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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
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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>
    </item>
    <item>
      <title>Enabling Next-Generation Safe, Efficient and Reliable Traffic Signal Management via Advanced Sensing and Foundation Models</title>
      <link>https://rip.trb.org/View/2691670</link>
      <description><![CDATA[Urban traffic signal management systems often rely on outdated techniques and strategies that fail to adapt to dynamic roadway conditions, leading to safety concerns, congestion, and access issues for road users. In addition, current signal optimization approaches rarely consider energy efficiency as the main objective. This research proposes a next-generation safe, efficient and reliable traffic signal control framework powered by advanced roadside sensing and foundation models, specifically Visual Language Models (VLMs) and Multi-Modal Large Language Models (MMLLMs). By integrating high-definition cameras, LiDAR, and real-time data analytics, the system will accurately detect multimodal traffic flows, predict future traffic conditions, and optimize signal phase and timings to enhance mobility while minimizing energy consumption. The framework will be validated through a case study at the Riverside Smart Intersection testbed, leveraging real-world data and co-simulation environments.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:42:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691670</guid>
    </item>
    <item>
      <title>Reducing empty miles of shared mobility on highway corridors</title>
      <link>https://rip.trb.org/View/2691661</link>
      <description><![CDATA[Smartphone-app-based technology has provided business opportunities to various demand-responsive urban transportation services, including e-hailing taxis, ride-pooling, and microtransit. These shared mobility services exhibit great potential for enhancing transportation services in rural communities. A common side effect, however, is a substantial portion of empty vehicle miles traveled (VMT) on highway corridors, which induces further congestion to highway traffic in peak hours. A quantitative analysis tool is necessary for planning agencies and policymakers to assess the impact of shared mobility on highway traffic. The researcher's recent work investigating ride-pooling systems serving uniformly distributed demands in a single community shows that their efficiency is highly sensitive to online matching schemes. This impact is expected to be even more significant in spatially imbalanced demand patterns, such as those between suburban/rural communities. This project will develop a traffic assignment model to allocate vehicular trips to corridor networks linking suburban and rural communities, which will assist policymakers in (1) understanding the relations between the spatial distribution of inter-community travel demands and excessive VMT; (2) identifying the most vulnerable corridors affected by shared mobility services; and (3) evaluating the potentials of various regulatory policies and public surcharges in reducing empty vehicle mileage. Ultimately, the analysis tool will enable planning agencies to explore practical measures to improve the accessibility of suburban and rural communities with shared mobility services.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:16:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691661</guid>
    </item>
    <item>
      <title>Influence of Traffic Noise and Light on Wildlife Movement Near Highways</title>
      <link>https://rip.trb.org/View/2691660</link>
      <description><![CDATA[Traffic results in noise and light propagated from roadways into surrounding landscapes, including human and natural communities. A previous National Center for Sustainable Transportation (NCST) project has supported the development of multi-scale (local to state) models of traffic illumination and noise extending from highways. These traffic-effect areas may be habitat for a wide range of species, including wildlife attempting to cross highways via existing culverts and bridges, or purpose-built wildlife crossings. With previous NCST research using camera traps, researchers found that coyote and mule deer have varying behavioral responses to traffic noise at wildlife crossings. This project extends both the noise and light modeling and the previous investigations of wildlife crossings to include more species (mountain lion, mule deer, and Peninsular bighorn sheep) and many more highways and regions. The research team will statistically model the effect of traffic noise and light from traffic on occurrence of these 3 species and movements of global positioning system (GPS)-collared individuals as they approach highways. This information is critical in informing and making more effective the massive investments that local, state, and federal governments are making in wildlife crossings and fencing to improve driver and wildlife safety. In other words, knowing the traffic effects on wildlife as they approach highways will allow mitigation of those effects using design and construction of the crossings, such as barriers and berms. Because wildlife crossings are the primary investments transportation agencies make to reduce wildlife impacts and increase driver safety, understanding wildlife ability to get to these crossings and use them is critical to the effectiveness of the investment and the role the investment plats in environmental sustainability.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:12:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691660</guid>
    </item>
    <item>
      <title>Comparing Traffic Safety Risks Across Transportation Modes</title>
      <link>https://rip.trb.org/View/2685708</link>
      <description><![CDATA[This research project compares traffic safety risks across different transportation modes. Very few studies have quantified crash risk across modes, especially recently in the United States. First, this study will calculate and compare mode-specific crash rates, using data from Utah and potentially elsewhere. Second, these mode-specific crash rates will be adjusted for key traffic safety risk factors, about the locations where and times when crashes take place, and the people who are involved. Finally, this work will propose a research-informed framework and analytical methods for improved comparisons of traffic safety risks across transportation modes, especially considering the contexts, factors, and mechanisms of crash causation. Overall, this research will generate useful traffic safety performance measures to help with prioritizing transportation safety investments and communicating safety risks to the public.]]></description>
      <pubDate>Sun, 29 Mar 2026 18:54:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2685708</guid>
    </item>
    <item>
      <title>Leveraging Emerging Data for Traffic Safety Analyses</title>
      <link>https://rip.trb.org/View/2685697</link>
      <description><![CDATA[This project will leverage emerging large-scale vehicle trajectory data to help identify high-risk roadway segments. The analysis will focus on leveraging surrogate safety indicators extracted directly from vehicle movement patterns. Key indicators such as abrupt speed changes, harsh acceleration or braking, and irregular motion signatures are used as proxies for operational risk. These indicators will be aggregated at the roadway-segment level and compared with the “traditional” crash data and crash outcomes on the KABCO scale. This is to help proactively and more quickly identify roadway locations that pose a higher potential safety risk based on data from driving behaviors.
Project efforts will address technical workflows for handling high-volume trajectory data, including data preparation, event extraction, spatial segmentation, and identification of behavior-based patterns. This work aims to develop a structured approach for highlighting rural segments with surrogate safety risk indicators of elevated operational risk based on trajectory-derived metrics.
As a case study, the efforts will use a dataset obtained a data aggregator for portions of the state of Nevada. The dataset contains over a billion trajectories recorded from millions of unique trips between June 2024 and June 2025. Each record includes spatial, temporal, and motion-related attributes, offering a high-resolution view of driving behavior on roadways. These data can be obtained within days or weeks compared to traditional crash data which typically takes many months to obtain.
The outputs of this project include the illustration of the exploratory use of large-scale vehicle trajectory data to identify high-risk roadway segments, and the development of a structured approach to highlight road segments with surrogate safety risk indicators of elevated operational risk based on trajectory-derived metric.
This work will highlight how high-resolution telematics data can support early identification of potential safety concerns on road networks. These insights can assist transportation and law enforcement agencies to identify parts of the road network for design and operations review considerations, prioritize law enforcement priorities and practices, allocate resources efficiently, and strengthen data-driven safety management practices. This could also help effect changes in policies, programs, procedures, and practices to improve traffic safety outcomes such as reduce fatalities and injuries.
]]></description>
      <pubDate>Sun, 29 Mar 2026 18:53:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2685697</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>Enhancing Freight Safety and Efficiency for California’s Logging Industry: A Data-Driven Approach</title>
      <link>https://rip.trb.org/View/2684215</link>
      <description><![CDATA[The logging industry plays a vital role in the U.S. economy, particularly in California’s northern regions, where timber production supports local supply chains. However, the safe and efficient movement of logging trucks is increasingly challenged by road curvature, steep grades, aging infrastructure, and seasonal fluctuations in freight demand. These factors create high-risk conditions, exacerbated by overlapping tourist activity and inadequate roadway data. This research aims to develop a comprehensive, data-driven framework to identify and mitigate freight safety risks for logging trucks. By leveraging open-source tools, data collection efforts, 3D road profiling, and advanced statistical and machine-learning models, this study will identify and predict high-risk freight routes for California’s logging industry.

