specify test variable– Estimate the fit of various

forecast populations

Forecasting Control Variables

Input base and forecast year required zonal data

Link control variable categories to forecast categories– 4 HHsize, 3 numworkers

Generate forecast marginals:– Proportional updating, or

– Forecast model

Scenario Definition Select sub-regions to apply

changes Select control variable to

modify Adjust variable marginal

distribution Multiple selections, modified

variables allowed

Forecasting Control Variable Distributions

Forecasting

Forecasting often done by proportional updating– Assume same marginal distribution in forecast year

However, marginals change over time– i.e. changes in pop, households, housing, etc. lead to

changes in household size– Can see in Census data, marginal dist. not constant– Distribution of each marginal should therefore change

Need model of marginal changes– Only for certain variables (HH Size and Number of Workers

in this study)– Need data that drives marginal changes– Income, race, etc. changes not modeled – done through

scenario definition

SURE Forecasting Model

SURE marginal changes forecasting model:– System of linear regression equations

– Related only through correlated error terms

– Accounts for cross equation correlations

– d(hh,emp) -› dhhsize=1, dhhsize=2, etc.

– Estimate change in hhsize and num workers categories

Model specification:

IEE

xy

xy

xy

ijjii

NiNiNNi

iii

iii

;0

2222

1111

Dependant variables are change in HH in each category:– HHsize=1, HHsize=2, HHsize=3-4, HHsize=5+– NumWorkers=0-1, NumWorkers=2+, NumWorkers=NA (non-family)– All dependent variables normalized by base year total HH– i.e. change in HHsize=i per base year household

Independent Variables include:– Total households in zone, base and forecast– Total employment in zone, base and forecast– Household Density, base and forecast– Base year demographics– Base year land use mix: (% of area devoted to Single Family)– Job accessibility (base and forecast – base year LOS/mode split)

SURE Forecasting Model:Explanatory Variables

SURE Forecasting Model:HH Size Results

MODEL: D HHS1 / HHbase

D HHS2 / HHbase

D HHS3-4 / HHbase

D HHS5+ / HHbase

Constant 0.032 0.017 -0.013 -0.037D HHtot / HHbase 0.076 0.112 0.151 0.057(%HHS=i) x D HHtot / HHbase 0.604 0.603 0.604 0.603D J OBS/HH) 0.050 -- -0.032 -0.018(HH DENSITYbase) -5.71E-07 -- -- 5.71E-07D HH DENSITY) 3.19E-05 -- -2.19E-05 -1.00E-05(%SINGLEbase) -- -0.015 -0.015 0.030(%RACE_OTHERbase) -0.130 -0.096 0.057 0.168

R2 0.68 0.80 0.88 0.55

SURE Forecasting Model:Number of Workers Results

MODEL D NWORK0-1 / HHbase

D NWORK2+ / HHbase

D NWORKna / HHbase

Constant 0.048 -0.043 -0.005D HHtot /HHbase 0.270 0.656 0.026

(%HHnwork=i) x D HHtot /HHbase 0.047 0.047 0.047D (HHsize = 1) / HHbase -0.415 -0.632 1.048D J OBS/HH) 0.037 -0.028 -0.009% BLACK 0.020 -0.020 0.000% OTHER -0.028 0.028 0.000HH DENSITYbase -7.33E-06 5.54E-06 1.79E-06D HH DENSITY) 4.41E-05 -5.22E-05 8.12E-06(J OBS/HH)base -0.020 0.011 0.008

R2 0.66 0.92 0.92

SURE Forecasting ModelValidation Validation run for HHsize and NWork models

– Run using unseen data (1980)– Validation forecast: 1980 to 2000– Compared against results from proportional updating

Shows moderate improvement (~10%) in R2, RMSE

HHSize Validation:

NumWorkers Validation:

