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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Travel Behavior Analysis with Chittenden County Survey Data
</title>
      <link>https://rip.trb.org/View/1656357</link>
      <description><![CDATA[The greater Burlington region of Chittenden County, Vermont is the only small urban area in the state.  The Chittenden County Regional Planning Commission conducted travel behavior surveys in 2000, 2006, 2012, and 2018. These surveys are unique in the degree to which they collect information about traveler attitudes and priorities for regional transportation investments, as well as the degree of consistency in their survey instruments, enabling a unique opportunity to analyze changes over time. The surveys each contain disaggregate (individual-level) data, and 2006, 2012, and 2018 surveys each contain approximately 500 observations and were designed to be representative of the regional population. This project is an opportunity to conduct the first original travel behavior research using the data from these surveys.  The research team will conduct a literature review and background research, including the generation of several maps of the study area that provide a visual representation of the spatial layout of travel facilities, key transit routes, and demographics such as density and poverty.  Using the data from the four surveys, researchers will conduct regression modeling to assess the relationship between the outcomes of interest and the factors that influence them. The survey designs are particularly well-suited to the factor identification and cluster analysis techniques that aid in traveler segmentation, used to better understand characteristics of the subgroups within the population. This project may inform transportation policy and planning in small urban areas, as well as efforts to more effectively target subgroups in the population most willing and able to reduce their car reliance.
]]></description>
      <pubDate>Tue, 09 Feb 2021 17:44:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/1656357</guid>
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      <title>Vermont Transportation and Land Use Carbon Calculator: Modeling Transportation Alternatives - Part 2</title>
      <link>https://rip.trb.org/View/1359763</link>
      <description><![CDATA[The objective of this project is to develop the "Vermont Integrated Land Use and Transportation Carbon Estimator". The estimator will be used by regional transportation planners and will advance the current state of the practice by considering directly the greenhouse gas (GHG) implications of alternative land use strategies. Currently, only GHGs from tailpipe emissions are estimated using existing transportation demand forecasting models. While land use arrangement indirectly affects the amount of travel and thus GHG emissions, land use also has a direct impact as well (forest versus parking lot for example). For input, the team will be guided by the typical and readily available datasets used by regional planners. The calculator or estimator will be deployed on the web with expert guidance from RSG Inc.]]></description>
      <pubDate>Thu, 02 Jul 2015 01:01:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/1359763</guid>
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    <item>
      <title>Spatial Extent of the Impact of Transported Road Materials on the Ecological Function of Forested Landscapes</title>
      <link>https://rip.trb.org/View/1359761</link>
      <description><![CDATA[This project investigates the impact of different types of roads on the plant community structure and soil environment (chemical and biological) of nearby forests. Our hypothesis is that deicing salt and other road particles alter the soil environment and local plant diversity of surrounding forests, and that the extent of the impact decreases with greater distance from the road. Furthermore, this impact is likely to vary depending on road type and presence or absence of a swale to collect and redirect material flows. We are sampling plant community structure, soil chemistry, and soil biology of the roadside landscape for three road types (unpaved, paved 2-land, and highway) and two road profiles (swale, no swale) at six different distances from the road edge. We will also include additional variables such as traffic volume in the analysis. The sampling location, road edges, and all sampling points are precisely geo-referenced and mapped. At each sampling location, measurements of slope, aspect, elevation, road width, tree canopy gap width will be determined. This empirical data will be used to develop a methodology that can be used within transportation models to determine the total landscape impact based on alternative development scenarios and road density patterns.]]></description>
      <pubDate>Thu, 02 Jul 2015 01:01:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/1359761</guid>
    </item>
    <item>
      <title>Integrated Land-Use, Transportation and Environmental Modeling: Complex Systems Approaches and Advanced Policy Applications, Phase 2</title>
      <link>https://rip.trb.org/View/1359742</link>
      <description><![CDATA[This project develops, evaluates, calibrates, and validates combinations of integrated frameworks for agent-based land use and transportation models using Chittenden County as a test-bed. The project proposes to implement UrbanSim with TRANSIMS for Chittenden County and to integrate the two models together with an activity model developed by partners at RSG Inc. Future conditions shall be simulated based on alternative scenarios generated in stakeholder meetings and ranging from changed land use policy constraints to construction of new infrastructure. The impacts of the transportation sector on mobile source air pollution will be conducted using data from Vermont UTC Signature Project #2. Researchers in other projects will be providing new model output metrics to consider land cover and carbon; storm water; impacts on plants and soils; network robustness; and commodity transportation. The researchers will develop code for processes to integrate these new output metrics into the combined model in a format that services the MPO context and models. The most important aspect of project B is evaluating which of the combined model architecture components are necessary for which set of regional planning and policy questions.]]></description>
