The Capital District, a four-county region surrounding Albany, New York, has experienced dramatic growth in vehicle-miles of travel (VMT) in recent years, which has far outstripped population growth. One cause of this rapid growth in VMT has been the dispersal of population and employment. While congestion does not yet represent a major threat to the area, future traffic increases are projected to increase traffic delay greatly and to affect transit and freight movement negatively.
Participants in the area's recent "New Visions" long-range transportation planning process expressed concerns over these trends and, in particular, over the transportation impacts of current and projected development patterns. In response, the Capital District Transportation Committee (CDTC), the metropolitan planning organization (MPO) for the Albany region, developed and applied a land-use "pivot" model. The model was applied to test the impacts of transportation actions, tax policies, zoning changes, and urban reinvestment on regional settlement patterns and mobility.
The results of the land use analysis showed that congestion by itself was likely to have a relatively small impact on regional settlement patterns. They also showed, however, that policies to encourage urban reinvestment could have a significantly positive impact on the transportation system. Traffic congestion and delay could also be reduced by locating new development in areas where excess highway capacity exists, away from critical congestion corridors.
This case study illustrates how a basic land use model can be applied, with relatively little effort, to expand the range of policies and impacts that are considered in the transportation planning process.
The Capital District of New York is a four-county region with a population of 872,000. The district includes the cities of Albany, Troy, Schenectady, and Saratoga Springs (Figure 1). Regional vehicle-miles of travel (VMT) growth is greatly outstripping population growth; between 1980 and 1990 peak hour VMT increased by 37 percent while regional population increased by only five percent.
Figure 1. New York's Capital District
Source: Capital District Transportation Commission.
Figure 2. Excess Delay with Year 2015 Travel
Source: Capital District Transportation Commission.
The Capital District Transportation Commission's (CDTC) New Visions long-range transportation plan was developed between 1993 and 1997. The Capital District Regional Planning Commission (CDRPC) developed baseline land use forecasts that were used as a starting point for this plan. In the context of regional trends toward dispersal, participants in the New Visions planning process recognized the importance of land use policies for transportation planning and felt that the strong influences of land use and transportation on each other should not be ignored. As part of the New Visions process, a Demographic, Land Use, and Growth Futures Task Force was formed. The task force constructed various land use and development scenarios to evaluate the regional impacts of different policy choices. These scenarios took the form of "what if" questions that allowed discussion of which development patterns might be more desirable than others.
CDTC developed its land use model to assist in framing these "what if" questions quantitatively. The model was designed to provide insight into questions such as:
CDTC realized that even if a desirable development pattern could be articulated, there would be no guarantee that it could be achieved. They further realized that a variety of other social and economic impacts, in addition to transportation mobility and land development, need to be considered when evaluating policy options. Nevertheless, participants in the New Visions process found the insights provided by the land use model to be useful in assessing future alternatives and in making policy recommendations
The CDTC Land Use Model was developed from available Lowry-Garin models such as those represented in the Highway Land Use Forecasting Model developed by Alan Horowitz. These models distribute regional employment and population among analysis zones, based on characteristics of the zones such as accessibility. CDTC's model differs from standard land use models in three respects:
The specific steps in the modeling procedure are as follows:
|HHPOTj||=||Potential number of new households that could be built in zone j.|
|PRICERATIOj||=||Ratio of median price of owner-occupied housing units in zone j, to the median price of all owner-occupied housing units.|
|TAXRATIOj||=||Ratio of total property tax rate per $1,000 full value in zone j to the mean property tax rate per $1,000 for all zones.|
|HHBEGINj||=||Number of households in zone j at the beginning of each increment.|
|a, b, c, d, and k||=||Coefficients developed from regression analysis of historical residential growth in the region.|
|dHHij,n||=||Number of households allocated to zone j for workers employed in zone i for increment n.|
|dHHtot,n||=||Number of households available for allocation in increment n.|
|EMPi||=||Fixed employment in zone i.|
|dEMPi,1…n-1||=||Employment added to zone i in previous increments.|
|RDSj||=||Residential Development Strength of zone j.|
|tij||=||Peak hour travel time by primary mode between zone i and j.|
|b||=||Gravity model exponent for work-to-home and home-to-work trips.|
|HHCFi||=||Calibration factor derived from the ratio of CDRPC forecast household growth for the zone to the modeled household growth for the zone.|
|dEMPij,n||=||Employment allocated to zone j based on households in zone i and other employment in zone i, for increment n.|
|HHi||=||Fixed households in zone i.|
|a||=||Gravity model exponent for home-to-other and other-to-home trips as calibrated in CDTC’s travel model.|
|s||=||Gravity model exponent for other-to-other trips.|
|EMPCFi||=||Calibration factor derived from the ratio of CDRPC forecast employment growth for the zone to the modeled employment growth for the zone.|
|Other variables are as defined in Equation 2.|
The following data were used in developing the CDTC Land Use Model:
For the "residential development strength" factor, CDTC selected variables that it found to correlate well with historical development trends in the Capital District. The residential development strengths (RDS) equation could be developed in different forms and with different variables. Analysts should review the availability of local data; and should test the effectiveness of alternative variables at explaining local development patterns. Alternative forms of these variables, such as log-transformations, could also be tested.
