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    <title>Research in Progress (RIP)</title>
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    <language>en-us</language>
    <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>
    <image>
      <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>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>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>Developing a Data Fusion Tool for Improved Traffic Crash Exposure
Analysis and Modeling</title>
      <link>https://rip.trb.org/View/2663603</link>
      <description><![CDATA[Accurate measurement of exposure is critical for understanding and preventing traffic crashes, as crash frequency is directly related to how much road users are exposed to risk. However, current exposure estimates rely on data sources with complementary but individually insufficient characteristics. Traditional traffic counts and Annual Average Daily Traffic (AADT) offer high accuracy but limited spatial and temporal coverage, while emerging Location-Based Services (LBS) data provide high-resolution mobility patterns but are often biased and less reliable. This fundamental mismatch between accuracy and coverage prevents agencies from developing the complete and reliable exposure estimates needed for effective safety analysis and planning.
This project develops a data fusion tool that integrates traffic counts and AADT, LBS data, and socio-demographically representative survey data from the National Household Travel Survey (NHTS) into a unified measure of exposure. Unlike previous efforts that focused on a single travel mode or low temporal resolution, the proposed framework generates exposure estimates for motor vehicles, pedestrians, bicyclists, and scooters at fine spatial scales (intersection and mid-block) and temporal scales (daily and monthly). The tool is evaluated in Washington, D.C., using three alternative fusion paradigms: Bayesian fusion through hierarchical or state-space modeling, Dempster–Shafer theory for explicit uncertainty representation and accommodation of LBS coverage gaps, and model-based fusion employing structured error modeling with NHTS socio-demographics to correct LBS data bias.
The fusion methods are compared through crash prediction models estimated with fused exposure measures against models using individual data sources, evaluated via pseudo-R², AIC, BIC, and out-of-sample prediction error, with a target improvement of at least 10% in predictive performance. Fused exposure patterns are further validated against Washington, D.C.’s High Injury Network and independent ground-truth count data where available. The final tool is delivered as an open-source Python package with documentation and secure coding practices. Agency outreach, including engagement with D.C. stakeholders managing the High Injury Network, informs tool refinement and supports preparation for future pilot deployment. This research supports USDOT’s Safety priority by generating more accurate and complete multimodal exposure measures that enable better identification of high-risk locations, improved crash prediction, and targeted safety interventions
]]></description>
      <pubDate>Tue, 03 Feb 2026 15:31:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663603</guid>
    </item>
    <item>
      <title>Connected Vehicle Data</title>
      <link>https://rip.trb.org/View/2640696</link>
      <description><![CDATA[The Compass IoT company is performing a pilot project with the Missouri Department of Transportation (MoDOT) to provide data, both historical and over a four-month period, for the research team to build a proof of concept for the member states in the Original Equipment Manufacturers (OEM) Pooled Fund. This will allow the research team to show the member states the benefits that can be realized with this information. The data will be focused on work zone information, near miss data, and winter weather events.]]></description>
      <pubDate>Tue, 16 Dec 2025 09:56:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2640696</guid>
    </item>
    <item>
      <title>Shared Space Safety: A Study of Campus Travel and Mixed Mode Interactions</title>
      <link>https://rip.trb.org/View/2625580</link>
      <description><![CDATA[A study of campus travel and mixed-mode interactions will develop a data-driven baseline of safety conditions on Hilltop Way at San Diego State University—a steep roadway where pedestrians, skateboarders, scooter users, cyclists, and vehicles frequently converge, creating conflicts during class transitions. Video data collected from both ground-level cameras and aerial drone footage will capture user behaviors, travel speeds, yielding patterns, and near-miss events. Analytical techniques such as post-encroachment time (PET) and computer-vision–based variable extraction will be applied to quantify the frequency and severity of potential conflicts. The resulting dataset and safety assessment framework will enable rigorous before–after evaluations of future countermeasures introduced by the university, allowing their effectiveness to be measured in terms of changes in near-crash indicators and interaction dynamics. The project’s outputs—including annotated datasets, analysis tools, and methodological guidelines—will provide a transferable model for studying multimodal safety on shared streets, advancing United States Department of Transportation priorities in safety, innovation, and data-driven decision-making.]]></description>
      <pubDate>Tue, 18 Nov 2025 15:50:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625580</guid>
    </item>
    <item>
      <title>Identifying Harsh Driving Behaviors and Contributing Factors Using Telematics Data: A Case Study in Oakland and Fresno, California</title>
