Assessing the Impact of Proposed Transit Investments and Public Policy Choices on Land Use Patterns (A Simulation Approach with UrbanSim).

摘要

Transit systems, such as commuter rail, light rail, bus rapid transit, and high speed rail, can generate significant impacts on land developme4nt patterns over time. As an aid to local decision-making, it would be very helpful to have an evaluation tool which can be developed, validated, and calibrated with local data to forecast the impacts of proposed transit investments on land use patterns and to assist in determining which policy choices might achieve a desired result. The study aims to develop an UrbanSim model to assess the changes that might result in a specific geographic area from a decision to construct a Bus Rapid Transit investment in Hillsborough County, Florida. UrbanSim has the potential to be a powerful tool in evaluating the benefits of transit investments because it works on a much smaller geographic scale than other alternative systems and can simulate location decisions made by individual business and families. Data on parcels, households, jobs, and land plans were collected for the base year (2010) from various data resources, such as the county appraiser, census database, InfoUSA, etc. The Tampa Bay Regional Planning Model (TBRPM) model was used to produce a traffic pattern forecast. The model was validated by comparing the simulation results to the protected demographic data in the Hillsborough County MPO 2035 Long Range Transportation Plan (LRTP). Four scenarios with different ridership levels were created of the proposed Bus Rapid Transit (BRT) investment using a short-term period (2010-2012) and a long-term period (2010-2020). The scenarios were compared to the ones without the proposed BRT route in the same years. The study found that the change in land use patterns is complex due to implementing the BRT service, either in a short-term (2010-2012) or a long-term period (2010-2020). The proposed BRT service has significant impacts on land use patterns in a long-term period (2010-2020).