      <pubDate>Thu, 02 Jul 2015 01:00:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/1359742</guid>
    </item>
    <item>
      <title>Agricultural Freight: Network Access and Issues (Part 1)</title>
      <link>https://rip.trb.org/View/1359733</link>
      <description><![CDATA[The modeling framework and tools under development throughout Signature Project 1 focus on informing policy related to passenger transportation. Yet, the concept of evaluating transportation in light of environmental impacts and resiliency to disruption applies equally well to freight transportation. In a state where agriculture is one of the largest sectors of the economy, contributing about a half billion dollars in gross receipts annually (USDA ERS, 2006), and is important for attracting tourists who bring another $20 million (USDA NASS, 2004) to the state each year, agricultural freight deserves attention. Because the majority of agricultural freight transportation in Chittenden County either originates or has destinations outside of the county, it is not appropriate to model within the closed-system model used for passenger transportation. However, by gaining a better understanding of agricultural freight networks within Vermont, modelers and planners will have a more complete picture of the transportation network under study. The issues related to agricultural freight, including potential impacts on road surface integrity, air and environmental quality need to be better understood before models can be extended or developed for this context. This project will describe how milk moves from farms to first collection points or processing plants and analyze the robustness of the road networks carrying this agricultural freight. This information will be useful to public and private sector entities concerned with how transportation relates to sustainable agriculture.]]></description>
      <pubDate>Thu, 02 Jul 2015 01:00:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/1359733</guid>
    </item>
    <item>
      <title>Network Robustness Index: A Comprehensive Spatial-Based Measure for Transportation Infrastructure Management (Part 2)</title>
      <link>https://rip.trb.org/View/1359727</link>
      <description><![CDATA[This project investigates the robustness, redundancy and resiliency of the transportation network under current and future conditions. Transportation planning efforts, especially those involving highway capacity expansions, have traditionally relied on the Volume/Capacity (V/C) ratio to identify congested or critical links, resulting in localized solutions that do not consider system-wide impacts related to congestion, security and emergency response. Members of the research team recently developed the Network Robustness Index (NRI): a new, comprehensive, system-wide approach for identifying critical links and evaluating transportation network performance. It relies on readily available sources of data from travel demand forecasting models. Analysis of three hypothetical networks has demonstrated that NRI-based solutions yield far greater system-wide benefits than traditional (V/C) solutions, as measured by travel-time savings (Scott et al. 2006). While the NRI has been tested on hypothetical networks, it has not yet been applied to a real world road network. As part of the current project, it is proposed to utilize actual road networks and origin/destination (O/D) pairs as input data to assess which network links are considered the most vulnerable in Chittenden County, Vermont. The integrated UrbanSim/TRANSIMS model will provide the inputs needed to calculate the NRI for Chittenden County. This will include information about specific road networks, traffic volumes and link capacities, and origin-destination flows. Researchers will use the NRI to identify specific road links that are the most critical or valuable with respect to maintaining the robustness of the overall road network system within Chittenden County based on average peak period traffic conditions. The most critical links identified by the NRI will be compared for overlap with those identified by other more traditional measures.]]></description>
      <pubDate>Thu, 02 Jul 2015 01:00:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/1359727</guid>
    </item>
    <item>
      <title>Agricultural Freight: Network Access and Issues (Part 2)</title>
      <link>https://rip.trb.org/View/1359723</link>
      <description><![CDATA[The modeling framework and tools under development throughout Signature Project 1 focus on informing policy related to passenger transportation. Yet, the concept of evaluating transportation in light of environmental impacts and resiliency to disruption applies equally well to freight transportation. View Full Summary The modeling framework and tools under development throughout Signature Project 1 focus on informing policy related to passenger transportation. Yet, the concept of evaluating transportation in light of environmental impacts and resiliency to disruption applies equally well to freight transportation. In a state where agriculture is one of the largest sectors of the economy, contributing about a half billion dollars in gross receipts annually (USDA ERS, 2006), and is important for attracting tourists who bring another $20 million (USDA NASS, 2004) to the state each year, agricultural freight deserves attention. Because the majority of agricultural freight transportation in Chittenden County either originates or has destinations outside of the county, it is not appropriate to model within the closed-system model used for passenger transportation. However, by gaining a better understanding of agricultural freight networks within Vermont, modelers and planners will have a more complete picture of the transportation network under study. The issues related to agricultural freight, including potential impacts on road surface integrity, air and environmental quality need to be better understood before models can be extended or developed for this context. This project will describe how milk moves from farms to first collection points or processing plants and analyze the robustness of the road networks carrying this agricultural freight. This information will be useful to public and private sector entities concerned with how transportation relates to sustainable agriculture.]]></description>
