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58 I N N O VAT I O N S I N T R AV E L D E M A N D M O D E L I N G , V O L U M E 1 oping denser activity centers. The Blueprint focuses on the pattern. The most frequently used formula is the good four Ds: density, diversity in the mix of land uses, design intersection ratio, which is the sum of 3- and 4-link node related to pedestrian- and transit-friendly approaches, and intersections divided by the sum of all intersections, for a destination or the utility clustering of complementary land particular area. The reverse of this ratio is the bad inter- uses. Activity-based modeling has a natural link to this section ratio, which is the sum of all 1-link node intersec- land use policy focus. Activity-based models provide the tions divided by the sum of all intersections. level of detail needed to evaluate the impact of different The street pattern variables are critical inputs to land use plans and different land use patterns. many of the choice submodels and are very predictive. For The new SACOG activity-based travel demand work locations, work tour destinations, and nonwork and model uses parcel-level data, rather than traffic analysis nonschool tour destinations, the good intersection ratio is zones. The main reason for using the parcel-level data is a highly significant, positive variable. For school and other that the data provide the detail needed to assess ques- tour mode choice models, intersection density at the tour tions related to development patterns, street patterns, origin is a highly significantly positive variable for walk- and proximity to transit services. SACOG used to-transit, walk, and bicycle modes. The bad or dead-end PLACE3s, which is a parcel-based land use scenario street ratio is a highly significant negative variable for the analysis package, in the development of Blueprint. Since intermediate stop location model. The difficulty is in fore- PLACE3s is parcel based, most of the land use data have casting street patterns at the parcel level. been transitioned to the parcel level over the past few Generation of forecast street pattern data that vary years. At the same time, SACOG coordinated a regional by parcel is used for input elements. These elements are roadway geographic information system (GIS) coopera- the parcel-level dwelling and employment for the base tive focused on protocols and data standards for a year, the parcel-level dwelling and employment for the regional roadway centerline GIS. SACOG has also devel- forecast year, the base-year roadway GIS, and a look-up oped a transit GIS, which includes routes and stops, as table of densities of the three intersection types for dif- part of a regional traveler information system. ferent types of areas. The generation process includes Based on these factors and other needs, SACOG five steps. First, land use in the base year and the forecast initiated the development of an activity-based travel year are compared by parcel, and parcels with changes demand model at the parcel level. This approach does are identified. Second, for parcels that are expected to present data production challenges. Activity-based mod- change in use and are over a threshold acreage, synthetic els are data intensive, especially when they include the points are generated in a grid pattern throughout the capability to capture the effects of land use, street pat- parcel. Third, each synthetic subparcel is populated with terns, and transit proximity. PLACE3s provides the capa- a computed number of each type of intersection using a bility to display and analyze land use changes. It contains look-up table. Fourth, the synthetic subparcel points in dwelling unit yields and constraint layers. Forecasts are change parcels are merged with the real intersections developed starting at the parcel level. from the base-year GIS for nonchange parcels. Finally, The estimation of the activity-based model was the merged points are buffered to parcel according to the based on a 2000 household survey, and parcel- and point- specific street pattern variable definitions. level data on dwelling units, employment levels, street pat- A different approach is used with the transit prox- terns, and accessibility to transit services. A comparison of imity variables. The transit proximity variables are the place type and development density between 2000 and defined by straight-line distance or time from each parcel 2030 can be made. In these areas, the forecast has to gen- to the nearest transit stop in the GIS. Actual transit stop erate an equivalent parcel-level detail on the street pattern locations are used for estimation. For forecasts, transit and transit proximity. This task is a challenge. stops must be added at reasonable locations. The future The street pattern is a geographical representation transit lines are overlaid on the existing routes. There are of how the streets appear and how supportive they are of different methods of synthesizing future stop locations nonmotorized travel and accessibility measures. Intersec- depending on the type of transit mode. There is a pro- tions are used to help define street patterns. Three types gram to generate stops at reasonable spacing for fixed- of intersections or nodes are included in the GIS. These route bus service. Most of the station locations for future types include 1-link nodes, which are cul-de-sacs or dead- light rail transit lines are known. These station locations end streets; 3-link nodes, which are T-street intersections; are manually added depending on the alternative being and 4-link nodes, which are four-legged street intersec- modeled. For express routes, stops are only located in tions. Based on the GIS definition, higher density or the the neighborhoods being served. prevalence of 3- and 4-link nodes is associated with "good" street design and the prevalence of 1-link nodes is associated with "bad" street patterns. A number of for- Julie Dunbar, Dunbar Transportation Consulting, mod- mulas are actually used in the model related to the street erated this session.