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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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      <title>Assessing the drivers of changes in aggregate fuel economy in Massachusetts: The role of vehicle reallocation</title>
      <link>https://trid.trb.org/View/1772137</link>
      <description><![CDATA[The U.S. state of Massachusetts has experienced a significant decrease in transport emissions along with an increasing vehicle population in recent years. Given the dominant share of fossil fuel vehicles, this divergence with emissions may be due to the changes in fuel economy. This paper seeks to quantify the aggregate fuel economy, its driving factors, and the corresponding changes in Massachusetts using comprehensive microdata from the Massachusetts Vehicle Census over the period 2008q1 – 2014q4. First, this paper develops an indicator of aggregate fuel economy that incorporates both individual fuel economy and vehicle usage. Next, it decomposes the time series of aggregate fuel economy into an unweighted average fuel economy and a covariance term and subsequently decomposes the growth in aggregate fuel economy into within, between, entry, and exit effects. The results show an improvement in aggregate fuel economy over the research period with an average quarterly growth of 0.63 percent. Furthermore, the results highlight the important role of reallocation of vehicles (entry and exit of vehicles) as it contributed the most (approximately 8.41 percent) to the growth of aggregate fuel economy.]]></description>
      <pubDate>Wed, 09 Jun 2021 17:19:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/1772137</guid>
    </item>
    <item>
      <title>Profiling commuters’ travel behavior in the Pacific states of the continental U.S.</title>
      <link>https://trid.trb.org/View/1739790</link>
      <description><![CDATA[The authors develop a more nuanced understanding of commuters’ travel mode choices by generating detailed profiles that capture the travel behavior of commuters in the Pacific states of the continental US. These profiles are created by utilizing the US Census Public Use Microdata Sample (PUMS) data. The microdata sample set allows for the estimation of fine-grained models that showcase how individual commuters make travel mode choices. The authors' results show appreciable locational variation in mode choices and statistically significant differences in commuting profile across and within population segments. A key revealing finding demonstrates that across the three states analyzed, the total number of vehicles driven for any day of the week could be reduced by up to 10 million assuming the commuting patterns observed in San Francisco applies to the rest of the states. The authors conclude with insights and policy implications provided by the study on making transportation-related infrastructure decisions.]]></description>
      <pubDate>Tue, 27 Oct 2020 12:23:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/1739790</guid>
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      <title>A Joint Model of Mode and Shipment Size Choice Using the First Generation of Commodity Flow Survey Public Use Microdata</title>
      <link>https://trid.trb.org/View/1489063</link>
      <description><![CDATA[A behavior-based supply chain and freight transportation model was developed and implemented for the Maricopa Association of Governments (MAG) and Pima Association of Governments (PAG). This innovative, data-driven modeling system simulates commodity flows to, from and within Phoenix and Tucson Megaregion and is used for regional planning purposes. This paper details the logistics choice component of the system and describes the position and functioning of this component in the overall framework. The logistics choice model uses a nested logit formulation to evaluate mode choice and shipment size jointly. Modeling decisions related to integrating this component within the overall framework are discussed. This paper also describes practical insights gained from using the 2012 Commodity Flow Survey Public Use Microdata (released in 2015), which was the principal data source used to estimate the joint shipment size-mode choice nested logit model. Finally, the validation effort and related lessons learned are described.]]></description>
      <pubDate>Thu, 30 Nov 2017 09:53:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/1489063</guid>
    </item>
    <item>
      <title>Peak Car in the Car Capital? Double-Cohort Analysis for Commute Mode Choice in Los Angeles County, California, Using Census and ACS Microdata</title>
      <link>https://trid.trb.org/View/1439710</link>
      <description><![CDATA[This paper develops double-cohort models to study the impact of demographic shifts on the aggregate commute mode choices in Los Angeles County, California from 2000 to 2010. Specifically, the models construct demographic cohorts by year of birth and immigration and study three commuting modes: automobiles, driving alone and carpooling. Using Integrated Public Use Microdata Seris (IPUMS) datasets on 2000 Census and 2009-2011 American Community Survey (ACS), the author found statistically significant effects on commuting mode choices across different cohorts in years 2000 and 2010. The trajectory charts show that the native-born younger generations are three-percent less possible to commute by automobiles than the older ones when reaching the same age levels, while the new immigrants staying in the US for less than ten years showed a strong increase in their expected probabilities of commuting by automobiles from 2000 to 2010. A simple projection shows that these two aforementioned demographic shifts: generational change and immigration assimilation push the aggregated preferences in commuting by automobiles to different directions. The projection shows there will be 82.5 to 84.0 percent of workers commuting by automobiles in 2020.]]></description>
