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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>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Transportation Corridor Fuel Consumption Calculator (TCFCC) Version 5.0</title>
      <link>https://rip.trb.org/View/2692310</link>
      <description><![CDATA[The Transportation Corridor Fuel Consumption Calculator (TCFCC) updates and enhances Georgia Tech’s 2018 spreadsheet-based modeling tool (http://fec.ce.gatech.edu/) that allows users to assess on-road fuel consumption under real-world traffic conditions. The team will incorporate the latest fuel use rates from the MOVES 5.0 model (2025) and extend the capabilities of the previous FEC to allow users to specify any one of more than 60 standard laboratory driving cycles that best represent corridor traffic congestion, and to incorporate any monitored or modeled second-by-second driving trace. Users specify fleet and model year composition, and the tool models corridor-level fuel consumption as a function of congestion. Hence, the tool allows users to assess the consumer fuel savings and cost savings of proposed congestion mitigation strategies that provide smooth traffic flow. The tool is directly applicable to the assessment of traffic signal coordination, ramp metering, express lane operations, etc. The research team will update the model to incorporate MOVES 5.0 model outputs, extend calendar year coverage to 2060, and introduce 40+ new driving cycles that are representative of urban, suburban, and freeway corridors. The project will deliver separate calculator spreadsheets for light-duty passenger cars, heavy-duty trucks, and express buses, each calibrated for mode-specific load factors and driving patterns. A new second-by-second fuel-use worksheet will allow users to input their own driving cycles for detailed vehicle-specific analysis. By focusing on fuel consumption, the project provides a technically neutral and performance-based approach for evaluating corridor operations and fleet technologies. The center will release the TCFCC as open source, encouraging further development and integration with travel demand and simulation models.]]></description>
      <pubDate>Tue, 14 Apr 2026 12:07:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2692310</guid>
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    <item>
      <title>Modeling Event Travel Dynamics for the 2028 Los Angeles Olympics Using Large-Scale Mobility Data</title>
      <link>https://rip.trb.org/View/2676006</link>
      <description><![CDATA[The 2028 Los Angeles Olympics will require innovative transportation strategies to move hundreds of thousands of travelers reliably to and from events. The challenge is that mega-events, such as the 2028 Los Angeles Olympics, produce travel behaviors that differ from routine patterns and which exceed the scope of existing planning models. Traditional data sources, such as travel surveys and long-term regional transportation models, cannot capture short-term behavioral changes in response to major events. Large-scale human mobility data from smartphones, which continuously updates, now enable direct observation of how millions of people adjust their travel in and around major events in high spatial and temporal resolution, and can form the basis for forecasting models for future events. This project will leverage large-scale mobile data to build a foundation for modeling mega-event travel. The 2028 Olympics present an obvious application, and this research can inform the work of the White House Task Force on the 2028 Summer Olympics (established by Executive Order 14328). This project’s results will also inform innovations in transportation planning models well beyond the Olympics, pioneering adaptations of mobile data that with follow up work could model travel patterns from novel events such as evacuations or changes to infrastructure to accommodate safety, health, economic, or seasonal needs.]]></description>
      <pubDate>Tue, 03 Mar 2026 16:26:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676006</guid>
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    <item>
      <title>Evaluating Autonomous Vehicles’ Safety Benefits in Mixed Autonomy Scenarios</title>
      <link>https://rip.trb.org/View/2292640</link>
      <description><![CDATA[Connected autonomous vehicles (CAVs) are gradually advancing towards widespread deployments. CAVs promise to improve transportation safety by operating more efficiently and avoiding incidents like crashes due to human driver error. However, they may cause incidents themselves, especially when interacting with humans. The goal of this project is to evaluate the potential safety benefits of CAVs in mixed-autonomy settings, in which CAVs and human vehicles share the road. This work has three parts: (i) estimating the effective incident rates of CAVs and how they are distributed across a city, leading to algorithms for prioritizing incident responses so as to reduce their overall impact on traffic flow and safety; (ii) incorporating CAVs’ and human drivers’ ability to react to human pedestrians, leading to algorithms for CAVs to reduce pedestrians’ impact; and (iii) evaluating models and analysis in a mixed-autonomy simulator.  