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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>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>Advanced Transportation Optimization and Modeling (ATOM)</title>
      <link>https://rip.trb.org/View/2676009</link>
      <description><![CDATA[The U.S. transportation system is experiencing increasing complexity driven by evolving infrastructure, land-use patterns, travel demand, demographic shifts, and rapid advances in vehicle and mobility technologies. Emerging behaviors such as telecommuting, ridesharing, and micromobility, along with changing attitudes toward public transit and vehicle ownership, are reshaping how people and goods move across regions. To ensure that transportation investments remain efficient, resilient, and cost-effective, transportation agencies require advanced, data-driven tools to anticipate and evaluate the system-level impacts of these changes.  

This project develops an advanced transportation modeling and optimization pipeline in Austin, Texas, to evaluate the impacts of alternative strategies and technologies through scenario-based analysis. The system will be built around the Behavior, Energy, Autonomy, and Mobility (BEAM) model. BEAM is an open-source, agent-based regional transportation model that enables realistic simulation of travel behavior, mode choice, fuel consumption, and system performance, and associated community-level impacts under different “what-if” scenarios.  

By leveraging BEAM’s scalable, modular architecture, the project will address key limitations of conventional four-step and activity-based transportation models, providing a robust framework for testing strategies such as emerging technologies, infrastructure enhancements, and new mobility services before deployment. The pipeline will be developed and extended to assess additional impacts (via coupling to additional models) and therefore to serve as a decision-support tool for engineers, planners, and service providers, allowing them to evaluate performance outcomes and trade-offs across multiple metrics relevant to both economic productivity and community outcomes. Model calibration and validation of the Austin BEAM Core pipelines will utilize highly resolved local datasets on traffic flows, speeds, and network performance. These data will enable precise representation of real-world operating conditions in the Austin region and ensure the model’s reliability for planning and investment analysis.  

Scenario development will be coordinated with implementation partners regional stakeholders identified through a stakeholder mapping exercise. These scenarios will reflect practical policy and technology options under active consideration in Texas, ensuring alignment with state and regional priorities. The resulting pipeline will be structured for extensibility, allowing future integration with additional datasets and modeling components for use in other applications. Project outcomes will be shared broadly through technical reports, workshops, and data portals to facilitate adoption by other agencies, research institutions, and industry partners.  

Ultimately, this project supports goals of enhancing efficiency, safety, and reliability, while strengthening economic competitiveness and enabling informed, data-driven investment decisions. By combining open-source modeling innovation with public–private collaboration, the project will provide a replicable framework for modern, performance-based transportation system management.  

Moreover, the pipeline embraces and deploys advanced and transformative research: using an open-source, agent-based framework (BEAM) exceeds conventional planning methods. The stakeholder-co-development model (with public and industry partners) ensures that this research is not only theoretically innovative but also rooted in real-world deployment potential. This initiative empowers decision-makers to implement policies that enhance safety, the economy, and with various co-benefits to communities.  ]]></description>
      <pubDate>Tue, 03 Mar 2026 16:42:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676009</guid>
    </item>
    <item>
      <title>Express Lanes Benefitting Freight Mobility: Can Express Lane Systems and its Benefits to General-purpose Lanes Improve Freight Mobility?</title>
      <link>https://rip.trb.org/View/2553996</link>
      <description><![CDATA[The objectives of the research project include the following: (1) assess the impact of Express Lanes on freight movement efficiency; (2) evaluate changed in travel time and improve safety; (3) analyze the impact on freight delivery reliability and efficiency, examine the economic impacts of Express Lanes associated with freight movements; (4) quantify cost changes for truck drivers and truck companies; (5) assess potential economic benefits to the region, evaluate the environmental impacts of Express Lanes associated with freight movements; (6) estimate reductions in greenhouse gas emissions; and (7) estimate savings in fuel consumption.]]></description>
      <pubDate>Fri, 16 May 2025 07:20:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553996</guid>
    </item>
    <item>
      <title>Strategies for Potential Emissions Reductions for Commercial Maritime Vessels at Ports</title>
      <link>https://rip.trb.org/View/2347370</link>
      <description><![CDATA[This project performs an analysis of employment opportunities in the inland waterways sector. The
analysis includes a comparative analysis of employment factors across the modes of rail trucking and
inland waterways, calculates the benefits the nation enjoys as a direct result of employment by the
inland waterway industry, and develops an educational toolkit about job opportunities in the inland
waterway sector.]]></description>
      <pubDate>Thu, 29 Feb 2024 14:33:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2347370</guid>
    </item>
    <item>
      <title>Evaluating Alternative Fuels in Snowplow/Maintenance Vehicles and Identifying Barriers to Adoption</title>
      <link>https://rip.trb.org/View/2344949</link>
      <description><![CDATA[Five existing snow plow trucks from DSM (N) will be outfitted with Optimus Technology Vector B100 conversion kits.  Three new plow trucks will be outfitted with the same technology. The 8th truck will be assigned to the Ames Garage and will receive fuel from the City of Ames B100 tank from a similar project and tank.  The trucks will burn pure biodiesel or B100 for the majority of the time they are running which decreases the amount of petroleum used.