      <pubDate>Wed, 15 Feb 2017 17:03:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/1439710</guid>
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    <item>
      <title>Some effects of highway land acquisition on farm owners and operators and possible adjustments in acquisition procedures</title>
      <link>https://trid.trb.org/View/1220414</link>
      <description><![CDATA[]]></description>
      <pubDate>Wed, 07 Nov 2012 12:40:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/1220414</guid>
    </item>
    <item>
      <title>Use of the U.S. Census Bureau's Public Use Microdata Sample (PUMS) by State Departments of Transportation and Metropolitan Planning Organizations</title>
      <link>https://trid.trb.org/View/1140949</link>
      <description><![CDATA[Census microdata are the confidential records of specific individuals and housing units from whom Decennial Census or American Community Survey responses have been obtained.  The U.S. Census Bureau also draws a sample from the full set of microdata and makes these sampled records available in the Public Use Microdata Sample (PUMS) data products, so that users can develop their own tabulations.  These data are being used by state departments of transportation (DOTs) and metropolitan planning organizations (MPOs) for studies, such as analyses of the commuting characteristics of population subgroups, and for supporting travel demand models and land use models.  Information for this study of PUMS use was gathered by literature review, survey of selected state DOTs and MPOs, and in-depth interviews.]]></description>
      <pubDate>Tue, 12 Jun 2012 07:53:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/1140949</guid>
    </item>
    <item>
      <title>Branson Airport Enters the Financing Phase</title>
      <link>https://trid.trb.org/View/882533</link>
      <description><![CDATA[The new Branson Airport, located in Branson, Missouri, is scheduled to open on May 11, 2009. Part of an ongoing series on Branson Airport's development, the authors discuss financing aspects of the privately funded public use facility in this article, beginning with the need for solid cost of construction establishment. An overview of challenges involved in raising capital for the airport and countering rising construction costs is also presented. Planning alterations and some of the final infrastructure package contents, such as a water well and water control facilities building, are discussed.]]></description>
      <pubDate>Tue, 17 Feb 2009 12:32:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/882533</guid>
    </item>
    <item>
      <title>Immigrants and Resource Sharing: The Case of Carpooling</title>
      <link>https://trid.trb.org/View/848862</link>
      <description><![CDATA[Immigration has altered the demographic composition of California where the foreign-born population now comprises more than one-quarter of the population.  Despite this staggering figure, surprisingly little academic scholarship has focused on the travel patterns and behavior of immigrants.  Existing studies on this population group have largely centered on their use of public transit, yet most immigrants travel by automobile.    In this study, we use data from the 2000 Public Use Microdata Sample (PUMS) of the U.S. Census and multinomial logistic models to examine the carpooling behavior of foreign-born workers in California relative to solo driving, public transit, and walking.  The models focus on the effect of nativity, length of residency, and race and ethnicity on mode choice.    The findings show that with time in the U.S. immigrants tend to assimilate away from alternative modes of transportation (carpool, public transit, and walking) toward solo driving.  Despite this trend, the odds of carpooling for Asian and Hispanic immigrants remain high even after many years in the U.S.  These findings help us to better understand the prevalence and role of resource sharing among immigrant households.  Further, they will aid transportation planners in planning for the transportation needs of this growing population group.]]></description>
      <pubDate>Wed, 27 Feb 2008 08:58:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/848862</guid>
    </item>
    <item>
      <title>Travel Behavior of Immigrants in California: Trends and Policy Implications</title>
      <link>https://trid.trb.org/View/848790</link>
      <description><![CDATA[This article examines the travel behavior of immigrants in California.  Drawing on data from the 1980, 1990, and 2000 Public Use Microdata Sample of the U.S. Census, we describe immigrants’ travel patterns in California, focusing on commute mode. We find that immigrants rely more extensively on alternative commute modes (carpooling and transit) than native-born commuters.  But with time in the U.S., immigrants quickly assimilate away from these alternative modes and increasingly rely on solo driving.  We then explore the effects of this transportation assimilation process for immigrant families and on public transit usage.  Cars may provide immigrants with increased access to employment and, consequently, contribute to their economic assimilation.  However, declining transit use among recent immigrants and slowing immigration suggest that, unless transit planners intervene, transit ridership in California will decline.  We conclude by discussing the implications of these findings for transportation policy.]]></description>