Problem: The terrain, road curvature, seasonal harvest demands, and aging infrastructure pose significant challenges to both roadway safety and freight efficiency. Certain high-risk locations - such as roads with sharp curves, steep grades, or deteriorating bridges - may be especially hazardous for large vehicles like logging trucks. Furthermore, the seasonal nature of logging, combined with heightened tourism activity, creates fluctuating traffic patterns and additional stress on key corridors.

Objectives/Goals: This proposal seeks to develop a comprehensive, data-driven framework to identify, analyze, and recommend improvements for critical freight corridors used by logging trucks.]]></description>
      <pubDate>Wed, 25 Mar 2026 17:03:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684215</guid>
    </item>
    <item>
      <title>Enhancing Heavy Vehicle Crash Prevention in North Dakota through Machine Learning and Weather Data Integration</title>
      <link>https://rip.trb.org/View/2683255</link>
      <description><![CDATA[Heavy vehicle crashes continue to be a persistent safety concern across the Midwest, with several states reporting disproportionately high rates of incidents involving large trucks. According to the National Safety Council, in 2023, North Dakota recorded 18% of its fatal crashes involving large trucks, placing it among the highest in the nation. Neighboring states, such as Nebraska (16%) and Iowa, also face elevated risks. Illinois reported 7,509 truck accidents in 2022, ranking among the top five states nationwide. In North Dakota, the risks are especially pronounced during the winter months. In 2023, 64% of heavy vehicle crashes occurred between October and March, with 81% of these crashes taking place in rural areas. These figures highlight how weather conditions and geography amplify the risk associated with large-truck travel in the region. Further, crashes in rural areas in challenging weather conditions poses immense issues for first responders and their ability to provide timely medical care to crash victims.   

Traditional safety strategies have struggled to account for the dynamic, real-time factors that contribute to crash risk. Static approaches often fall short when adverse weather, road conditions, and traffic volume interact in unpredictable ways. This gap highlights the urgent need for predictive, data-driven solutions.  

This proposal aims to investigate the application of machine learning (ML) models, combined with weather and crash data, to predict high-risk scenarios before accidents occur, to support planning for safety and emergency response needs. By leveraging predictive analytics, North Dakota could enhance resource allocation, deploy preventive interventions, and reduce the frequency and severity of heavy vehicle crashes. The high incidence of winter crashes and the limitations of conventional methods make North Dakota an ideal proving ground for an innovative, ML-driven approach to roadway safety.  

The study will utilize historical crash records for heavy vehicles in North Dakota, including crash type, severity, date, and time, combined with corresponding weather data such as temperature, precipitation, snowfall, and visibility. Feature engineering will create representations of temporal and weather conditions relevant to crash severity. Machine learning models, including Random Forest, XGBoost, and Neural Networks, will be trained to predict crash severity. To ensure interpretability, SHAP (SHapley Additive exPlanations) will be applied to quantify the contribution of each feature to individual predictions and overall model behavior. This analysis will reveal which weather or temporal factors most strongly influence severe crashes, both globally across the dataset and locally for specific incidents. High-risk periods and conditions identified by the model, along with explanations provided via SHAP, will be visualized both temporally and geographically, offering actionable insights to support targeted preventive measures and inform DOT decision-making.  ]]></description>
      <pubDate>Tue, 24 Mar 2026 14:09:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/2683255</guid>
    </item>
    <item>
      <title>Improving the Quality and Useability of Planned and Active Work Zone Data</title>
      <link>https://rip.trb.org/View/2683244</link>
      <description><![CDATA[Work zone data may be used to support efforts ranging from internal operational and safety analysis to public communications and connected vehicle navigation. Ensuring the quality and consistency of this data is vital to its usability. The Virginia Department of Transportation (VDOT)’s current systems,  VaTraffic and the Lane Closure Advisory Management System (LCAMS), require double entry of data, and the other data sets they feed into all display the data differently. This project will review data quality standards and create guidance that can be applied in LaneAware to ensure quality moving forward. In November 2024, the Federal Highway Administration (FHWA) updated its Work Zone Safety and Mobility Final Rule (23CFR630 Subpart J), which in part requires state departments of transportation (DOTs) to identify mobility and work-zone-exposure performance metrics that will be used to track performance and the statewide level and for specific major projects.  Best practices used by other DOTs will be gathered and recommended for adoption. Tools and scripts for data cleaning and analysis will improve the application of these data to operational and safety analysis, which is currently hampered by issues such as identifying data from planned work zones from active ones. By consulting with a wide range of stakeholders, these recommendations will consider the wide-ranging needs of both data producers and consumers in this system.     ]]></description>
      <pubDate>Tue, 24 Mar 2026 10:53:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2683244</guid>
    </item>
    <item>
      <title>Mid-America Freight Coalition (MAFC) Phase 5</title>
      <link>https://rip.trb.org/View/2683017</link>
      <description><![CDATA[The Mid-America Freight Coalition (MAFC) pooled fund began in 2006 to support collaboration, innovation, and development in freight planning, freight policy, and operations across the 10-state Mid America Association of State Transportation Officials (MAASTO) region (Illinois, Indiana, Iowa, Kansas, Kentucky, Michigan, Minnesota, Missouri, Ohio, and Wisconsin). The Coalition’s operations are founded and guided by the Memorandum of Understanding (MOU) signed by the Board of Directors of MAASTO and the University of Wisconsin (UW)-Madison.

The MAFC’s major emphasis areas support advances in multimodal freight planning practices, freight operations and technology, and freight policy, all in a collaborative framework. Importantly, the emphasis areas are determined by the participating state professionals. The work is completed in service to both the states and the region, as well as towards advancing national freight planning priorities throughout the MAASTO region. The projects and activities of the MAFC support critical linkages between freight movement and services, as well as economic and community development. The freight coordination of the MAASTO region can provide guidance and identify best practices at a national level relating to multistate coordination of freight activities and in support of goals within the Infrastructure Investment and Jobs Act (IIJA).

This solicitation is for the fifth iteration of the MAFC pooled fund. Previous iterations were TPF-5 (156), TPF-5 (293), TPF-5 (396), and TPF-5 (509).]]></description>
      <pubDate>Thu, 19 Mar 2026 09:48:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2683017</guid>
    </item>
    <item>
      <title>Leading Permitting Practices that Harmonize Enforcement of Divisible Load Permits across Jurisdictions</title>
      <link>https://rip.trb.org/View/2681239</link>
      <description><![CDATA[A February 2023 workshop with industry and state representatives identified challenges related to inconsistent interpretation and enforcement of divisible load requirements. Although 23 CFR 658.5 provides a definition of “divisible load,” both industry and state agencies report variation in how the definition is applied across states and, in some cases, within the same state.

Some states provide written guidance for operators and enforcement personnel, while others offer limited or no formal documentation. These differences can create operational challenges, including route adjustments, additional travel time, increased fuel use, parking constraints, and scheduling complications. Operators may also receive citations in one jurisdiction for loads that are permitted in another.

This scan will examine how divisible load requirements are interpreted and enforced across states, counties, metropolitan areas, municipalities, and other transportation agencies. It will document differences in practice, explore factors contributing to those differences, and incorporate input from industry partners regarding cross-jurisdictional challenges and potential solutions.]]></description>
      <pubDate>Tue, 17 Mar 2026 14:58:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2681239</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>SMARTER Center CAV Testbed Digital Twin</title>
      <link>https://rip.trb.org/View/2676080</link>
      <description><![CDATA[This project advances transportation safety and mobility by developing a high-fidelity digital twin of the SMARTER Center’s Connected and Automated Vehicle (CAV) testbed at Morgan State University. The proposed platform synchronizes key infrastructure states, sensor observations, and traffic dynamics with a virtual environment in near real time, enabling safety and mobility interventions to be evaluated in a controlled, repeatable setting without exposing road users to risk. Currently, CAV safety validation faces a well-documented gap: physical testing is costly, slow, and may introduce safety concerns, while purely virtual simulations often lack real-world calibration. This project addresses that gap by integrating live testbed data—including LiDAR, CCTV cameras, roadside units, and V2X messages—with simulation-based scenario testing using CARLA, sensor fusion methods, and validated data pipelines. The system targets low latency and high spatial accuracy suitable for behavioral and safety analysis under representative traffic conditions. The platform demonstrates multi-modal capability through two application scenarios: (1) pedestrian crossing conflict analysis at signalized intersections under varying speeds, visibility, and occlusion conditions, and (2) transit signal priority evaluation using U.S. DOT bus trajectory data to assess potential operational impacts, including delay reduction. Validation is conducted using RTK-GPS probe vehicles and annotated video data, with trajectory similarity and time-to-collision metrics quantitatively assessed. Key outcomes include a functional digital twin system, evaluation of safety-critical scenarios with agreement between digital and physical testbed behavior on key performance indicators, a 5-hour annotated dataset with DCAT-US metadata, and three software modules released via GitHub. The extensible platform architecture supports future applications such as emergency vehicle preemption, freight operations, and micromobility, with documented APIs enabling replication across diverse testbeds and agencies.]]></description>
      <pubDate>Wed, 11 Mar 2026 15:33:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676080</guid>
    </item>
    <item>
      <title>Road Network Restoration after Major Disruptions</title>
      <link>https://rip.trb.org/View/2447123</link>
      <description><![CDATA[This project develops practical optimization methods for selecting, sequencing, and scheduling restoration actions for disrupted road networks based on incomplete and gradually improving information. Road networks may be severely damaged by events such as hurricanes and earthquakes, and prompt restoration is often necessary for the resumption of emergency services, other essential services, and normal activities.

The proposed methods employ artificial intelligence heuristics such as genetic algorithms and particle swarm algorithms to optimize the schedules of restoration tasks. A hybrid optimization approach combines fast traffic assignment with microscopic simulation to refine solutions. The methods are designed to start with incomplete, uncertain information and adapt dynamically as additional data becomes available from weather forecasts, work crews, and the public. The project also develops methods for pre-planning purposes, including preparing effective restoration plans based on estimated probabilities of disruptions and their consequences.

The research team will collaborate with the Maryland State Highway Administration and other agencies to ensure the practical applicability of the methods. Technology transfer activities include journal papers, conference presentations, software with a user manual, a final technical report, and workshops for interested transportation organizations.]]></description>
      <pubDate>Wed, 11 Mar 2026 13:21:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2447123</guid>
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