      <pubDate>Thu, 02 Jul 2015 01:00:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/1359723</guid>
    </item>
    <item>
      <title>Bicycle and Pedestrian Travel in Chittenden County Vermont: Part III</title>
      <link>https://rip.trb.org/View/1357468</link>
      <description><![CDATA[In the first part of this project, a Transportation Research Center (TRC) team gathered automated hourly pedestrian counts from a sidewalk in downtown Montpelier, Vermont to determine if temperature, relative humidity, precipitation and wind affect the number of walkers. The researchers found that, after adjusting for time of day and day of week, weather and seasonal variables explained 30 percent of the variations in pedestrian volume -- and that bad weather such as cold temperature or precipitation consistently affected walking traffic, but by only a moderate amount (less than 20 percent). For the next part of this project, hourly distributions of non-motorized traffic data at 9 locations along shared-use paths in Chittenden County, Vermont were investigated for a linkage between total daily volumes, daily distributions, and surrounding land-use. The analysis failed to reveal significant variations in the hourly distributions relative to the land-use proximate to the count location. The findings were then used to identify temporal and spatial gaps to provide a robust, heterogeneous data set for non-motorized travel modeling and exposure estimation. Additional regression analyses was also conducted on a separate set of intersection-based non-motorized traffic counts to determine more generally if a connection exists between pedestrian and biking volumes and proximate land use. A geographically-weighted regression was performed which was sensitive to spatial autocorrelation in the dependent and independent variables. The University of Vermont (UVM) TRC is currently developing a new method of collecting non-motorized travel counts in rural locations, using a closed-circuit camera. This new method is being used to collect counts at the 18 new locations in support of the calculation of total bike and pedestrian miles of travel (BPMTs) in the County.]]></description>
      <pubDate>Sat, 13 Jun 2015 01:00:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/1357468</guid>
    </item>
    <item>
      <title>Navigating Trade-Offs in Complex Systems</title>
      <link>https://rip.trb.org/View/1357379</link>
      <description><![CDATA[Metropolitan Planning Organizations (MPOs) are required by Federal law to develop a long-range Metropolitan Transportation Plan (MTP) at least every five years. This research focuses on assessing the trade-offs between business-as-usual MTP scenario of gasoline driven transportation infrastructure and suburban growth with two alternate sustainable community design scenarios in Chittenden County Metropolitan Planning Area (CCMPO). The CCMPO adopted its last long-range transportation plan in 2005 for a temporal horizon of 2005 to 2025 and is currently updating 2025 MTP to 2035 MTP. The researchers implemented two focus groups with multiple stakeholder representatives of the regional transportation planning network and conducted numerous interviews to implement a participatory multi-criteria evaluation of 2035 MTP scenarios. Three MTP scenarios are evaluated on twelve decision criteria: operational performance, sustainable land-use, safety and accessibility, minimize time and total costs, protect built and natural environs, community development, access and mobility, transportation system efficiency, energy efficiency and conservation, improve alternate travel modes, public education and cost effective and inclusive. Research analysis reveals that the underlying expected value functions of all stakeholder representatives in the regional transportation planning network overwhelmingly reject the business-as-usual MTP scenario. Instead, a more sustainable, growth contained community design scenario emerges with the highest expected value for all stakeholder groups. Formal implementation of sustainable community design scenario would, however, require CCMPO and regional transportation planning network actors to overcome a series of legal, political and economic challenges. The researchers discuss the implications of these trade-offs, challenges and opportunities on the development and implementation of sustainable community designs.]]></description>
      <pubDate>Fri, 12 Jun 2015 01:01:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/1357379</guid>
    </item>
    <item>
      <title>Integrated Land-Use, Transportation and Environmental Modeling: Complex Systems Approaches and Advanced Policy Applications, Phase 3</title>
      <link>https://rip.trb.org/View/1357376</link>
      <description><![CDATA[This project develops, evaluates, calibrates, and validates combinations of integrated frameworks for agent-based land use and transportation models using Chittenden County as a test-bed. The project proposes to implement UrbanSim with TRANSIMS for Chittenden County and to integrate the two models together with an activity model developed by partners at RSG Inc. Future conditions shall be simulated based on alternative scenarios generated in stakeholder meetings and ranging from changed land use policy constraints to construction of new infrastructure. The impacts of the transportation sector on mobile source air pollution will be conducted using data from Vermont UTC Signature Project #2. Researchers in other projects will be providing new model output metrics to consider land cover and carbon; storm water; impacts on plants and soils; network robustness; and commodity transportation. The researchers will develop code for processes to integrate these new output metrics into the combined model in a format that services the MPO context and models. The most important aspect of project B is evaluating which of the combined model architecture components are necessary for which set of regional planning and policy questions.]]></description>
      <pubDate>Fri, 12 Jun 2015 01:01:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/1357376</guid>
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