      <pubDate>Wed, 27 Feb 2008 08:58:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/848790</guid>
    </item>
    <item>
      <title>Federal Real Property: Most Public Benefit Conveyances Used as Intended, but Opportunities Exist to Enhance Federal Oversight</title>
      <link>https://trid.trb.org/View/803361</link>
      <description><![CDATA[Under the public benefit conveyance (PBC) program, state or local governments and certain nonprofits can obtain surplus real property for public uses. The General Services Administration (GSA) has responsibility for the program but has delegated authority to the Department of Defense (DOD) for properties disposed of as part of the Base Realignment and Closure (BRAC) process. Several "sponsoring agencies" ensure that properties are used as agreed to by grantees. The U.S. Government Accountability Office (GAO) (1) determined the number, types, and locations of PBC properties disposed of in fiscal years 2000 through 2004, (2) assessed efforts to ensure that the properties are used as agreed to, and (3) identified any challenges facing agencies and grantees. GAO could not determine from GSA, DOD, and sponsoring agency data the exact number, types, and locations of properties conveyed in fiscal years 2000 through 2004 as part of the PBC program. Although GSA and DOD data on properties conveyed should have matched sponsoring agency data on properties being monitored, there were numerous inconsistencies. GSA data showed that 285 properties were conveyed, but 128 (45 percent) of these properties were not identified in data provided by sponsoring agencies. Similarly, DOD data showed that 179 properties were conveyed, yet 41 (23 percent) of these properties were not identified in sponsoring agency data. As a result, GSA, as well as the Office of Management and Budget and Congress, are not well equipped to effectively oversee the program. Better data would facilitate oversight and assessment of results and possible problems. GAO tried to resolve the inconsistencies and identified 298 properties that were conveyed for a variety of public uses, such as airports and parks. They were located throughout many states and U.S. territories. GAO noted that data on reverted property were not regularly collected. GAO found that agencies generally did not follow policies and procedures they established, or those outlined in the property deeds, for ensuring that conveyed properties are used as intended. GAO could evaluate compliance monitoring for 41 of 58 properties selected for review. Of these, 36 did not receive the compliance monitoring specified in agency policies and procedures or the deed. Despite this, 51 of the 58 properties GAO analyzed were being used as agreed to by the grantee under the conveyance terms; while 4 had reverted back to the federal government, 2 had not been fully developed, and 1 was not used as agreed to by the grantee. GAO also found wide variation in agency policies and practices, depending on type of use, which seems to make the program unnecessarily complex. GAO identified several challenges faced by agencies and grantees. Agency officials cited the need to allocate sufficient resources to manage the program and to adhere to complex federal real property-related laws as challenges. Some agencies were concerned that GSA avoids reversions; however GSA said that in avoiding reversions, its intentions are to reduce the government's overall financial burden. Grantees were generally pleased with the program, although a common challenge they cited was not having adequate information on both the program in general and individual properties.]]></description>
      <pubDate>Thu, 01 Mar 2007 08:38:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/803361</guid>
    </item>
    <item>
      <title>Urbanization of Minnesota's Countryside, 2000-2025: Evolving Geographies and Transportation Impacts</title>
      <link>https://trid.trb.org/View/789690</link>
      <description><![CDATA[In this study, the authors examine population and housing change, changes in industrial activity and occupational changes, and characteristics of commuters and the journey to work for those working away from home in 26 regional centers and their commute sheds in Greater Minnesota.  The authors also explore ways in which Public Use Microdata Samples (PUMS) and Public Use Microdata Areas (PUMAs) might be exploited to shed additional insight into the changing nature of the demographic, economic and commuting patterns that are now pervasive throughout Greater Minnesota. These data are evaluated to explore links between demographic and economic features of working-age populations, and relationships between worker and household characteristics and aspects of commuting activity on the other. The final chapter examines regional economic vitality and travel behavior across the Minnesota Countryside. When population change in sample regional centers in the 1990s is compared with change in the nearby counties that comprise the centers’ commuting fields, four situations appear: those where centers and their commuting fields both had population increases; centers with declining populations, but increases in the commuting fields; centers with growing populations, but with declines in their commuting fields; and situations where both the center and the commute field lost population.]]></description>
      <pubDate>Tue, 26 Sep 2006 11:38:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/789690</guid>
    </item>
    <item>
      <title>1990 NATIONWIDE PERSONAL TRANSPORTATION SURVEY: USER'S GUIDE FOR THE PUBLIC USE TAPES</title>
      <link>https://trid.trb.org/View/572997</link>