DSM (N) will use the trucks for the duration of the pilot project which is 2 years starting in November of 2019.  B100 will be consumed and fuel transaction data will be collected. The list below indicates the type of data and information that will be available to evaluate: GPH fuel consumed – fuel transaction data will be available, truck performance – engine data will be available, driver experience – interview drivers with scripted questions; vector system malfunctions – document anything that failed; tank / dispenser malfunctions– document anything that failed; calculated reductions in pollutants – could be calculated; # gallons of fossil fuel displaced – could be calculated; acres of soybeans processed into B100 – could be calculated; carbon emissions reduced – could be calculated; accuracy of fuel transaction data – timely delivery etc. – data will be available for review; DPF regeneration cycles reduced – can be measured against sister trucks; and DPF regeneration system maintenance costs reduced - can be measured against sister trucks.]]></description>
      <pubDate>Tue, 27 Feb 2024 17:27:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2344949</guid>
    </item>
    <item>
      <title>Ecological Driving System for Connected Automated Vehicles: A New Model Predictive Control Framework</title>
      <link>https://rip.trb.org/View/2343737</link>
      <description><![CDATA[The growing importance of sustainable and eco-friendly transportation has spurred the need for effective solutions to optimize the operational efficiency of connected automated vehicles (CAVs) in such complex environments. The trajectory planning problem (TTP) for CAVs is complicated by non-linear constraints, especially when dealing with the Eco-trajectory Planning Problem (EPP), characterized by its nonlinear, high-order, and non convex objective function. To tackle this challenge, the research team proposes a novel heuristic explicit predictive model control (heMPC) framework and aim to develop an innovative multi-objective ecological driving system that optimizes CAVs' trajectories along signalized arterial roads. Three key objectives are targeted: minimizing travel time, reducing fuel consumption, and improving traffic safety. The proposed framework comprises two interlinked modules: an offline module and an online module. In the offline module, the team will construct an optimal eco-trajectory batch by optimizing a series of simplified EPPs, considering diverse system initial states and terminal states. This process can be
likened to a lookup table in the general eMPC framework, with the intention of precomputing all necessary optimizations and calculations to eliminate online optimization in the subsequent stage. In the online module, the team will employ both static and dynamic trajectory planning algorithms to efficiently handle trajectory planning for CAVs. After proving the effectiveness of the proposed algorithms in the simulation environment, the team will test the entire system with field studies, leveraging the in-house CAV in the lab.]]></description>
      <pubDate>Thu, 22 Feb 2024 15:57:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2343737</guid>
    </item>
    <item>
      <title>Modernizing Fuel Tax Revenue Forecasting</title>
      <link>https://rip.trb.org/View/2307248</link>
      <description><![CDATA[State departments of transportation (DOTs) are facing funding challenges because state and federal fuel tax revenues are changing and becoming harder to accurately forecast. One of the factors responsible for changes is improvements in vehicle fuel economy. For example, there are increases to the National Highway Traffic Safety Administration (NHTSA)’s Corporate Average Fuel Economy (CAFE) standards, fleet economy changes, electric and alternative fuel vehicles, and changes in vehicle miles traveled (VMT). Some state legislation has inadvertently decreased fuel revenues as a side effect. For instance, more than a dozen states have adopted regulations through legislation or other government actions to rapidly scale down emissions of light-duty passenger cars, pickup trucks, and sport utility vehicles and require an increased number of zero-emission vehicles to meet air quality and climate change emissions goals.

