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89 C h A P T e R 5 Research Findings Initial research on key practitioner needs provided a framework for evaluating smart growth strategies: ⢠Most agencies were interested in scenario planning as a strategy for evaluating smart growth. ⢠Many agencies recognize the need for coordination, coopera- tion, and communication with local governments on land use policy, since land use regulations are governed by local governments. ⢠Many agencies want to understand the impacts on per- formance as a result of induced demand, travel demand management strategy, and urban form impacts as well as congestion reduction strategies. The research and products were therefore focused on devel- oping a regional scenario planning tool that could be used by land use and transportation planners to provide opportunities for interaction on common goals. The scenario planning tool is able to assess the impacts of various travel demand manage- ment, urban form, congestion reduction strategies, as well as induced demand that arises from these. There were five topics considered in the background research. In each case, research was conducted to identify and clarify well-established relationships that could be used in the evalu- ation of smart growth strategies. There were also gaps in the research that were identified for each topic. These gaps were also used to define useful capabilities in the SmartGAP soft- ware, although not all gaps were completely filled with this first version of SmartGAP. Table 5.1 presents a summary of background research relationships and limitations. SmartGAP Use SmartGAP is intended for use by planning agencies that are involved in regional planning activities, such as regional/ metropolitan planning agencies, state department of transpor- tations, and local land use planning agencies. If all the agencies that are engaged in regional planning for a particular area were to use the same tool, with similar inputs, then collaboration would be more straightforward and decisions made regarding potential scenarios would be made on a consistent basis. SmartGAP is designed to be easy to set up and use, so smaller planning agencies with fewer staff resources can make use of the tool. It is also envisioned that larger planning agencies may take advantage of the processing speed and relative ease of use to run multiple scenarios for screening purposes before more complex and time-consuming integrated land use and travel demand forecasting models are needed. SmartGAP is delivered as a zip file and can be installed simply by unzipping the file to a location on your computerâs hard drive. The zip file contains text files scripts, CSV input files, and .Rdata binary files for the models. SmartGAP is coded in R, which is an open source statistical software platform. SmartGAP uses several add-in packages to R, which it downloads auto- matically the first time it is run. Future enhancements to SmartGAP During the course of the development of SmartGAP and the pilot testing, the TETG and the pilot testing agencies identified potential future enhancements to SmartGAP that could be con- sidered at some point in the future. These were not identified as flaws, or major barriers to the current use of the modeling sys- tem, but enhancements that may expand the future usefulness. There were also short-term enhancements that were identi- fied and included in the current version of SmartGAP. These longer-term enhancements were not possible within the first version of SmartGAP and are summarized here in three main areas: Model Enhancements ⢠Expand the freight analysis capabilities to provide sensitivity in the model to freight smart growth strategies. Research Findings and Conclusions
90 ⢠Re-estimate models for different regions of the United States to recognize regional differences in model parameters. Re-estimate the household income models using updated national data (the current model is based on Oregon Census data from 2000). ⢠Expand the transit features to recognize different parameters by place type and to calculate transit per employment. ⢠Expand the modal representation to include other modes, such as taxi. ⢠Enhance the nonmotorized mode features and include pedestrian travel more explicitly. ⢠Consider housing market response and household budgets as factors in the models that are sensitive to congestion and transportation and land use policies. ⢠Add residential and commercial building emissions to the existing method of estimating greenhouse gas emissions from transportation sources. Smart growth should have a positive impact on land use greenhouse gas emissions com- pared to conventional development. ⢠Add supporting infrastructure costs to the model, such as sewer, schools, and local roads, which are needed to support new residential and commercial development. There is available research on this topic that can be used to estimate these costs. ⢠Include life-cycle costs, such as operations and maintenance, for highway infrastructure. ⢠Consider adding costâbenefit analysis to the system. For example, what is the return on an ITS investment compared to building new roads? This can be done outside the model by using the available results, but may be useful to build in as a feature. ⢠Consider additional ITS policies (in addition to incident management) that could be included in the SmartGAP evaluation. ⢠Enhance the sensitivity of the performance metrics to transportation supply and congestion by including in the calculations all of the metrics (currently some metrics are calculated based on elasticities that are sensitive only to land use changes). ⢠Enhance the congestion module with improvements made to GreenSTEP providing more sophisticated support for pricing scenarios by transferring these improvements to SmartGAP. ⢠Enhance the truck modeling component to allow for sensitivity to policy changes. ⢠Add additional sensitivity to the model based on employ- ment type (such as the allocation of jobs by industry type). ⢠Make speed improvements so that larger areas (in particular) can run the model more quickly. This could be achieved by code refactoring or evaluation of a weighted sample of households. Graphical User Interface Enhancements ⢠Replace the data editor window with a more functional and aesthetically improved object. ⢠Add charting of additional inputs and other calculated variables that are not part of the primary performance met- ric charting. Add functionality to compare across projects as well as across scenarios. ⢠Enhance error handling of file naming for inputs and layouts to be more friendly and useful. ⢠Add a scenario dashboard that can summarize all of the metrics in one view and that allows cross-scenario com- parisons for multiple metrics at once. User Information, Data and Access Enhancements ⢠Provide a linked help system in addition to the userâs guide (which is accessible in PDF form in the software). These enhancements are recorded to document the future possibilities that were considered, but were outside the origi- nal scope for the development of SmartGAP. Table 5.1. Summary of Background Research Relationships and Limitations Topic Well-Established Relationships Gap in Research Built environment impact on peak auto demand Impact on daily travel Impact by time of day Mobility by mode and purpose Impact on daily travel Impact by trip purpose Relationship between induced traffic and induced growth Capacity expansion on an expanded facility Route, time-of-day shifts and mode shifts, induced trips, new destinations, growth shifts; effects of operational improvements, land use plans Relationship between smart growth and congestion Localized effects Macro-level or regional effects Smart growth and freight Freight is necessary for population centers Impacts of loading docks, truck routing, full-cost pricing, freight facilities and crossings, interfirm cooperation, stakeholder communication
