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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>Research in Progress (RIP)</title>
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      <title>Last-Mile Warehousing and Fulﬁllment Development in North Carolina: Safety, Trafﬁc, and Quality of Life Impacts</title>
      <link>https://rip.trb.org/View/2491049</link>
      <description><![CDATA[Expansion of e-commerce, especially throughout the COVID-19 pandemic, has rapidly changed the way goods reach customers’ homes. As the volume of parcels has surged (from 15.4 billion in 2019 to 21.7 billion in 2021), e-commerce companies like Amazon continue to expand their logistics footprint to match demand by building warehousing and fulfillment centers. This development reflects an industry shift from a national distribution model to a regional model, which positions a wider array of goods closer to North Carolina’s population centers. While this shift enables faster, cheaper shipping of goods to customers, it entails substantial impacts on transportation patterns, infrastructure use, safety, and quality of life. For example, locating facilities closer to population centers means increased volumes of linehaul transport, delivery vans, and employee traffic in areas previously unaccustomed to these uses. Moreover, there is concern over illegal overnight truck parking on state roads, as these facilities attract trucks but do not provide overnight parking for trucking.

This project will: a) examine current development approval processes for warehousing in North Carolina; and b) analyze and document the impacts that these developments are having on traffic volumes, safety, and quality of life. The research team addresses these goals specifically by examining emerging siting patterns, development/approval processes, traffic volume and crash data, and impacts on surrounding communities of fulfillment centers and delivery stations in North Carolina. Based on these impacts, the research team will provide guidance for future Traffic Impact Analysis (TIA) and approval processes for these facilities.

These issues are of critical importance to the North Carolina Department of Transportation (NCDOT) in several ways, but most crucially in that a) NCDOT is responsible for design and oversight of TIA on state roads, b) NCDOT is often called upon to fund extensive road infrastructure projects to support new fulfillment centers, c) NCDOT is financially and logistically responsible for maintaining road infrastructure that serves these centers, and d) improving traffic safety, including that related to commercial vehicles, is a key priority for NCDOT. Moreover, these goals have been recently highlighted in the context of freight in NCDOT’s Statewide Multimodal Freight Plan. These issues are also of critical importance to a broad spectrum of North Carolina communities. Rural communities are often chosen for siting due to cheap land and ease of approval processes, and can see radical transformations in traffic volumes, affecting safety, air quality, and nearby residents. Urban communities, meanwhile, are often the target markets for these last-mile facilities, and can thereby endure increased commercial traffic volumes, congestion, safety issues, and a documented increasing problem with illegal long haul truck parking. The research team's investigation will provide NCDOT, local municipalities, and other partners with insights to make future decisions about siting, as well as to design long term policy to accommodate the continued growth of e-commerce and the infrastructure that supports it.]]></description>
      <pubDate>Wed, 15 Jan 2025 16:11:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2491049</guid>
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      <title>NCHRP Implementation Support Program. Implementing the Areawide Approach to Roadway Safety Management and Safety Planning</title>
      <link>https://rip.trb.org/View/2213755</link>
      <description><![CDATA[Quantitative safety analysis is a relatively new field, and crash prediction modeling and other data-driven safety analysis procedures traditionally focus on the project level within state departments of transportation (DOTs). Many decisions affect safety or constrain safety improvement options later in a project’s lifecycle, and these decisions could benefit from quantitative safety analysis. Macro-level crash prediction models (CPMs) are a quantitative safety analysis method that predict an average crash frequency, by crash type and severity, for a defined area, such as a census block group, tract, traffic analysis zone, or county.
NCHRP Research Report 1044: Development and Application of Quantitative Macro-Level Safety Prediction Models describes macro-level CPMs designed for a quantitative consideration of safety in planning-level decisions. This supports a proactive and comprehensive approach to addressing safety needs for all road users. Macro-level CPMs were developed for inclusion in the AASHTO Highway Safety Manual, second edition, in a chapter titled, “Areawide Approach to Roadway Safety Management.” The development process involved utilizing state- and metropolitan planning organization (MPO)-level data from three MPOs (Richmond, VA; St. Louis, MO; and Chicago, IL) and two states (Illinois and Utah).
NCHRP Research Report 1044 observed a high level of consistency between the individual agency models, as well as the ability to integrate data and models into a combined model that incorporated more than one agency. Supplementing NCHRP Research Report 1044 are (a) a spreadsheet tool to support the testing and implementation of the macro-level CPMs; and (b) NCHRP Web-Only Document 348: Macro-Level Analysis of Safety Planning and Crash Prediction Models: A Guide, which provides guidance on how to use the spreadsheet tool.
There is a need to provide technical guidance and training to state DOTs to implement the CPMs from NCHRP Research Report 1044 to address safety needs across diverse scenarios.
OBJECTIVES
The objectives of this research are to (a) provide technical help to state DOTs to implement the macro-level CPMs from NCHRP Research Report 1044, (b) conduct pilot projects with state DOTs, and (c) develop supplemental materials for the macro-level CPMs based on the results of the pilot projects.
]]></description>
      <pubDate>Mon, 17 Jul 2023 23:37:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2213755</guid>
    </item>
    <item>
      <title>RES2020-18: Developing Statewide Land Use Forecasting Model and Integrate with TDOT's Statewide Travel Demand Model</title>
      <link>https://rip.trb.org/View/1716835</link>
      <description><![CDATA[In this research, a statewide forecasting land-use model is presented. The land-use model is called the LargeScale Land Use Model (LS-LUM) and can be integrated with Tennessee Statewide Travel Demand Model (TSM
v3). Massive data collection was followed in this project and households’ information, employment information,
housing information was collected at the Traffic Analysis Zones (TAZ) level. Moreover, parcel data for 94 counties
were collected to be incorporated into the model. The model is developed for the base year 2010 and forecasts the
demographic and socio-economic condition of the state of Tennessee from 2015 to 2050 with five years intervals.
The developed model is validated using the backcasting approach and the goodness of fit and error measures are
provided to show the accuracy of the model. To present the result of the model, an online dashboard is created
providing forecasting results from 2015 to 2050 at TAZ and County levels Moreover, the online dashboard
presents a brief statistical analysis. The developed land-use model is integrated with TSM v3 and results are
provided. Since the model incorporates house conditions (total houses and vacant houses) and land use conditions
in each TAZ (residential, commercial, industrial, agricultural, and developable lands), it provides a powerful tool
for policy analysis. A software is developed for running the model using MATLAB Compiler Runtime, which
enables sharing the model with other parties.

