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    <title>Research in Progress (RIP)</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>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Trip Generation and Traffic Prediction: Evaluating Alternative Traffic Prediction Methods for Traffic Impact Analysis</title>
      <link>https://rip.trb.org/View/2721744</link>
      <description><![CDATA[ Traffic impact analysis (TIA) forecasts how a proposed development will affect the surrounding transportation system and what improvements, if any, are needed to improve safety and efficiency.  In addition to trips generated by the proposed development (typically based on rates published by the Institute of Transportation Engineers [ITE], a TIA will include “background traffic” which is traffic on the roadway generated from other sources. The assumption is that background traffic growth is not driven by the proposed development, nor by other parcels approved but not yet built within the study area.  The purpose of this research is twofold: (1) to determine the extent to which this assumption is valid and (2) if the assumption is not valid, to identify best practices to account for this.  This research will thus (1) review practices from other states regarding ways to combine background traffic growth and site-specific trip generation in the TIA process; (2) compare forecast and observed trip generation for a selected set of developments; and (3) conduct an additional case study to compare the traditional approach of doing a TIA (based on ITE rates) and an approach based on a travel demand model.  Lessons learned from this effort may inform Virginia Department of Transportation (VDOT)’s Traffic Impact Analysis guidelines.  This research need tied with another for being the top-ranked research need by the Transportation Planning Research Advisory Committee (TPRAC).]]></description>
      <pubDate>Thu, 02 Jul 2026 11:02:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2721744</guid>
    </item>
    <item>
      <title>Research for the AASHTO Standing Committee on Planning. Task 90. Best Practices in the Use of Microsimulation Models</title>
      <link>https://rip.trb.org/View/2706285</link>
      <description><![CDATA[Research resulted in a report that summarizes whether there is consensus on the state of the practice regarding where, when and how micro simulation modeling can be best supported, justified, and cost effective.
]]></description>
      <pubDate>Wed, 27 May 2026 15:09:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2706285</guid>
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    <item>
      <title>Research for the AASHTO Standing Committee on Planning. Task 96. Selecting and Using Advanced Travel Demand Modeling Tools - A Peer Exchange</title>
      <link>https://rip.trb.org/View/2706282</link>
      <description><![CDATA[A peer exchange was conducted on September 12-13, 2009 at the Beckman Center of the National Research Council in Irvine, California as a companion activity to NCHRP 08-36, Task 90 Practices in the Use of Microsimulation Models.  The objective of NCHRP 08-36, Task 90 was to analyze how and when selected states and metropolitan planning organizations are using micro simulation models, the nature of results being obtained, and whether organizations believe that the effort and expense associated with micro simulation models is warranted. The final report for Task 90 includes summary information about the peer exchange.]]></description>
      <pubDate>Wed, 27 May 2026 14:58:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2706282</guid>
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    <item>
      <title>The Effects of Complete Streets Policies and Projects on Local Business Development and Growth in California</title>
      <link>https://rip.trb.org/View/2696848</link>
      <description><![CDATA[This project aims to explore the effects of Complete Streets policies and projects on local business development in California. The project uses a mixed-methods research design and micro-level business databases to explore how Complete Streets influences job accessibility, business attraction, business survival rates, and the broader transformation of mixed land use surrounding Complete Streets project sites. It investigates the interplay between Complete Streets projects and local business dynamics, particularly focusing on the clustering or dispersion of businesses for the agglomeration economy. By comparing the range of Complete Streets policies adopted by different entities, such as state agencies, counties, Metropolitan Planning Organizations, and cities, within varying sociodemographic, environmental, economic, and physical contexts, the research assesses their influence on transportation improvement plans and economic development strategies. The primary objectives of the research comprise three major research tasks. First, it aims to evaluate how Complete Streets policies impact changes in travel demand and behavioral patterns, considering the allocation of various transport modes. Second, the research examines the effects of proximity to Complete Streets project sites on the growth and development of local businesses. Finally, the project seeks to understand the perspectives of transportation agencies and local governments regarding the urban environment modifications driven by Complete Streets initiatives, particularly their implications for local business growth and broader land use changes. By employing mixed methods, the research aims to provide comprehensive insights that inform policymakers, urban planners, and business owners of strategies to refine Complete Streets policies for better local business development.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:07:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696848</guid>