      <description><![CDATA[This report is part of a series of products from the 1990 Nationwide Personal Transportation Survey (NPTS).  In the NPTS, information is collected on the amount and nature of personal travel in the U.S., as related to the demographics of persons and households.  This report is designed to serve as documentation for the public use data tapes and, as such, includes sections on survey procedures and methodology, the survey questionnaire, the public use data formats, weighting the data, and comparability of the 1990 NPTS with earlier NPTS surveys and with other data sources.  The report also includes sample tables from the 1990 NPTS data, a data codebook, a procedure contents listing (for SAS tape users), a section on estimating sampling errors, a glossary of NPTS terms, and other information needed by a user of the public use data tapes.]]></description>
      <pubDate>Tue, 29 Jul 1997 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/572997</guid>
    </item>
    <item>
      <title>THE SOUTHWESTERN PENNSYLVANIA FREIGHT TRANSPORTATION GUIDEBOOK</title>
      <link>https://trid.trb.org/View/459985</link>
      <description><![CDATA[The following directory presents company profiles for 755 freight transportation businesses in southwestern Pennsylvania. The region is served by over 540 motor carriers, 25 railroads, 8 intermodal marketing companies, 21 barge/tow companies, 39 public use river terminals, 9 steamship lines/agents, 22 public use airports, 18 air freight carriers, 17 international freight forwarders and customs brokers, 4 contract logistics firms and 51 public use warehouses.  Detailed information on each of these organizations follows in this guidebook.]]></description>
      <pubDate>Thu, 04 Jul 1996 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/459985</guid>
    </item>
    <item>
      <title>USING 1990 CENSUS PUBLIC USE MICRODATA SAMPLE TO ESTIMATE DEMOGRAPHIC AND AUTOMOBILE OWNERSHIP MODELS</title>
      <link>https://trid.trb.org/View/414448</link>
      <description><![CDATA[Disaggregate (household-level) automobile ownership choice models are typically estimated by using large-scale cross-sectional household travel surveys.  Automobile ownership choice models typically stratify households into households owning zero, one, or two or more vehicles.  This automobile ownership market segmentation is critical in the application of a regional set of disaggregate travel demand models for aggregate forecasting purposes.  An alternative regional data set for estimating disaggregate automobile ownership choice models is the 1990 Census Public Use Microdata Sample (PUMS). PUMS consists of two disaggregate files of individual 1990 census records (household and population characteristics) of either 1% of an area's households or 5% of an area's households (the 1% and the 5% samples).  Disaggregate workers in household and automobile ownership choice (logit) models were estimated on the basis of PUMS data files for the nine-county San Francisco Bay Area and the one-county San Diego region.  These models were also compared with disaggregate models on the basis of the 1990 Metropolitan Transportation Commission household travel survey. The strengths and weaknesses of both approaches--PUMS versus household travel surveys--are discussed.  The primary weakness of PUMS is the lack of data on neighborhood characteristics, such as land use density or accessibility measures, at a fine enough geographic level (i.e., regional travel analysis zone) for model estimation purposes.  The transferability of the model estimation methodology to other metropolitan regions is discussed.]]></description>
      <pubDate>Fri, 23 Dec 1994 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/414448</guid>
    </item>
    <item>
      <title>ESTIMATING REGIONAL AUTO OWNERSHIP MODELS USING THE 1990 CENSUS PUBLIC USE MICRODATA SAMPLE (PUMS)</title>
      <link>https://trid.trb.org/View/383878</link>
      <description><![CDATA[Disaggregate (household-level) auto ownership choice models have typically been estimated using large-scale cross-sectional household travel surveys.  These travel demand models will typically stratify housholds into households owning zero, one, or two-or-more vehicles within the household.  This basic market segmentation is critical in the estimation and application of a regional set of disaggregate travel demand models.  An alternative regional data set for estimating disaggregate auto ownership choice models is the 1990 Census Public Use Microdata Sample (PUMS).  The PUMS are two disaggregate files of individual 1990 Census records (household and population characteristics) of either 1% of an area's households, or 5% of an area's households (the 1% and the 5% Sample).  Disaggregate auto ownership choice (logit) models have been estimated based on the PUMS data files for the nine-county San Francisco Bay Area.  Disaggregate validation is reported at the Public Use Microdata Area (PUMA) and by other market segments.  These models are also compared to disaggregate models based on the 1981 and 1990 Metropolitan Transportation Commission (MTC) household travel surveys.  Strengths and weaknesses of both approaches - PUMS versus household travel surveys - are discussed.  The primary weakness of the PUMS is the lack of neighborhood characteristics, such as land use density or accessibility measures, at a fine enough geographic level (i.e., regional travel analysis zone).  Transferability of the model estimation methodology to other metropolitan regions is discussed.]]></description>
      <pubDate>Wed, 08 Dec 1993 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/383878</guid>
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