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21 WSA developed automatic procedures (TransCAD) and Ac- Shipping costs (1) cess database macros, allowing for the update of attribute in- Total trips by area (1) formation from LADOTD Summary log file or other files as long as these files contain beginning and ending mile posts for the updated attributes. With these macros, the updating net- MOEs are similar to those found in urban models. Among work attributes become a simple task. For example, near the the seemingly obvious MOEs not mentioned were energy end of the model development work, LADOTD systematically consumption and any user benefits other than time savings. reclassified their functional classification system, and WSA was able to incorporate this latest information easily for the fi- States will often disaggregate MOEs by time of day or by lo- nal model revalidations. cation to better identify problems. For example, Massachu- setts looks at congestion measures by time of day. Ohio In summary, given the scale and extent of the statewide model network coverage, the ability to link these DOT existing, breaks down its MOEs by Ohio DOT district and by county. well-established attribute databases to the modeling GIS net- Oregon computes various MOEs depending on the issue, work becomes increasingly important once the statewide model such as VMT by travel market segment, VHT by travel mar- is developed. It eliminates duplicate efforts, reduces network ket segment, shipping costs by area, total production by area, coding errors, and increases job satisfaction by eliminating te- dious manual work and increasing fast turnaround time in con- employment by area, land prices by market segment and ducting alternative analysis, corridor studies, scenario planning, area, and trips by travel market segment. Montana's HEAT and other statewide planning activities (S. Yoder, personal com- included measures of accessibility, business activity within munication, 2005, and Wilbur Smith Associates 2004). cities or markets, production costs, and personal income. Three states indicated that they had no MOEs. OVERALL MODEL CONSIDERATIONS All states with operational models have used them for long- Employment Data range forecasting purposes. With the exception of Vermont, forecasts of 20 or more years have been done. Two particularly difficult aspects of travel forecasting are obtaining good TAZ level employment data and good long- range economic forecasts. Employment data from govern- Measures of Effectiveness mental sources are often restricted by confidentiality issues and incorrect street addresses. Many states have opted to ob- A state selects measures of effectiveness (MOEs) that relate tain their employment data from commercial sources. Here closely to the rationale of the model. MOEs are usually ag- are the primary sources of employment data reported by gregations of results that would pertain to individual links states, in order of prevalence. A state may have used more (e.g., road segments) or nodes (e.g., intersections) and are than one source. aids to deciding between alternatives. MOEs are relied on during the decision-making process because people are able CTPP (10) to readily grasp only a few indicators of system performance, MPO databases (10) and aggregate measures have a lower percentage of error Commercial data vendor (10) than raw travel forecast outputs. The following is a complete Department of Workforce/Employment/Labor list of MOEs used by states in order of prevalence. Development (6) Workman's compensation tax records (5) VMT (22) Unemployment records (4) VHT (20) Employer or establishment survey (2) Volume and capacity ratios (18) Regional economic model (2) Employer directory (1) Levels of congestion (15) Other unspecified (1) Traffic growth rates (14) System delay (11) Many states have taken advantage of MPO models for Passenger volumes by mode (9) employment data. Although the same data problems also ex- Corridor delay (9) ist at an MPO, usually an individual(s) with good local Employment by area (8) knowledge has already confronted them. Ten states use the Time savings (8) CTPP, which derives employee location from a large sample Freight tonnages by mode (6) of households during the decennial census. The CTPP is es- Air pollution emissions (3) pecially attractive because of its low marginal cost. Although Crash reduction (2) unemployment records (ES-202) seem to be an attractive Greenhouse gas emissions (2) source of data, some states have reported considerable prob- Benefitcost ratio (2) lems in obtaining and using this database. Goods production by area (2) Interregional travel (1) Economic forecasts are done regularly by the BEA; how- Land prices (1) ever, the BEA regions are usually too large for direct inclusion