Modeling and Forecasting Household and Person Level Control Input Data for Advance Travel Demand Modeling Presentation at 14 th TRB Planning Applications Conference May 6, 2013 Columbus, Ohio

Socio-economic and demographic Data – Crucial in transportation planning – Generally developed by local agencies – Number of variables included Current focus – macro to micro-level, – disaggregate and activity-based – Improvements to past studies Background (1)

Population synthesis methods – Detailed socio-demographic characteristics households and employments Households – # of workers, size » Cross classified by income Similarly other variables – Employment – Vehicle ownership – Occupation Background (2)

Limitations of controlled attributes used as input to these synthesis models: – Commonly applied at more macro levels geography county region state Forecasting poses a major problem – IPF and / or IPU are commonly used – Marginal and joint distribution accuracy is disrupted – Not enough details available for each variable for synthesis For accurate inputs: Other approaches are needed – Considers evolution process – Macro-level: less accuracy -> data available – Micro-level: higher accuracy -> data inefficiency Background (3)

To develop a meso-level modeling framework – control variables at zonal level suitable for population synthesis – captures historical population evolution trend Variables can be estimated and forecasted, such as – housing type, – householder age, – population age, – number of vehicles, – employment type, and – workers by occupation. Objective

Methodology Framework 1990 Census Data HH TypeHH AgePerson AgeEmploymentChildOccupation Location Characteristics Input Explanatory Variables HH SizeHH Inc # of Workers Base Year: Validate with 2010 Census Quality Control with 2030 Prediction at County Level Step 1: Coefficient Estimation Logistic Regression Estimation Input Response Variables for 2000 Step 2: Forecast HH TypeHH AgePerson AgeEmploymentChildOccupation Step 3: Validation

Step 1: Coefficient Estimation Let the probability of population in each age group be where j=1 for age 0-4; j=2 for 5-14; j=3 for 15-37; j=4 for 18-24; j=5 for 25-34; j=6 for 35-44; j=7 for 45-64; j=8 for over 65. The age group j=7 for is selected as the reference category. The formulation of the model is X is the vector of explanatory input variables, which contain the major variables (1990 population by age cohort), and secondary variables like median income in Coefficient Estimation

Step 2: Forecast Assumption: Evolution trend from 2000 to 2030 is consistent with the trend from 1990 to First, probability of 2010 population in each age group will be calculated using 2000 as the base year. Forecasting Process (1)

Step 2: Forecast Then the population by each group Age10 is calculated based on the total population Pop10 in each TAZ in 2010 by the formulation Similar to the previous step, we can calculate the probability of population by each age group in Iteratively, the target population by each age group in 2030 can be achieved. Forecasting Process (2)

Step 3: Validation Mean Absolute Percentage Error (MAPE) and Median Absolute Percentage Error (MedAPE) are the two indicators in the validation. Year-2000 – The fitted value and observation for 2000 at the TAZ level Year-2010 – The forecast result for 2010 and census in 2010 at the county level Year-2030 – The forecast result in 2030 and projected county control totals provided by local agency (In our case study MDP) Validation

Geography and Data Source Area – Baltimore MPO (except Baltimore City) – 814 TAZs Data Source – Census 1990 and 2000 – Baltimore Metropolitan Council (BMC) – Maryland Department of Planning (MDP)

Data Descriptive VariablesLabelmeanminmax Std- deviation PAge0_4Percentage of Age 0-4 in %0.00%16.67% 2.31% PAge5_14Percentage of Age 5-14 in %0.00%24.45% 3.50% PAge15_17Percentage of Age in %0.00%19.62% 1.33% PAge18_24Percentage of Age in %0.00%29.15%2.72% PAge25_34Percentage of Age in %3.64%50.00% 6.65% PAge35_44Percentage of Age in %0.00%36.54% 3.73% Page45_64Percentage of Age in %5.23%41.67% 6.03% PAge65Percentage of Age over 65 in %0.00%51.17% 6.21% medinc (10K) 2000 Median income in TAZ (in unit 10,000) HHDEN Household density in TAZ (per acre) EMPDEN Employment density (per acre) GQDEN Group quarter density (per acre)

Estimation Result Sample size: 763 TAZs. Dependent variable: Classified by 8 age cohort. The explanatory variables include the – historical age distribution in 1990, – median income, population density, employment density and group quarter density in Other type of variables, – distribution of household size, – income and – number of workers – not highly correlated with age distribution.