    </item>
    <item>
      <title>Monitoring Active Transportation Demand and Safety with Computer Vision</title>
      <link>https://rip.trb.org/View/2696847</link>
      <description><![CDATA[Monitoring demand for and safety of active transportation has been a challenge for decades. With a history of designing roads for cars and monitoring efforts similarly aimed at the flow of cars, transportation researchers and professionals lack system-level knowledge of active transportation. The current state of bicycle and pedestrian counting practice in most cities deploys a few costly permanent counters using inductive loops and passive radar, combined with a few days of manual peak hour traffic counts at few intersections. This neither monitors system-wide demand nor safety. However, recently several companies have produced video- and LiDAR-based sensors and multiclass tracking technology to monitor active transportation demand and unsafe events. These sensors can be installed permanently, or temporarily, and are generally lower in cost to install than other permanent counting devices. This research will leverage an ongoing Caltrans project with these sensors to validate safety metrics, and a mobile version of the sensors to collect active transportation count data for modeling system level active transportation volume in Davis, California as a pilot for other cities and agencies. It will include the prediction of network-wide travel volumes for planning the intervention purposes, and two safety metric evaluations. The final report is expected to not only provide information on the state-of-the-art in active transportation monitoring, but will have direct policy impacts by informing the Active Transportation Data program within Caltrans Traffic Operations, among other programs such as the Active Transportation Resource Center research-to-practice education elements.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:05:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696847</guid>
    </item>
    <item>
      <title>Which Way Forward? Learning from Global Informal Transport Networks to Inform Microtransit Services in California</title>
      <link>https://rip.trb.org/View/2695811</link>
      <description><![CDATA[This proposed 12-month study seeks to draw upon lessons learned from informal transit systems, particularly from the developing world, to inform the development and implementation of demand-responsive transit (often referred to microtransit) strategies in California. Through a comprehensive review of existing literature, case studies (n= up to 5), and expert interviews (n=15-20), this study aims to identify lessons learned, challenges, and opportunities associated with informal transit operations. Leveraging this understanding, the research will assess how such lessons can be applied to the design, deployment, and evaluation of microtransit and other demand-responsive services in California communities, including transportation network companies (TNC) and taxi models. Key areas of focus include business and operational models, fare affordability and financial sustainability (including operational costs), and potential policy frameworks. By synthesizing insights from informal transit experiences internationally, this proposed study seeks to contribute to the development of efficient and sustainable microtransit and demand-responsive strategies tailored to the diverse needs of all travelers.]]></description>
      <pubDate>Thu, 23 Apr 2026 18:05:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2695811</guid>
    </item>
    <item>
      <title>Implementing an Advanced Open-Source Activity Based Travel Demand Model to Support Rural Transportation Planning and Policy Decisions</title>
      <link>https://rip.trb.org/View/2692312</link>
      <description><![CDATA[Travel demand models (TDMs) are used to support state and regional transportation planning and policy decisions. TDMs were originally developed to forecast passenger traffic volumes with the primary objective of identifying investments to reduce traffic congestion. Today, TDMs are used to support a much broader range of purposes, including multimodal and freight transportation planning, demand management strategies, forecasting accessibility outcomes, evaluating network resiliency to disasters, and modeling air quality and public health impacts. However, the aggregate, trip based TDMs used by most regional and state transportation agencies lack the fidelity and sensitivity to evaluate contemporary planning and policy decisions. Activity based travel demand models (ABMs) offer substantial improvements and their agent-based simulation platforms allow for integration with a wide range of other agent-based modeling including land use simulation, vehicle adoption, population growth simulation models among others. Despite their advantages, the complexity of ABMs has constrained their adoption to all but the largest metropolitan areas, often with support from academic researchers. Smaller urban areas and rural states like Vermont could benefit substantially from adopting ABMs. The goal of this project is to implement an open source and/or free for public use ABM in Vermont. Several ABMs meeting these criteria have been developed by US Department of Energy labs. This project will implement a modeling platform that University of Vermont can use in partnership with regional and state stakeholders to advance rural transportation planning and policy research; perform a case study to demonstrate the unique capabilities of ABMs to inform current transportation policy debates in Vermont; identify implementation barriers; and identify future research directions to address implementation barriers to enable wider ABM adoption outside of large urban areas.]]></description>
