Transcript GPS Survey Presentation

Evaluating Transportation Impacts of Forecast
Demographic Scenarios Using Population
Synthesis and Data Simulation
Joshua Auld
Kouros Mohammadian
Taha Rashidi
Kermit Wies
The 12th TRB National Transportation Planning Applications Conference
May 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:
y1i  1 x1i   1i
y 2i   2 x2i   2i

y Ni   N x Ni   Ni
E  i   0; E  i  j    ij I
SURE Forecasting Model:
Explanatory Variables
 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:
HH Size Results
MODEL:
Constant
D HH / HH
(%HHS=i ) x D HH / HH
D JOBS/HH)
(HH DENSITY
)
D HH DENSITY)
(%SINGLE
)
(%RACE_OTHER
)
R
D HHS /
HH
0.032
0.076
0.604
0.050
-5.71E-07
3.19E-05
--0.130
D HHS /
HH
0.017
0.112
0.603
----0.015
-0.096
D HHS /
HH
-0.013
0.151
0.604
-0.032
--2.19E-05
-0.015
0.057
D HHS /
HH
-0.037
0.057
0.603
-0.018
5.71E-07
-1.00E-05
0.030
0.168
0.68
0.80
0.88
0.55
SURE Forecasting Model:
Number of Workers Results
MODEL
Constant
D HH /HH
(%HH
=i ) x D HH /HH
D (HH
) / HH
D JOBS/HH)
% BLACK
% OTHER
HH DENSITY
D HH DENSITY)
(JOBS/HH)
R
D NWORK /
HH
0.048
0.270
0.047
-0.415
0.037
0.020
-0.028
-7.33E-06
4.41E-05
-0.020
0.66
D NWORK /
HH
-0.043
0.656
0.047
-0.632
-0.028
-0.020
0.028
5.54E-06
-5.22E-05
0.011
0.92
D NWORK /
HH
-0.005
0.026
0.047
1.048
-0.009
0.000
0.000
1.79E-06
8.12E-06
0.008
0.92
SURE Forecasting Model
Validation
 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:
Model
Base Year
1990
1980
Forecast Year
2000
2000
RMSE
79
110
Proportional
R2
0.75
0.65
RMSE
89
127
R2
0.68
0.53
% Improvement
RMSE
13.3%
15.9%
R2
10.4%
23.5%
NumWorkers Validation:
Base Year
1990
1980
Forecast Year
2000
2000
Model
R2
RMSE
107
0.77
138
0.65
Proportional
R2
RMSE
119
150
0.72
0.59
% Improvement
R2
RMSE
11%
7%
9%
11%
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
–
–
–
–
–
Auto Trip
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
Mandatory
Trip
Maintenance
Trip
Discretionar
y 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
0.08 Des Moines area
0.07
0.06
0.05
Transferred
0.04
Obsereved
0.03
National
0.02
0.01
0
0
10
20
30
40
50
60
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
30
25
20
15
10
5
0
15
25
35
45
Original
55
65
75
85
Scenario
Scenario 2: Increased Youth in Chicago
Scenario 2: High Ageing in Suburbs
40
30
35
25
30
20
25
20
15
15
10
10
5
5
0
0
15
25
35
45
Original
55
Scenario
65
75
85
15
25
35
45
Original
55
Scenario
65
75
85
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
BY
FY
S1
S2
Total Trips
11.38
11.32
10.60
10.30
Whole Region - Average Per Household
Mandatory
Maintenance Discretionary
1.76
3.09
2.52
1.76
3.07
2.50
1.57
2.85
2.40
1.54
2.76
2.34
BY
FY
S1-high ageing
S2-low ageing
Daily Trips
11.05
10.79
10.46
10.84
Mandatory
1.69
1.66
1.52
1.63
Chicago
Maintenance
2.98
2.90
2.82
2.89
Discretionary
2.47
2.40
2.36
2.44
Auto Share
85.5%
85.6%
86.3%
86.0%
Mandatory
1.78
1.78
1.59
1.51
Suburbs
Maintenance
3.12
3.11
2.86
2.73
Discretionary
2.54
2.53
2.41
2.31
Auto Share
90.7%
91.0%
91.7%
92.3%
BY
FY
S1-high ageing
S2-higher ageing
Daily Trips
11.47
11.45
10.64
10.17
Auto Share
89.6%
90.0%
90.7%
91.0%
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?