]]></description>
      <pubDate>Mon, 29 Jun 2020 14:12:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/1716835</guid>
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      <title>Spatial Disaggregation of California Freight Demand</title>
      <link>https://rip.trb.org/View/1448967</link>
      <description><![CDATA[In recent years the role of statewide freighting forecasting models has been expanded to much finer levels of analysis than regional or even county levels, those being the most disaggregate spatial levels for which public freight data sources are typically available. In partnership with other state agencies and Metropolitan Planning Organizations (MPOs), Intelligent Transportation System Irvine (ITSIrvine) has completed development for the California Department of Transportation (Caltrans) of the  California Statewide Freight Forecasting Model (CSFFM). A critical challenge was to provide a framework for organic integration between the CSFFM and a finer spatial level such as that in the new California Statewide Travel Demand Model (CSTDM) to meet Caltrans and MPO needs. Factoring methods are currently largely used for disaggregating freight demand. Such methods cannot adequately capture the complex structure and behavior of freight movements, advances in logistics, information technology, and relocating infrastructure at the MPO level. One advantage of the CSFFM, modal path-based origin-destination (OD) representation, cannot be fully utilized by MPOs because factoring methods tend to break the chains of modal path-based information in the conversion to tripbased information. This research initially sought to explore and develop truck tour-based models for disaggregating CSFFM from an aggregate Freight Analysis Zone (FAZ) level to the more disaggregate Traffic Analysis Zone (TAZ) level in CSTDM, by using truck GPS data from the American Transportation Research Institute (ATRI). Expected results included new and improved insights into the spatial and temporal operations of trucks at the urban and MPO level, contribution to the statewide-related component of urban freight modeling, and an evaluation of traffic and environmental impacts of state-level policies and air pollution mitigation strategies. However, after detailed investigation of the ATRI GPS data it was concluded that several problems with the data made it inadequate for disaggregating a CSFFM truck matrix for about 200 FAZs to the CSTDM 5000 TAZ level. Therefore, a new approach was developed. It involved estimation of a direct demand model at the CSFFM FAZ level using as inputs only independent variables readily available at CSTDM's level of aggregation and, as dependent variables, the final truck matrices estimated by CSFFM. CSFFM outputs can then be applied to CSTDM's zoning system with the resulting estimates being appropriately scaled.]]></description>
      <pubDate>Thu, 02 Feb 2017 11:17:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/1448967</guid>
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    <item>
      <title>Development and Evaluation of a Residential Allocation Model Using Time Series Tax Parcel Data in GIS</title>
      <link>https://rip.trb.org/View/1229826</link>
      <description><![CDATA[Residential projection allocation models in Delaware currently function at an aggregate level typically producing data for modified grids, traffic analysis zones, or census geographies. These methods are satisfactory for developing draft allocations subsequently reviewed by agency staff prior to use in travel models or other planning efforts. This project intends to develop an additional allocation model which can be used to supplement existing methods and will be especially beneficial to applications in small-area transportation or land use planning studies.]]></description>
      <pubDate>Thu, 03 Jan 2013 13:49:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/1229826</guid>
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