      <pubDate>Tue, 14 Apr 2026 12:09:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2692312</guid>
    </item>
    <item>
      <title>Implementing an Advanced Open-Source Activity Based Travel Demand Model to Support Rural Transportation Planning and Policy Decisions: Phase 2 – Calibration</title>
      <link>https://rip.trb.org/View/2691726</link>
      <description><![CDATA[Travel demand models (TDMs) are used to support state and regional transportation planning and policy decisions. TDMs were originally developed to forecast passenger traffic volumes with the primary objective of identifying investments to reduce traffic congestion. Today, TDMs are used to support a much broader range of purposes, including multimodal and freight transportation planning, demand management strategies, forecasting transportation access outcomes, evaluating network resiliency to disasters, and modeling air quality and public health impacts. However, the aggregate, trip based TDMs used by most regional and state transportation agencies lack the fidelity and sensitivity to evaluate contemporary planning and policy decisions. Activity based travel demand models (ABMs) offer substantial improvements and their agent-based simulation platforms allow for integration with agent-based population growth and land use simulation tools, among others. Despite their advantages, the complexity of ABMs has constrained their adoption to all but the largest metropolitan areas, often with support from academic researchers. Smaller urban areas and rural states like Vermont could benefit substantially from adopting ABMs. The goal of this project is to continue current National Center for Sustainable Transportation (NCST)-funded work on implementing a statewide ABM in Vermont using the POLARIS modeling system developed by Argonne National Lab. The current project is focused on initial model setup and testing. This Phase 2 project will focus on calibration and validation. The expected outcome is a calibrated implementation of the POLARIS modeling system for the state of Vermont that can be used for the evaluation of statewide and regional transportation planning and policy decisions and to advance research on rural transportation challenges.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:58:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691726</guid>
    </item>
    <item>
      <title>The San José's Mobility Credit Pilot: A Delayed Randomized Control Trial Evaluation</title>
      <link>https://rip.trb.org/View/2691659</link>
      <description><![CDATA[The San Jose Mobility Credit pilot (MCP) tests a new approach that allows individuals the freedom to travel when, where, and how they want to go. The pilot provides MCs that enable individuals to maximize travel while minimizing costs. Interest in these programs is growing throughout the U.S. The research team has experience evaluating similar programs in the U.S. The project will include a delayed longitudinal randomized control trial (RCT) to evaluate the MCP. The design of the 18-month MCP in-person participant recruitment, training, and support by the City of San Jose will support high participation and survey response rates. The study will be the first to use a delayed RCT design with a difference-in-differences (DID) statistical analysis to evaluate an MCP. In general, RCTs are rarely used to test the effectiveness of transportation projects and policies. The proposed study will evaluate the effects of the MCP, not only on individuals’ overall travel freedom, but also on transportation security (e.g., travel speed, time, and reliability), community participation (e.g., church, family, school, and volunteer activities), employment, education, and overall health (which could lead savings in health care costs). Few studies have evaluated the significance of transportation access interventions on these measures. The longer duration of the MCP may allow for a better assessment of evaluation measures. The MCP evaluation will be one of few studies that examine the causal effects (randomized control trial with difference-in-differences analysis) of a transportation intervention on multiple evaluation measures.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:10:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691659</guid>
    </item>
    <item>
      <title>Scaling Shared Autonomous Vehicle Services: Adoption, Demand, and System Implications</title>
      <link>https://rip.trb.org/View/2691658</link>