Towards modeling CAVs’ effect on traffic incident rates, the research team will account for the fact that vehicle incident rates vary with the road congestion level and type, e.g., Pennsylvania data show that incidents are more common in heavy-traffic surface streets than sparsely populated highways. The team will build on their prior Mobility21 work studying mixed-autonomy traffic patterns to account for changes in congestion levels across the road network due to vehicle incidents, e.g., if CAVs overall reduce the incident rate on highways, this might lead to better overall traffic flow and fewer subsequent incidents. The results will enable prioritization of incident response so as to maximally reduce the resulting traffic congestion.  The team will then incorporate the effects of human pedestrians into their mixed-autonomy setting. Pedestrians can change safety dynamics as their actions may be more difficult to predict, especially for CAVs that may not be well-trained on pedestrian data. For example, CAVs can improve traffic flow by more closely following other vehicles; this is less feasible when human pedestrians are present. The team therefore plans to incorporate these pedestrian “shocks” into  their model of traffic flow and incident rates. The team will use these results to propose new techniques for CAVs to predict and plan for pedestrian behaviors.  The team will use their existing mixed-autonomy simulator, developed with Mobility21’s support, to numerically evaluate their models and how the above safety effects vary for different amounts of CAVs. The team will also leverage models and feedback from their deployment partner, the Southwestern Pennsylvania Commission (SPC), in their simulations. The team will further measure how CAVs’ effects are distributed around a city and implications for equity (see also “Outputs” below).  This project is synergistic with the concurrently submitted proposal entitled “Mitigating Cascading Failures for Safety in Transportation Networks in the era of Autonomous Vehicles,” where the goal is to evaluate the safety impact of AVs from the perspective of their impact on cascading road failures and congestion. In contrast, the current project focuses on CAVs’ safety impact in terms of the traffic incident rate in mixed-autonomy settings. As such, the two projects complement each other and can be combined at a total budget of $150,000 if preferred.]]></description>
      <pubDate>Mon, 20 Nov 2023 19:35:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/2292640</guid>
    </item>
    <item>
      <title>Synthesis of Information Related to Highway Practices. Topic 55-13. Practices for Operational Traffic Simulation Models</title>
      <link>https://rip.trb.org/View/2190446</link>
      <description><![CDATA[The objective of this synthesis project was to document state department of transportation (DOT) processes and procedures for operational traffic simulation models. 
]]></description>
      <pubDate>Mon, 05 Jun 2023 16:21:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2190446</guid>
    </item>
    <item>
      <title>Traffic Analysis, Modeling, and Simulation</title>
      <link>https://rip.trb.org/View/1728174</link>
      <description><![CDATA[The Traffic Analysis, Modeling, and Simulation (TAMS) PFS is intended to serve as a forum and provide an opportunity for the participants to identify, address, and collectively tackle key issues and challenges that are common among public agencies in conducting, managing, and/or approving traffic analysis and simulation studies. The TAMS PFS will address key technical and programmatic traffic analysis issues through the investigation and development of best practices, lessons learned, and recommended guidelines or methodologies. The TAMS PFS will also provide an opportunity to facilitate the interaction, sharing of information, and exchange of knowledge with a broader audience to advance and improve upon the current state-of-the-practice related to the usage, management, and/or approval of traffic analysis and simulation tools.  The goal of this study is to improve the state-of-the-practice in traffic analysis, modeling, and simulation to enable public agencies to make the best possible transportation investment decisions based upon high-quality traffic analyses. The objectives of this study are to assemble federal, state, regional, and local agencies to: (1) identify challenges and issues common among those responsible for conducting, managing, and/or approving traffic analysis and simulation studies; (2) suggest approaches to address identified challenges; (3) initiate and monitor projects intended to address identified challenges and issues; (4) develop and disseminate noteworthy practices, recommendations, and results; and (5) promote and facilitate technology transfer related to traffic analysis and simulation issues nationally.]]></description>