Six separate excise taxes are imposed to finance the federal Highway Trust Fund (HTF) program. Three of these taxes are imposed on highway motor fuels (gasoline, diesel fuel and kerosene, and alternative fuels) and generate the majority of the revenues dedicated to the HTF. The FHWA’s Highway Revenue Forecasting Model (HRFM) provides projections for a 20-year time horizon for HTF and new revenue sources. The model uses VMT and fuel economy projections, as well as changes in composition of vehicles over the forecasting period. The fuel efficiency projection incorporates anticipated penetration of fuel-efficient vehicles, including electric vehicles (EVs). The model provides revenue projections, contribution of the 21 different vehicle classes to revenues, and costs (tax burdens) to households by income group and other demographics. Outputs from this model are primarily used for conducting highway cost allocation (HCA) studies (https://www.fhwa.dot.gov/policy/hcas/final/).

Research is needed to help state DOTs develop improved models to accurately forecast motor fuel transportation revenue in the near and long term for operational and planning needs. Further, these forecasts are necessary to quantify and understand potential shortfalls in revenue that need to be replaced by alternative sources of revenue.

The objective of this research is to develop a method and model(s) to help states forecast motor fuel transportation revenues in light of increased fuel efficiency and alternative fuels.]]></description>
      <pubDate>Mon, 11 Dec 2023 21:33:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2307248</guid>
    </item>
    <item>
      <title>Research and Support Implementation of Highway Construction and Materials that Reduce Environmental Impacts and Emissions, and Maximize Material Efficiency and Recycling.</title>
      <link>https://rip.trb.org/View/2093179</link>
      <description><![CDATA[This project aims to reduce costs for highway users and agencies by conducting research on tools, evaluation techniques, and best practices to increase fuel efficiency and reduce fuel use in construction, operations, and maintenance.]]></description>
      <pubDate>Tue, 03 Jan 2023 13:53:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2093179</guid>
    </item>
    <item>
      <title>Electric Fast Foil Ferry: Re-imagining the Mosquito Fleet for Accelerating Passenger Ferry Innovation</title>
      <link>https://rip.trb.org/View/2062441</link>
      <description><![CDATA[Kitsap Transit, across the Puget Sound from Seattle, will receive funding to design a model for a high-speed passenger ferry powered by battery-electric, low-emission technology. The state-of-the-art hydrofoil design will rely on lightweight carbon fiber construction and batteries to speed up travel between urban centers and suburban and rural communities and significantly reduce fuel use compared to conventional fast ferries.]]></description>
      <pubDate>Tue, 15 Nov 2022 16:17:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2062441</guid>
    </item>
    <item>
      <title>Optimizing CAV platoon movements in a signalized road network for travel and energy efficiency  </title>
      <link>https://rip.trb.org/View/1716531</link>
      <description><![CDATA[The research team's previous work to optimize CAV platoon movements through an isolated intersection has shown that considerable savings (up to 40%) in both travel time and fuel use can be achieved. In this research, the team attempts to solve the CAV platoon optimization problem for a network of signalized intersections, which presents several challenges that their previous work did not address. In this research, the team proposes a cooperative platoon-trajectory-optimization framework that consists of four components: optimal route planning to identify the CAVs travel paths based on the real-time traffic state, a lane-changing strategy to form platoons for different travel directions, boundary control for generating the initial and final states of optimization, and a platoon-trajectory-optimization method to minimize the fuel consumption and travel time. Simulation studies will be carried out to evaluate the system performance of the proposed framework in reducing fuel consumption and travel delay.]]></description>
      <pubDate>Thu, 25 Jun 2020 13:53:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/1716531</guid>
    </item>
    <item>
      <title>Eco-Driving Study on Trucks along a Signalized Arterial with Significant Freight Traffic </title>
      <link>https://rip.trb.org/View/1531716</link>
      <description><![CDATA[Eco-driving is a concept of reducing fuel consumption and greenhouse gas emissions through changing driving behaviors. Along a signalized arterial, frequent driving mode changes in decelerating, stopping/idling, and accelerating contribute to elevated levels of fuel consumption and emissions. Because of the extra distance and time needed for deceleration and acceleration of heavy trucks, and because of the significantly alleviated fuel consumption rate and emission rates during the acceleration processes of these trucks, the driving decisions of the trucks not only have major impacts on the mobility, but also have significant environmental and economic impacts. 