      <link>https://rip.trb.org/View/2625583</link>
      <description><![CDATA[Despite extensive safety countermeasures, vulnerable road users continue to face significant risks on urban roadways, resulting in a substantial loss of life. Safety frameworks like Vision Zero and the Safe System Approach call for proactive solutions that address these dangers before severe crashes occur. This proactive approach can be powered by surrogate safety measures, which use data on near-misses and risky behaviors to identify hazards. Harsh driving events—such as harsh braking or acceleration—serve as excellent indicators of elevated crash risk. These behaviors are influenced by a combination of factors, including roadway design, traffic flow, and the complex, unpredictable interactions between vehicles and other road users in dense urban environments. This study leverages high-resolution telematics data from the Cities of Oakland and Fresno to investigate the differential impacts of harsh driving behaviors on road safety.  We will construct and compare crash hotspots (e.g., high injury network) and harsh driving behavior hotspots to examine which types of harsh driving behaviors most strongly align with crashes involving vulnerable road users and latent crash risks. Additionally, by using statistical methods and explainable artificial intelligence techniques, we will analyze roadway characteristics (e.g., intersections, lane curvature, or slope), as well as traffic flow and surrounding conditions, to determine whether specific features are associated with increased prevalence of harsh driving events that, in turn, elevate crash risk.  By integrating the spatiotemporal patterns of crashes and telematics-based behavioral measures, along with infrastructure characteristics, this study aims to better understand how risky driving patterns contribute to vulnerable road user safety outcomes. The findings will provide actionable insights for prioritizing enforcement such as speed camera deployment, designing infrastructure countermeasures, and developing data-driven, proactive strategies to support safe transportation.]]></description>
      <pubDate>Tue, 18 Nov 2025 15:33:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625583</guid>
    </item>
    <item>
      <title>Leveraging Connected Vehicle Data for Enhanced Highway Safety Modeling and Decision-Making</title>
      <link>https://rip.trb.org/View/2596530</link>
      <description><![CDATA[
The primary objective of this project is to assess the utility and reliability of connected vehicle data (CVD) in safety modeling and analysis, either as a supplement to or a substitute for traditional crash data where appropriate. This assessment will be conducted across a range of roadway designs and traffic control settings, dependent on the geospatial availability of CVD.]]></description>
      <pubDate>Tue, 09 Sep 2025 08:37:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2596530</guid>
    </item>
    <item>
      <title>SPR-5020: Identifying Locations with Abnormally High Wrong Way Driving or Interstate U-Turns</title>
      <link>https://rip.trb.org/View/2577103</link>
      <description><![CDATA[Wrong way driving on Interstate entrance ramps and illegal U-Turns are emerging as safety concerns. These types of crashes are relatively infrequent, highly dependent on crash report narrative, and difficult to track at scale for systematically identifying locations that are candidates for mitigation measures. Connected vehicle data provides an important data source that scales well for developing procedures to identify wrong way driving and illegal Interstate U-Turns.]]></description>
      <pubDate>Thu, 17 Jul 2025 15:59:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2577103</guid>
    </item>
    <item>
      <title>SPR-5022: Development of Diagnostic Analytics Tool for Causal Effect Analysis from 511 Database</title>
      <link>https://rip.trb.org/View/2576661</link>
      <description><![CDATA[By leveraging the results of the platform and tool developed from the SPR-4937 project, we propose to develop and implement a Diagnostic Analytics tool with intelligent web-based user interfaces for highway traffic management and operations based on 511IN traffic data and other related data sources. This tool will be capable of displaying various spatial-temporal traffic patterns and events and enabling users to draw connections between traffic patterns and identify correlations to causes. The tool can be used to develop an analytic or machine learning model for generating and predicting the traffic patterns for future occurrence of the event.]]></description>
      <pubDate>Tue, 15 Jul 2025 15:44:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2576661</guid>
    </item>
    <item>
      <title>Improving Holiday Congestion Forecasting on Interstate Highways</title>
      <link>https://rip.trb.org/View/2571772</link>
      <description><![CDATA[Holiday travel periods present a significant challenge for speed and congestion prediction due to highly variable travel behavior, event-driven surges, and inconsistent congestion patterns. The Virginia Department of Transportation (VDOT) currently relies on historical averages of travel speeds based on INRIX TMC data from the past three years, averaged by day and week of the holiday period, to predict likely congestion levels during holidays. While this method has shown good accuracy for holidays that fall on the same day of the week and month, it is less effective for holidays that occur on specific dates, such as the 4th of July and Christmas Day, where travel patterns shift year to year based on the day of the week of the holiday.