      <pubDate>Wed, 12 Aug 2020 14:13:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/1728174</guid>
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    <item>
      <title>Predicting Lane Change Intensity within Urban Interchange Influence Areas (IIA)</title>
      <link>https://rip.trb.org/View/1723548</link>
      <description><![CDATA[This research is intended to assist NCDOT in improving mobility and safety performance at urban interchange influence areas (IIA's) in North Carolina, to remedy the excessive levels of discretionary lane changes occurring at those locations. The research will predict how driver lane changing behavior is impacted by local traffic, by control and by site conditions. A second objective will be to ascertain whether changes in signing, markings or other traveler information near the IIA can induce fewer discretionary lane changes and thus reduce unnecessary traffic turbulence near interchanges. Currently NCDOT has no means to track lane changing behavior. This research will take advantage of an existing (and continuously expanding) NC State high resolution second by second trip database which uses an in-vehicle OBD-II unit (called i2d) in the Triangle Region. Supplemented with controlled experiments and other data sources the research will produce predictions of lane changing behavior at the vehicle scale, based on present IIA geometrics, the prevailing traffic states and any implemented lane-discipline-inducing treatments. To achieve these objectives, we are proposing to develop a statistical model to predict lane change intensity in urban interchange influence areas. Lane changes will be characterized as mandatory or discretionary. Our i2d data can inform us which type of lane change it is, based on knowledge of the trip origin and destination. Initially, however we will assume that all lane changes are discretionary except when it is known that the vehicle is either entering or exiting the IAA. Thus our predictor variable will be the expected discretionary lane change intensity per vehicle mile in the IIA. The explanatory variables will include both road design variables at the IIA as well as traffic and environmental conditions. On the geometric side we will consider the effect of the number of lanes, the spacing between ramps, the length of acceleration/ deceleration lanes, the presence of a lane drop and the distance to the nearest upstream and downstream ramps, along with any significant grades on the mainline and ramp roadways. On the traffic stream side, the current best source of data would be existing NCDOT RTMS devices or portable counting stations that can provide lane by lane volume and speed and could also serve to validate the lane change model. We will also explore the use of the NGSIM datasets with detailed trajectories for all vehicles on several freeway facilities. In addition the research team will extract from i-PEMS sub-TMC or link speed data that have occurred in the same time interval in which the lane changes were executed. This will help our understanding of the effect of local congestion levels on the intensity of lane changes. We will discard any data associated with the presence of incidents in the IIA as these will obviously impact the lane selection behavior of drivers. Upon completion of model calibration, we intend to validate the model at IIA sites not used in the model development process. The final set of influencing variables will be the type of control/treatment at the IIA, which could include the presence of ramp metering, possible additional signing or marking to encourage certain lane shifts or an optimal lane distribution prior to entering the IIA. Depending on the schedule of NCDOT actually implementing any such intervention, the team will compare its prediction of lane change intensity in the baseline case with the observed post-treatment lane change intensity (measured via video, RTMS or i2d) and make an assessment of the effectiveness of the proposed intervention.]]></description>
      <pubDate>Thu, 23 Jul 2020 13:07:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/1723548</guid>
    </item>
    <item>
      <title>Research for the AASHTO Committee on Environment and Sustainability. Task 96. 
Quick Reference Guide for Traffic Modelers for Generating Traffic and Activity Data for Project-Level Air Quality Analyses</title>
      <link>https://rip.trb.org/View/1705847</link>
      <description><![CDATA[There is a frequent disconnect between the traffic data needed for project-level air quality analysis and the traffic data provided by traffic modelers.  The disconnect leads to waste in processing and time and introduces project risk by introducing uncertainty into the NEPA and Transportation Conformity process. The challenge stems from different technical vocabulary and knowledge-base of air quality and transportation modelers.  State DOTs need guidance that can bridge the gap between air quality and traffic modelers by articulating air quality needs with an understanding of common language and tools available to traffic modelers. 