The objective of this research is to develop eco-driving strategies by optimizing the speed profiles of the trucks along a signalized arterial to minimize fuel consumption and emission while maintaining good mobility of the corridor. Two significant differences exist between this proposed study and prior studies. Firstly, the study has a focus on trucks which have distinct characteristics in acceleration, deceleration, and speed. These characters will have significant impacts on traffic operations as well as fuel consumption and emissions for an arterial with significant freight traffic. Secondly, this study will specifically consider market penetration and compliance rate of eco-driving from truck drivers and other drivers. The market penetration and compliance rates will significantly affect the results of optimization and also call for different modeling approaches, from a deterministic one to one that is probabilistic and considers uncertainties. 

The optimization is based on current traffic conditions including current truck speed modes and the queuing conditions at the downstream intersection, and also based on the current signal states including SPaT data. A queue detection algorithm considering connected vehicle market penetration rate will be considered. Evaluations will be made for different scenarios of traffic and signal conditions with both a macroscopic procedure and a microscopic approach that integrates EPA’s MOVES models with a microscopic traffic simulation program (VISSIM). The optimization solutions based on mobility considering trucks as well as cars will also be compared with those that minimize fuel consumption and emissions, and integrated optimization strategies will be explored.

This research will also explore the additional benefits in mobility, fuel consumption, and air quality by considering the interactions of signal control and truck eco-driving decisions under a connected operation environment. Trucks will be assumed to be connected (or have a high market penetration rate) and communicate with the infrastructure (signal control), and the benefits from the combination of eco-driving decisions in real time and adaptive signal control will be further evaluated, and strategies and methodology for real-time implementation will be explored.]]></description>
      <pubDate>Fri, 10 Aug 2018 08:07:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/1531716</guid>
    </item>
    <item>
      <title>HRDO-FY16-15 Eco-Drive Experiment on Rolling Terrain to Optimize Fuel Consumption</title>
      <link>https://rip.trb.org/View/1509093</link>
      <description><![CDATA[This project models and estimates fuel consumption of an eco-drive concept for mixed traffic under different percentages of penetration including an experiment using HIL with a hybrid engine to control gear and speeds simultaneously.]]></description>
      <pubDate>Thu, 19 Apr 2018 15:32:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/1509093</guid>
    </item>
    <item>
      <title>Investigation of the Relationship between Fuel Consumption, Dynamic Load, and Roughness of Pavement Preservation Treatments</title>
      <link>https://rip.trb.org/View/1504877</link>
      <description><![CDATA[Understanding the costs of highway construction, highway maintenance and vehicle operation is essential to sound planning and management of highway investments, especially under increasing infrastructure demands and declining budget resources. Pavement preservation has recently gained wide acceptance amongst the highway agencies because of its cost-effectiveness and ability to enhance pavement performance and reduce environmental impacts. However, these benefits are not well quantified. Reduction in vehicle fuel consumption is one of the main benefits considered in technical and economic evaluations of road improvements considering its significance. Analysis of the effects of pavement rolling resistance on vehicle fuel economy and emissions needs to consider the total system of the pavement, road geometry, vehicles and their operation, and climate.  The pavement characteristics influencing rolling resistance and vehicle fuel economy are roughness, texture, and structural response. This project investigates the increase in vehicle energy consumption due to pavement structural response caused by the increase in roughness induced dynamic loading.]]></description>