RDS coefficients: The coefficients for the RDS equation are based on multivariate linear regression of Capital District residential growth between 1970 and 1990.
Travel time impedances: Travel time impedances for accessibility factors are based on gravity model friction factors from the CDTC's travel model.
Zone-specific calibration factors: To calibrate the model to regional employment and household forecasts, the following procedure is used:
CDTC estimates that approximately three person-months of staff time were required to develop, calibrate, and apply the model. As the model is implemented through a simple program, it is straightforward to apply once the data have been assembled and the coefficients determined. The regressions were performed using a statistical package and the model was written and compiled by CDTC.
Once the land use model was developed and calibrated against baseline forecasts, CDTC took the following approach to analyzing alternative transportation and land use scenarios:
Six regional scenarios were identified, representing different levels and locations of growth. These include:
The following impacts of each scenario were measured:
All of these impacts (except for the land use measures) have been established as performance measures within the New Visions process. This core set of performance measures has allowed CDTC to evaluate widely differing projects, including transit, intelligent transportation systems (ITS), land use, and roadway improvement, in common terms
Some of the findings resulting from the CDTC land use modeling exercise include:
Figure 3. Growth in Households, 1990 - 2015
Source: Capital District Transportation Commission, 1995.
Figure 4. Growth in Employment, 1990 - 2015
Source: Capital District Transportation Commission, 1995.
Figure 5. Effects of Urban Reinvestment
Based on its discussion of transportation and land use issues, the Demographic, Land Use, and Growth Futures Task Force developed a number of planning and investment principles for the region. It further recommended a number of actions consistent with these principles. Some of these recommendations included establishing an urban service area; providing funding and staff participation in community-based, corridor-level land use/transportation plans; and surveying industries to determine the impacts of transportation on location decisions. The results of the CDTC Land Use Model assisted the task force in identifying the potential impacts of many of its recommended actions and helped the task force make more informed decisions.
While CDTC's land use pivot model does not have the range of capabilities of large-scale urban models such as MEPLAN, TRANUS, or UrbanSim, it can be used to evaluate certain land use impacts with a modest level of resources. The model itself consists simply of three equations that are calibrated using historical data on population, employment, taxation levels, and housing prices, as well as travel times obtained from the regional travel demand model. In most urban areas, it should be possible to assemble these data with relatively little difficulty.
The primary strengths of the model are its ease of use and calibration based on local historical data. NCHRP Report 423A, Land Use Impacts of Transportation: A Guidebook, notes that the approach "may prove useful as a sketch planning tool because it provides a qualified improvement over the traditional cross-sectional implementation" (p. 73). The approach of allocating only incremental growth, consistent with an exogenous baseline forecast, can save a significant amount of time in calibration. Also, it eliminates the need to explain differences between locally-determined forecasts and modeled forecasts to planners and policy-makers.
CDTC's modeling exercise was useful in assessing the effects of congestion on land use patterns, as well as the relative effects of alternative land use distributions on congestion and mobility-related performance measures. The results appear plausible and are consistent with the results of land use modeling exercises in other urban areas. The "marginal allocation" nature of the model simplifies the exercise and also adds an element of realism, in the sense that existing development is likely to remain in use for many years. The CDTC's method of summarizing impacts by area type (e.g., central city, inner suburb) provides a useful framework for policy analysis.
The CDTC model shares a number of limitations that are common to all land use models, to varying degrees. For example:
In addition, the approach described here has some further limitations that should be noted:
A discussion of other strengths and limitations of various land use models can be found in NCHRP Report 423A. Despite these limitations and approximations, the CDTC model probably gives a reasonable "first-order" approximation of impacts. Furthermore, it appears that planners and policy-makers in the Albany region found the results both believable and useful in assessing the potential effects of "what-if" scenarios for the region.
Capital District Transportation Commission. Evaluation of the Transportation Impacts of Land Use and Development Scenarios. Albany, NY (October 1995).
Poorman, John (1993). Case Study in Integrating Transportation and Land Use Decisions. Prepared for the Lincoln Institute of Land Policy and the Fall 1993 Statewide Transit Conference. Internet: http://ntl.bts.gov/DOCS/ilu.html.
|Capital District Transportation Commission||Chris O'Neil||(518) 458-2161|