The objective of this research is to supplement NCHRP Report 765, "Analytical Travel Forecasting Approaches for Project-Level Planning and Design" by providing guidance focusing specifically on generating traffic information for air quality analysis.  The guidance will aid traffic modelers in understanding the traffic data needed for air quality analysis. It will also contribute to improved accuracy and efficiency of the traffic and activity modeling required for federally required air quality analyses by providing guidance to modelers to help them develop data in formats that can be easily used by air quality analysts.  NCHRP Report 765 addressed the general subject of project-level modeling in detail but does not address needs specific to project-level air quality analyses. For more information on NCHRP Report 765, visit: http://www.trb.org/Publications/Blurbs/170900.aspx

The Quick Reference Guide describes traffic needs for project screening, refined analyses, and dispersion modeling of carbon monoxide (CO), particulate matter (PM), and mobile source air toxics (MSATs). Information on greenhouse gas (GHG) analysis is also included.]]></description>
      <pubDate>Mon, 11 May 2020 17:02:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/1705847</guid>
    </item>
    <item>
      <title>Assessment of Parcel Delivery Systems Using Unmanned Aerial Vehicles (Phase III)</title>
      <link>https://rip.trb.org/View/1669766</link>
      <description><![CDATA[The project will evaluate alternative parcel delivery methods that use UAVs. The different methods to be evaluated include UAVs operating independent from ground vehicle delivery, mobile depots where ground vehicles are equipped with UAVs that aid in the delivery process, and data driven approaches where the UAV provides information on the traffic state to improve vehicle route choice and UAV deployment decisions. The delivery systems will be evaluated under different levels of demand and considering different UAV capabilities in terms of range and allowable parcel size. The current objectives of the project include assessment of different parcel delivery systems that exploit drone traffic monitoring and delivery capabilities. Specifically, the research team is developing open loop control algorithms for adaptive drone deployment and truck routing. The resulting strategies are compared against exiting delivery mechanisms that do not incorporate traffic congestion. As an extension for this project, the team will evaluate novel probabilistic modeling procedures as well as techniques for estimating the probability of non-recurrent congestion at incident prone locations.]]></description>
      <pubDate>Sun, 01 Dec 2019 22:36:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/1669766</guid>
    </item>
    <item>
      <title>Sea Level Rise Resilient Transportation Systems in Coastal Communities</title>
      <link>https://rip.trb.org/View/1646763</link>
      <description><![CDATA[The research team proposes to work with coastal agencies and local governments in California to investigate the potential impact of loss of shoreline infrastructure on local multi-modal traffic circulation. The team has contacted 2 jurisdictions that have demonstrated an interest in understanding these types of impacts. The team will suggest how the combination of flood-risk and traffic circulation modeling can improve resilience decision-making by local, regional and state governments.]]></description>
      <pubDate>Wed, 21 Aug 2019 15:11:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/1646763</guid>
    </item>
    <item>
      <title>Assessment of Parcel Delivery Systems Using Unmanned Aerial Vehicles (Phase II)</title>
      <link>https://rip.trb.org/View/1595112</link>
      <description><![CDATA[The project will evaluate alternative parcel delivery methods that use unmanned aerial vehicles (UAVs). The different methods to be evaluated include UAVs operating independent from ground vehicle delivery, mobile depots where ground vehicles are equipped with UAVs that aid in the delivery process, and data driven approaches where the UAV provides information on the traffic state to improve vehicle route choice and UAV deployment decisions. The delivery systems will be evaluated under different levels of demand and considering different UAV capabilities in terms of range and allowable parcel size. The current objectives of the project include assessment of different parcel delivery systems that exploit drone traffic monitoring and delivery capabilities. Specifically, the project team is developing open loop control algorithms for adaptive drone deployment and truck routing. The resulting strategies are compared against exiting delivery mechanisms that do not incorporate traffic congestion. As an extension for this project, the team will evaluate novel probabilistic modeling procedures as well as techniques for estimating the probability of non-recurrent congestion at incident prone locations.]]></description>
      <pubDate>Mon, 25 Mar 2019 10:14:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/1595112</guid>
    </item>
    <item>
      <title>Dynamic Traffic Assignment: Assessing Its Value as a Planning Support Tool in Arizona</title>
      <link>https://rip.trb.org/View/1516639</link>
      <description><![CDATA[The Arizona Statewide Travel Demand Model (AZTDM) simulates the interaction between people and the roadway system. The model produces travel forecasts used for highway design and transportation planning.

