Use/Demand Concepts/units Methods Use in Planning –Management/operations –Evaluation –Needs assessment –Forecasts

Why Measure Use Operational decisions –maintenance, staffing, budget allocations Needs Performance measure/evaluation

What to measure Use units – individual, groups…visit, visitor, trip, day, RVD, vehicle entry,… Level of detail/aggregation –Temporal –Spatial –By activity –By user characteristics

Use estimation questions Measure or estimate use at existing site Forecast use at existing site Forecast changes in use –New sites/products –Change at site or within the environment

Key determinants of use Population size & characteristics Distances to markets Tastes & preferences Prices Substitutes Quality/quantity of supply Promotion/information Weather/climate

Methods for estimating use On-site techniques –Counting devices –Observation –Surveys (sample or census) Off-site –Use prediction Models – e.g. Gravity model Trip generation Trip distribution

Double Sampling Technique 1. Select a variable that is easily measured E.g. water meter - W 2. Establish a relationship with use U = a * W + c Calibration via “double sampling” /linear regression U = 1/3 * W Regularly measure W and apply equation 4. Periodically recalibrate See page 21 of lecture handouts for details

Sampling to estimate use Stratify time periods into H, M, L use E.g. weekend, weekday, am, pm, eve Sample representative periods within each stratum Measure use during each sampled period, compute averages Expand from sample to population

Lansing Trail Strata: Use counts for one hour Day Am (8-12)Pm (12-4)Eve (4-8) Weekday 7,8,18 Avg =11 3,8,8,8, 101 Avg= 25 78,59,139,13,72 Avg=72 Weekend 11, 21, 9 Avg= 14 8, 6, 116 Avg =43 8,104,123 Avg =135 Overall avg = 42,Total use = 42 *12 hours*31 days = 15,624

Gravity Model T i,j = K* P i *A j / D 2 ij Where –T i,j = trips from origin i to destination j –P i = population of origin I –A j = measure of attractiveness of destination j –D ij = distance from i to j (or cost) –K =calibration constant

Linear regression model V = a 0 + a 1 X 1 + a 2 X 2 + a 3 X 3 +… a n X n Where –a’s are coefficients to be estimated –X’s are independent variables/use predictors –E.g. V = participation rate »Frequency of participation »Visits in past year –X’s : age, income, gender, location, weather…

Demography Forecast size, geographic distribution, and age-sex composition of the population Census 2000 data American Fact Finder

Participation by demographic characteristics Age Income Gender Rural/urban, location Racial/ethnic group