This research aims to develop an advanced framework to improve predictions of travel speeds and the expected levels of congestion during holidays designated by the Commonwealth of Virginia. The project will explore a variety of modeling techniques- including machine learning classifiers, regression models, and rule-based systems to determine the most effective method for this application. The models will leverage 30-minute interval INRIX XD speed data between 7:00 AM and 12:00 AM for holidays, on Virginia’s interstate network for 2022–2024.  Additional input features will include such factors as time of day, date context (e.g., "day before holiday," “day after holiday”, day of week), direction of travel (e.g., inbound, outbound, or balanced conditions), urban/rural classification, and VDOT district. In addition to XD data, the research team will evaluate the incorporation of supplementary data sources such as roadway weather information system (RWIS) or National Oceanic and Atmospheric Administration (NOAA) weather data, incident reports, and historical volume trend overlays to enhance model performance. Non-holiday data will also be analyzed to adjust for annual baseline changes in traffic volume, supporting more robust holiday comparisons.     
]]></description>
      <pubDate>Thu, 03 Jul 2025 09:08:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/2571772</guid>
    </item>
    <item>
      <title>Automatic Signal Retiming Using Vehicular Trajectory Data</title>
      <link>https://rip.trb.org/View/2562295</link>
      <description><![CDATA[Traffic signal optimization is a cost-effective method for reducing congestion and energy consumption in urban areas. However,
due to the high installation and maintenance costs of detection systems, most intersections are controlled by fixed-time traffic
signals that rely on manual data collection and are not regularly optimized. More cost-effective methods for signal optimization
need to be explored, such as the use of vehicle trajectory data that is now available. Research is needed to determine if this
process can provide optimized timings at more frequent and regular intervals, yielding better overall signal performance.]]></description>
      <pubDate>Fri, 06 Jun 2025 15:12:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2562295</guid>
    </item>
    <item>
      <title>Misinformation Detection for Safe Transportation Systems</title>
      <link>https://rip.trb.org/View/2548501</link>
      <description><![CDATA[This project is focused on developing a methodology for the detection of misinformation regarding transportation systems on social
media. A novel dataset combining Twitter and traffic sensor data will be constructed and shared with the broader community. This will be the first large-scale public dataset for research on this topic. A comprehensive characterization of the dataset, correlating
traffic events and tweets will be performed. One key question to be investigated is whether misinformation related to transportation follows similar patterns to misinformation in other domains, such as vaccines and politics. Results from the characterization will be applied to identify potential fake tweets related to traffic events. This analysis will be combined with a manual inspection to generate a set of labeled tweets that will be used for training semi-supervised methods for misinformation detection related to transportation. The proposed approach will be compared against alternatives from the literature. The main findings will be summarized in the project report and in at least one publication. Software, datasets, and metadata produced through the project will be made publicly available.]]></description>
      <pubDate>Tue, 29 Apr 2025 16:51:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2548501</guid>
    </item>
    <item>
      <title>Development of a Scalable, Low-Cost, Environmentally-Friendly Adaptive Traffic Signal Control (SLE-ATSC) System</title>
      <link>https://rip.trb.org/View/2495005</link>
