evaluating transportation impacts of forecast demographic scenarios using population synthesis and...
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![Page 1: Evaluating Transportation Impacts of Forecast Demographic Scenarios Using Population Synthesis and Data Simulation Joshua Auld Kouros Mohammadian Taha](https://reader035.vdocuments.mx/reader035/viewer/2022062518/56649f0b5503460f94c1e928/html5/thumbnails/1.jpg)
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
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Overview
Introduction
Population Synthesis
Forecasting Marginal Variables
Travel Data Simulation Model
Scenario Analysis
Conclusions
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Introduction
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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
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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
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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
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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)
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Population Synthesis Program
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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
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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
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Scenario Definition Select sub-regions to apply
changes Select control variable to
modify Adjust variable marginal
distribution Multiple selections, modified
variables allowed
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Forecasting Control Variable Distributions
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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
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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
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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
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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
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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
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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
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Travel Data Simulation Model
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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
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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– …
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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
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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
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Analysis Results
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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
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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
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Selected scenario analysis results
Change in Total Trips/HH for S1 and S2 compared to FY:
Increase No change Decrease
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Selected scenario analysis results Change in Discretionary Trips / HH for S1 and S2 compared to FY:
Increase No change Decrease
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Selected scenario analysis results Change in Auto Share for S1 and S2 against FY
Increase No change Decrease
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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
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Conclusions
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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
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Thank You!
Questions?