Base Year Forecast Year RMSE R2RMSE R2

RMSE R2

1990 2000 107 0.77 119 0.72 11% 7%1980 2000 138 0.65 150 0.59 9% 11%

Model Proportional % Improvement

Base Year Forecast Year RMSE R2 RMSE R2 RMSE R2

1990 2000 79 0.75 89 0.68 13.3% 10.4%1980 2000 110 0.65 127 0.53 15.9% 23.5%

Model Proportional % Improvement

Travel Data Simulation Model

Data simulation overview

Objective– Quick alternative to travel demand model– Generating joint disaggregate travel data

at household level– Transfer data from NHTS to synthetic

population

Travel Attributes– Household Total Trips per Day– Household Mandatory Trips per Day– Household Maintenance Trips per Day– Household Discretionary Trips per Day– Household Auto Trips per Day

Total Trip

Auto TripMandatory

Trip

Maintenance Trip

Discretionary Trip

Data simulation overview Travel attributes generating models

– 32 explanatory variables are employed including (NHTS, TIGER files):

– Household socio-demographic characteristics. E.g.– Age– Income– Occupation– Education– Ethnicity– ….

– Built-environment variables. E.g.– Residential density– Intersection density– Transit Use– …

Data simulation model

Travel attributes generating models– Models are decision trees with a maximum of three depth

levels – Decision trees were tested against the observed travel

data for Des Moines add-on data and they provided good fits

Simulation Model Validation

Travel attributes generating models– Probability density functions for observed, transferred

and national household total number of trips per day in Des Moines area

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0 10 20 30 40 50 60

Transferred

Obsereved

National

Analysis Results

Scenarios Analyzed

Base year, Forecast year and two scenarios analyzed for six-county Chicago region

Four different synthetic populations generated– BY: 2000 (base year)– FY: 2030 (forecast year)– S1: 2030 High Ageing– S2: 2030 High Ageing in Suburbs, Lowered Age in Chicago

Travel data indicators simulated for each scenario

Scenario Marginal Distributions

Scenario 1: High Ageing

0

5

10

15

20

25

30

15 25 35 45 55 65 75 85

Original Scenario

Scenario 2: Increased Youth in Chicago

0

5

10

15

20

25

30

35

40

15 25 35 45 55 65 75 85

Original Scenario

Scenario 2: High Ageing in Suburbs

0

5

10

15

20

25

30

15 25 35 45 55 65 75 85

Original Scenario

Selected scenario analysis results

Change in Total Trips/HH for S1 and S2 compared to FY:

Increase No change Decrease

Selected scenario analysis results Change in Discretionary Trips / HH for S1 and S2 compared to FY:

Increase No change Decrease

Selected scenario analysis results Change in Auto Share for S1 and S2 against FY

Increase No change Decrease

Scenario Analysis Results Aggregate results for whole region, Chicago and suburbs:

– Ageing decreases total trips, increases auto share overall– In Chicago, increased aging and decreased aging both increase auto share

Total Trips Mandatory Maintenance Discretionary Auto ShareBY 11.38 1.76 3.09 2.52 89.6%FY 11.32 1.76 3.07 2.50 90.0%S1 10.60 1.57 2.85 2.40 90.7%S2 10.30 1.54 2.76 2.34 91.0%

Daily Trips Mandatory Maintenance Discretionary Auto ShareBY 11.05 1.69 2.98 2.47 85.5%FY 10.79 1.66 2.90 2.40 85.6%S1-high ageing 10.46 1.52 2.82 2.36 86.3%S2-low ageing 10.84 1.63 2.89 2.44 86.0%

Daily Trips Mandatory Maintenance Discretionary Auto ShareBY 11.47 1.78 3.12 2.54 90.7%FY 11.45 1.78 3.11 2.53 91.0%S1-high ageing 10.64 1.59 2.86 2.41 91.7%S2-higher ageing 10.17 1.51 2.73 2.31 92.3%

Whole Region - Average Per Household

Chicago

Suburbs

Conclusions

Conclusions and Discussion

Flexible, easy to use scenario analysis tool– Few limitations on geography/analysis variables

Allows:– Accurate forecast, with minimal info requirements– Quick scenario visualization/analysis– Apply different scenarios to different sub-regions

Useful for:– 4-step travel demand – reduce agg. bias– ABM – synthesize agents for microsimulation

Thank You!

Questions?