      <description><![CDATA[As an enhanced method for vehicle detection at signalized intersections, it is possible to use vehicle-probe data from smartphones, Global Navigation Satellite System (GNSS) receivers, and other types of mobile devices to complement existing traffic sensing and signal control, resulting in lower energy consumption. Using these additional data, it is now possible to estimate reliable traffic queue lengths at high-density traffic intersections. Given real-time reliable traffic queue lengths, it is possible then to dynamically adjust the signal phase and timing of an intersection, with the goal of minimizing traffic queues, waiting times, and energy use. Using UC Riverside’s Innovation Corridor as a target arterial roadway, the research team will develop a scalable, low-cost, environmentally-friendly adaptive traffic signal control (SLE-ATSC) system based on receiving real-time traffic data from sources such as TomTom and INRIX. The signal control system will be implemented for several of the key intersections along the corridor, using a calibrated state-of-the-art traffic simulation platform. Various metrics will be evaluated, comparing the existing traffic signal phase and timing to the new dynamic signal phase and timing resulting from the adaptive signal control system. Using the calibrated simulation model, traffic system metrics will be estimated. In addition, part of the research team (TSU) will utilize their driving simulators as part of a “Hardware-in- the-Loop” testing system for the proposed adaptive traffic signal control system. The traffic simulation model developed at UCR will interface directly with the TSU driving simulators, allowing the research team to see more realistic driving behavior operating in the simulation platform. This will provide more realistic measures of the overall system performance, with a focus on safety, mobility, and environmental metrics.]]></description>
      <pubDate>Fri, 31 Jan 2025 16:35:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2495005</guid>
    </item>
    <item>
      <title>Analysis of 2018-2024 Network Level Pavement Structural Testing with the TSD</title>
      <link>https://rip.trb.org/View/2495008</link>
      <description><![CDATA[The Traffic Speed Deflectometer (TSD) is a device used to measure the structural response of pavements while traveling up to the prevailing traffic speed. Virginia Department of Transportation (VDOT) has previously collected data on more than 8,000 lane miles of its roadway network using the TSD. This study seeks to combine thickness data from ground penetrating radar (GPR) and VDOT traffic data to calculate the remaining structural life of the network tested between 2018 and 2024 and to upload the data to VDOTs Pavement Management System.  ]]></description>
      <pubDate>Sat, 25 Jan 2025 10:49:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2495008</guid>
    </item>
    <item>
      <title>TRC2503: Feasibility of Vehicle Probe Data for Origin-Destination Estimation</title>
      <link>https://rip.trb.org/View/2353410</link>
      <description><![CDATA[Origin-Destination (O-D) estimation is an important step for travel demand forecasting. Traditional approaches to O-D estimation involve either survey-based trip diaries or traffic counts. Both methods have limitations.  With the emergence of “Big Data” sources in the form of third-party probe data gathered from Global Positioning System (GPS) and cell-phone sources, approaches to O-D estimation have broadened. The objective of this project is to evaluate Probe data accuracy (or bias) by context (location, region, time of day, etc., and measure the feasibility of extracting the trip attributes such as vehicle type, vehicle occupancy, trip purpose, and mode of transport from the data, if available. Due to the unique characteristics of Arkansas' Interstate and National Highway System (NHS) routes, the accuracy of the probe data measured in locations not in Arkansas may not apply to Arkansas. Penetration rates and adjustment factors estimated at the national level may not accurately represent the characteristics for Interstate and NHS long-distance trips seen in Arkansas. In this regard, it is essential to understand the opportunities and limitations of vehicle probe data for O-D estimation in the context of Arkansas. The results of this study will provide a decision-making tool to guide data purchase decisions based on project specific needs.]]></description>
      <pubDate>Wed, 15 Jan 2025 12:32:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2353410</guid>
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