Estimation Result ParameterAge0_4Age5_14Age15_17Age18_24Age25_34Age35_44Age65 constant PAge0_ PAge5_ PAge15_ PAge18_ PAge25_ PAge35_ PAge medinc (10K) HHDEN EMPDEN GQDEN A zone with more children aged 0-4 in 1990 will still have more children of this age in A zone with more aged 5-14 in 1990 will still have more of the age in in 2000.

Estimation Result (1) A zone with more children aged 0-4 in 1990 will still have more children of this age in TAZs with more 5-14 and aged kids will have relatively more population in and 25-34, correspondingly. Zones with people from 18 to 34 are still high with the same age group, because of the high mobility of the young people and some fixed educational locations, such as colleges. The residential location of and 65+ in 1990 are more likely to be replaced by the young population in in 2000.

Estimation Result (2) TAZs with high median income is more attractive – to population within age – to household with children aged High housing density is more likely – to have older residents (65+) – less likely to attract household with adults in and child in Young populations in – prefer to live in the area with high employment density – the older follow opposite trend Higher group quarter density indicates more or 65+ persons as either college students or retirees.

Validation -Year 2000

Forecasting Result: Year 2010

County plus Anne Arundel 32,92587,36425,92937,35658,76995,090128,76566, %13.7%-2.4%-25.2%-1.0%1.2%25.0% Baltimore County 46,822122,21837,36565,99093,122135,910185,999129, %9.7%0.9%-15.1%1.7%-2.5%8.1% Carroll 10,31729,8399,57111,99116,64431,28144,16121, %3.6%6.2%-20.0%-4.9%5.1%14.8% Harford 15,20742,37613,08815,37225,26944,05461,82731, %5.4%1.6%-22.5%-3.9%5.0%25.0% Howard 18,97652,66215,20415,02429,47453,87070,19528, %13.5%0.5%-29.5%-3.2%3.9%33.2% (MAPE = 10.2%; MedAPE = 6.2%)

Forecasting: 2030 County plus Anne Arundel 37,04197,82133,48849,99356,169104,099125,46269,953 8%29%12%-8%-39% Baltimore County 51,171131,34044,41483,90692,933144,533181,729132,110 5%10%17%-7%-28% Carroll 12,80434,62412,49217,76219,40738,41547,13324,674 8%21% 4%-49% Harford 17,84747,74916,90122,96228,07052,12264,76137,420 1%20%13%0%-38% Howard 23,39264,16221,47222,95128,92462,55370,42933,573 17%41%11%-8%-50% (MAPE = 16.7%; MedAPE = 12.0%)

Evolution Pattern Comparison between percentages of population in 2000 and 2030 for each TAZ (0-4)

Evolution Pattern Comparison between percentages of population in 2000 and 2030 for each TAZ (5-14)

Evolution Pattern Comparison between percentages of population in 2000 and 2030 for each TAZ (65+)

Conclusions Framework is applied to forecast several age cohort in Baltimore The model evaluation and validation of prediction results are reasonable This model provides a good estimation and prediction for the age group and 35-64, but not and 65+ groups. The final prediction for 2030 – has a lower estimation for population over 65+ – an overestimation for teenagers comparing with the projection data (consistent with synthesis outcome).

Future Work To apply the framework – on other demographic variables – incorporate more land use variables into the model The variables accuracy in 2020 and 2030 needs to be evaluated at TAZ More land use data can be incorporated – transit service – number of schools – recreation centers – as they are highly related to planning decisions

Acknowledgement Contact Information Sabya Mishra Assistant Professor Department of Civil Engineering University of Memphis, Memphis, TN Phone: