![]() ![]() However, NHTS, as well as the three travel surveys above only capture the travel information for one single day. One area we wish to have a better handle is the day-to-day variation in mode choice and total demand (for example, the amount of driving measured in vehicle miles traveled), so that we can predict long-term behavior from a daily model. There are two areas that extra data would be still beneficial. Second, since each of the data sets was collected by a different State/agency, the information collected varies and how the variables are measured or coded are likely different, which adds extra work to create a unified data set at the least and may weaken the final models at the worst. First, these three data sets were collected in different years, which may create odd effects that are included in the model estimation. But with the 2009 NHTS data with confidential residential block group location, there is limited additional benefit of pursuing combining CATS, OHAS and PSRTS for a number of reasons. We also looked into alternative data sources, including the 2012 California Travel Survey (CATS), 2011 Oregon Household Travel and Activity Survey (OHAS), the 2014-2015 Puget Sound Regional Travel Study (PSRTS) and explored the potential to combine these 3 surveys to create a unified data set with diverse coverage. With the confidential residential block group location, we joined the 2009 NHTS with the 2010 SLD to get a combined dataset of travel information and built environment/urban form variables of households’ residential block group. We retrieved the 2009 NHTS data with confidential block group level residence location (and Census Tract and ZIP code of workplace location), which is the ideal data set we eyed for modeling mode choice. Additional data sources include TTI’s Urban Mobility Report dataset and National Transit Database. The primary data sources we identified and used for Task 2 are the 2009 National Household Travel Survey (NHTS) and 2010 EPA Smart Location Database (SLD). Members of the TAC/OSA contract will review and suggest adjustments to the PSU researchers for model design, estimation data and results, validation data, approach and results guide the selection of the best model design. The deliverable of Task 2 is a working paper (this document) that describes model designs, data sources, estimation, results of sensitivity tests and validation documented R scripts used to process and analyze data. ODOT staff shall review and adjust the proposed designs, estimation data and validation data/approach. The PSU team will discuss and coordinate with Brian Gregor in the model design and estimation process, as he implements the RSPM common framework, to make sure the design and data format match the latest RSPM modeling framework. PSU researchers will also identify sensitivity tests to assess the upgraded model with literature elasticities, repeating some of the tests previously calculated by the RSPM to ensure these remain intact, as well as adding tests to evaluate the new functionality. The PSU team will suggest functional form and independent variables for model estimation with associated data sources for estimation and validation. ![]() These approaches build on the existing RSPM module and utilize household and land use inputs and budget constraints already embedded in the RSPM tool. More specifically, the plan is to select and estimate one or more possible designs of the mode choice model based on literature review and data exploration in Task 1 and to understand what mode shifts occur as vehicle travel is reduced, incorporating and testing interactions in RSPM. ![]() Task 2 of the project is to “select one or more possible model designs for RSPM mode shift, estimate model parameters and evaluate the designs and estimated parameters with sensitivity tests and validation”.
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