      <description><![CDATA[Autonomous vehicle (AV) operations are expanding across major U.S. cities, prompting public agencies and industry stakeholders to consider how shared AV services should be deployed, scaled, and integrated into existing transportation systems. Shared AVs have the potential to serve a wide range of travelers, including individuals who currently drive but may choose to use AVs occasionally, those seeking alternative travel options that allow more productive use of travel time, and travelers who may not consistently rely on a private vehicle for day-to-day transportation. Despite growing deployment, empirical evidence remains limited on how shared AV services will be adopted across regions, travel needs, and service contexts, and how their expansion can be guided to align with observed demand and system performance goals. Most prior AV studies were conducted before widespread deployment and relied on respondents with little to no direct exposure to AV services. As AV operations expand, more individuals are encountering these vehicles firsthand as passengers, road users, or through broader media exposure, creating a timely opportunity to reassess their implications for travel behavior and system-level outcomes. This transition from limited testing to sustained operations highlights the need for updated evidence that reflects current deployment conditions and real-world exposure. This study will generate policy-relevant evidence to support informed shared AV deployment by examining adoption expectations, anticipated use by trip purpose, and geographic variation across urban, suburban, and rural areas. Using data from the UC Davis Mobility Panel Survey and a targeted convenience sample from regions with active AV operations, researchers will analyze anticipated shared AV use for commuting, shopping, escorting, and healthcare travel. The project will also assess how service attributes such as pricing, wait times, and availability influence adoption and demand across various geographic contexts. By identifying where shared AV services are most likely to complement existing transportation services, the study will provide actionable guidance on deployment strategies, service design, and policy considerations, supporting policymakers and industry stakeholders in evaluating scaling pathways and system impacts as services expand.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:06:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691658</guid>
    </item>
    <item>
      <title>A Data-driven Approach in Improving Truck Parking Efficiency</title>
      <link>https://rip.trb.org/View/2684213</link>
      <description><![CDATA[Freight transportation systems are a critical component of the United States' economy, underscoring the importance of adequate truck parking to ensure safe and efficient operations. However, a significant disparity between truck parking demand and supply has resulted in numerous challenges, including increased road safety risks, regulatory non-compliance, and operational inefficiencies. This study aims to address this knowledge gap by conducting a comprehensive review of current truck parking management approaches, with a focus on data-driven prediction models, and truck parking pattern analysis. In collaboration with the North Carolina Department of Transportation (NCDOT), the study will analyze truck parking patterns along key freight corridors and develop data-driven solutions to enhance parking efficiency and address these pressing challenges.

This project aims to address this gap by conducting a comprehensive review of existing literature and offering a nuanced exploration of potential truck parking solutions. Using NC as a case study, the project will provide data-driven recommendations to improve the efficiency and utilization of existing parking facilities along key freight corridors. By enhancing the safety and efficiency of truck parking, this study will directly benefit truck operators, supply chain stakeholders, regulatory agencies, and local communities. The findings will serve as a foundation for informed policymaking and infrastructure planning, ensuring that North Carolina’s freight transportation network remains resilient, sustainable, and operationally efficient in the face of growing demands.]]></description>
      <pubDate>Wed, 25 Mar 2026 17:16:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684213</guid>
    </item>
    <item>
      <title>Utility of Improving Nonmotorized Volume Forecasts for Bike Infrastructure</title>
      <link>https://rip.trb.org/View/2681257</link>
      <description><![CDATA[Virginia Department of Transportation (VDOT) lacks clarity on several foundational questions for bicycle and pedestrian demand forecasting: (1) the accuracy of the current forecasting method(s), (2) the full range of decisions that would benefit from more precise demand estimates, (3) the availability and reliability of existing bicycle and pedestrian count data, and (4) whether a more advanced forecasting method could be effectively adapted for Virginia. Given these four unknowns and the anticipated large expense of a Virginia-specific model, it is unclear whether VDOT should spend substantial time and resources creating a better approach for estimating nonmotorized demand. Through a literature review, survey and potentially follow-up interviews, assessment of alternative methods, evaluation of existing count data, and data analysis to evaluate the utility of improving forecasts, this research will determine if VDOT should develop a better method or continue the current approach.  ]]></description>
      <pubDate>Tue, 17 Mar 2026 09:48:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2681257</guid>
    </item>
    <item>
      <title>UAM-enabled Multimodal Analysis of Transportation Systems for LA28 and beyond</title>
      <link>https://rip.trb.org/View/2676008</link>