However, the AZTDM currently has a static traffic assignment process, which represents average conditions over a long period, in contrast to the rapidity with which traffic levels can change in the real world.

A recently developed technology, called dynamic traffic assignment (DTA), is designed to represent fluctuating traffic volumes and long trips on complex networks. DTA may enhance ADOT’s understanding of existing and future travel behavior, enabling a more accurate modeling of traffic congestion and freight movement, as well as the testing of alternative solutions.

The objective of the research is to evaluate the feasibility of using DTA statewide and the implications in terms of cost, accuracy, and integration with the existing model.


]]></description>
      <pubDate>Thu, 21 Jun 2018 13:08:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/1516639</guid>
    </item>
    <item>
      <title>Travel Forecasting Resource with TRB and Travel Analysis Metadata</title>
      <link>https://rip.trb.org/View/1512355</link>
      <description><![CDATA[The National Cooperative Highway Research Program and the Transportation Research Board (TRB) Travel Forecasting Resource are developing broad spectrum resources for travel forecasting and analysis. Feedback from lead modeling agencies during peer reviews requested additional context information to efficiently fit the modeling tool to fit agency needs and priorities. This effort will develop metadata and search tools to assist modelers in identifying data and methods that are specific to their application context.  The TF Resource is a travel demand modeling reference written by travel modelers for travel modelers. It is hosted by TRB and overseen by Special Committee ADB45 per the recommendations from Special Report 288. These funds will be made available to TRB under a grant to develop content and manage the reference.]]></description>
      <pubDate>Tue, 15 May 2018 12:00:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/1512355</guid>
    </item>
    <item>
      <title>FAF 4 Network Modeling</title>
      <link>https://rip.trb.org/View/1509905</link>
      <description><![CDATA[This project concerns the development of the FAF commodity flow model.]]></description>
      <pubDate>Thu, 26 Apr 2018 15:03:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/1509905</guid>
    </item>
    <item>
      <title>A Constraint-Based Bicycle Origin-Destination Estimation Procedure</title>
      <link>https://rip.trb.org/View/1422729</link>
      <description><![CDATA[Cycling is an active, green transportation mode that improves environmental sustainability and the livability of urban communities. Promoting cycling also has significant public health benefits. Despite the increasing importance of cycling as a transportation mode, it is often ignored in traditional transportation planning procedures. Origin-destination (O-D) matrix estimation methods have focused on estimating O-D matrices from link traffic counts for motorized vehicles, which are regularly collected by transportation agencies for traffic monitoring purposes. However, traffic monitoring of non-motorized traffic is not as comprehensive as motorized traffic monitoring in the United States. Hence, O-D matrix estimation methods developed for motorized traffic cannot be directly used to estimate bicycle O-D trip matrices. This project proposes a bicycle O-D estimation procedure that is flexible and can be adjusted to different levels of data availability and quality. Bicycle data that are useful for bicycle O-D estimation are first explored. A constraint-based bicycle O-D estimation procedure that utilizes bicycle data from multiple sources is then proposed. A case study is also conducted to demonstrate the proposed methodology. The results demonstrate that in practice, the proposed bicycle O-D estimation procedure is a promising tool.]]></description>
      <pubDate>Fri, 09 Sep 2016 11:28:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/1422729</guid>
    </item>
    <item>
      <title>Develop Multi-Scale Energy and Emission Models for Arterial Traffic Systems</title>
      <link>https://rip.trb.org/View/1363409</link>
      <description><![CDATA[This research is to develop a tool for the assessment of short-term and medium-term effects of network-level, traffic flow improvement projects, including Intelligent Transportation System (ITS) applications, on the transportation network energy consumption and environment. This tool can be utilized to evaluate the energy and environmental impacts of alternative transportation-related projects to their implementation in the field, thus significantly reduce the energy and environmental impacts of transportation projects.]]></description>
      <pubDate>Thu, 30 Jul 2015 01:01:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/1363409</guid>
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