      <pubDate>Sun, 11 Mar 2018 11:20:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/1504877</guid>
    </item>
    <item>
      <title>Leveraging Connected Vehicles to Enhance Traffic Responsive Traffic Signal Control</title>
      <link>https://rip.trb.org/View/1401178</link>
      <description><![CDATA[Actuated traffic signal controllers rely on sensors to detect vehicles so that green time can be allocated on a second-by-second basis. Traffic signals that are part of a closed loop system running coordination plans can also utilize detector information to select different pre-programmed plans based on the current traffic state. These Traffic Responsive Plan Selection (TRPS) algorithms currently rely on point detectors that only measure volume and occupancy. With the anticipated implementation of Connected Vehicles, sensors can be installed at signalized intersections to collect the trajectory of these vehicles, which will allow queue lengths to be estimated. Additionally, many radar-based sensors that are currently on the market are capable of tracking vehicles approaching an intersection, which can also be used to estimate queue lengths. This queue length information can be fused with the volume and occupancy data from point detectors to gain an even better understanding of the state of the signal system. This enhanced information could likely allow even better selection of pre-programmed coordination plans. When trajectory-based vehicle information becomes widespread and reliable, it is entirely possible that this information will be used by the controller logic to directly make decisions. In the meantime, this research will investigate whether this information can be leveraged to further enhance TRPS control, which is widely available in most traffic signal controllers. An existing Central system-in-the-loop simulation of a traffic signal system in Morgantown, WV will be utilized to implement and test algorithms for estimating queue lengths from vehicle trajectory data in real-time, estimating the state of the system in real-time, and communicating information back to the controllers to change the timing plans, when appropriate. The advanced TRPS will be compared to basic coordination timing plans and basic TRPS control across various volume scenarios to estimate improvements in delay, emissions, and fuel consumption.]]></description>
      <pubDate>Tue, 15 Mar 2016 18:37:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/1401178</guid>
    </item>
    <item>
      <title>In-Place Recycle Paving Methods: Energy Use Analysis
</title>
      <link>https://rip.trb.org/View/1370730</link>
      <description><![CDATA[This project will develop methodology to assess the total or differential energy use involved in functionally comparable recycle-in-place paving techniques, taking into consideration all possible factors including equipment operation, fuel consumption, transportation, materials production and handling, reusability of reclaimed aggregates, expected longevity/durability, and other factors. This information will also be gathered for conventional paving so that any differential between the two approaches can be quantified. Ultimately, this data will be made available as a tool for use by transportation organizations toward the specification of highway rehabilitation projects. Making paving choices with the goal of sustainability is a complicated matter, and involves the consideration of many factors beyond the scope of this project. This effort seeks to isolate and focus on the energy use of a group of similar-outcome paving techniques so as to have another data resource among many to use in the decision-making process. By knowing the total energy use for a given paving technique as well as other environmental impact factors associated with each technique, agencies will be better positioned to make informed, objective decisions as to what method is optimal for a given budget, performance level, and environmental outcome.
]]></description>
      <pubDate>Tue, 29 Sep 2015 16:20:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/1370730</guid>
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