      <description><![CDATA[The Los Angeles region has long been projected as a testing ground for urban air mobility (UAM), comprised of air taxis and drone delivery, given the region’s favorable climate, traffic problems, and tech-savvy ecosystem. The LA28 Olympic and Paralympic Games present an opportunity to make such a testing ground a reality. This project will model the potential for mode shifts, from ground to air taxi modes, with the LA28 Games as an initial case study. Modeling mode shift requires modeling the operation of an air taxi system. For that reason, this project will develop algorithms for optimal dispatch operation of a network of air taxis during LA28 and thereafter, and use those results to study the resulting mode shift from other ground-based modes of transportation. The results of this research can inform the work of the White House Task Force on the 2028 Summer Olympics (Established by Executive Order 14328), which includes the Secretary of Transportation. The results will also be relevant to both the public and private sector entities planning Olympic Games travel. By developing improved dispatch operation models for air taxis in a major urban area, and then predicting mode shifts from/to other ground modes, this research will also develop knowledge that will be helpful throughout Region 9 and the U.S. and which can help accelerate the maturing of the air taxi sector.]]></description>
      <pubDate>Tue, 03 Mar 2026 16:34:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676008</guid>
    </item>
    <item>
      <title>Evaluating Fuzzed Connected Vehicle Data to Support Travel Demand Modeling </title>
      <link>https://rip.trb.org/View/2663276</link>
      <description><![CDATA[The Virginia Department of Transportation (VDOT) is currently developing a method to use connected vehicle (CV) data to support the development of travel demand models. The work includes estimating nuanced information about trip time, trip distance, and path patterns with fine geographic and temporal resolution. CV trajectory data providers may use algorithms that affect the raw vehicle trajectory data for privacy reasons. These algorithms may affect the feasibility, accuracy, and robustness of VDOT’s application of CV data for planning purposes. This project assesses the impact of such algorithms used by two different CV trajectory data providers on potential VDOT application scenarios related to calibration and validation of transportation planning models. This research will further assess the potential of using such data to support the development of truck ODs, and if feasible, a valuable enhancement to the current VDOT truck origin-destination (OD) estimation procedure.
]]></description>
      <pubDate>Sun, 01 Feb 2026 11:00:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663276</guid>
    </item>
    <item>
      <title>Strategic Approaches to Managing Emerging Transportation Infrastructure Assets Through Public-Private Partnership</title>
      <link>https://rip.trb.org/View/2658058</link>
      <description><![CDATA[Oklahoma is currently undergoing major transportation infrastructure network expansions statewide yet faces unique challenges especially in low population regions with insufficient travel demand and questions of economic viability. This project aims to develop a business case for the management of emerging transportation infrastructure assets for different regions in Oklahoma by analyzing best practices from other states, assessing the interdependence between infrastructure assets and travel demand, and evaluating innovative funding and partnership models. The project will focus on charging infrastructure for alternative fuel vehicles as the use case. The research will identify strategies to reduce long-term maintenance cost, increase technology adoption, and prioritize locations for infrastructure expansions based on short-range and long-term community needs and economic impacts. Key tasks include a (1) comprehensive literature review and policy benchmarking, (2) vulnerability, interdependency, and accessibility analysis, (3) key stakeholder engagement, (4) economic and technical feasibility analysis, (5) development of asset management strategies and implementable guidelines for Oklahoma DOT and its partners. The anticipated outcomes include actionable recommendations to support the long-term financial viability of transportation infrastructure asset management, promote access, and foster economic growth in different communities. Overall, the proposed research will analyze the economic feasibility of emerging transportation infrastructure asset management strategies through cost-benefit assessments and investment justifications, strengthening the case for federal and private funding. Its alignment with national priorities and ODOT’s goals ensures the findings are both timely and impactful. Based on the results, ODOT may need to revise Oklahoma’s Transportation Asset Management (2022-2031) and Long Range Transportation (2022-2031) plans to incorporate updated guidelines on financial viability, location priorities, and infrastructure life cycle management. Implementing these changes before future expansions will improve efficiency and ensure smoother project delivery. The results will directly contribute to the state’s mission of building a safer, more reliable, and efficient transportation system.  ]]></description>
      <pubDate>Fri, 23 Jan 2026 13:43:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2658058</guid>
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