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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>OpenRoad Link: A Public-Private Data Exchange for Safer, Smarter Trucking </title>
      <link>https://rip.trb.org/View/2646948</link>
      <description><![CDATA[Work zones, lane closures, and traffic incidents significantly impact roadway safety and efficiency. When lanes are blocked due to construction, crashes, or other disruptions, roadways no longer function as designed—leading unexpected congestion, increased crash risk, and reduced operational reliability. Many work zones are established to perform critical maintenance on aging infrastructure—essential to improving durability and extending the service life of roadways—but they also introduce temporary risks and delays that must be better managed.  Effects of lane blockages are particularly severe for commercial motor vehicles (CMVs), which require more time and space to slow or reroute and are subject to strict hours-of-service regulations that make delays especially costly. 

This project proposes to develop and evaluate a data exchange framework—OpenRoad Link—to integrate and share real-time lane closure, work zone, and incident data from the Oklahoma Department of Transportation (ODOT), the Oklahoma City and Tulsa Traffic Operations Centers (TOCs), and other key transportation and traffic enforcement partners. To build this framework, the project will first identify and assess the roadway data already collected and shared by these agencies, as well as the types of information currently accessible to the CMV industry through private telematics platforms. Building on national standards such as the Work Zone Data Exchange and SAE J2735 (the standard message set for vehicle-to-everything communications), the project will extend the data scope to include lane-blocking crashes, maintenance activities, and other short-term or unplanned restrictions not currently emphasized in existing feeds. Through collaboration with ODOT, city TOCs, and trucking industry partners—including a pilot with a major trucking company such as ABF—the project will demonstrate the delivery of curated, high-value information directly to in-cab devices or fleet management systems.  

Key tasks will include identifying and cataloging roadway and incident data currently collected by the Oklahoma Department of Transportation (ODOT) and the Traffic Operations Centers (TOCs) of Oklahoma City and Tulsa, as well as evaluating what information is already being shared with the commercial vehicle industry through private telematics platforms. The project will establish partnerships with ODOT, city transportation and public safety agencies, and private industry stakeholders to design and implement a unified, standards-compliant data exchange framework. Following the design phase, the team will develop and deploy the OpenRoad Link data feed, ensuring compliance with existing national standards and verifying data accuracy and reliability. A pilot deployment will be conducted in collaboration with a trucking company using a selected in-cab device to deliver actionable, real-time information directly to CMV drivers.  

Anticipated outcomes include improved safety for CMV drivers, a reduction in secondary crashes, enhanced freight reliability, and a validated proof-of-concept for scalable public-private data exchange. By producing a replicable model for collaboration between state DOTs and private-sector technology providers, the project aims to accelerate national adoption of interoperable safety data systems and promote safer, more efficient freight transportation. ]]></description>
      <pubDate>Tue, 06 Jan 2026 08:59:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646948</guid>
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    <item>
      <title>Evaluating the Economic and Safety Trade-offs of Interchange and Access Drive Separation Distances
</title>
      <link>https://rip.trb.org/View/2627344</link>
      <description><![CDATA[The research project will evaluate whether the Iowa Department of Transportation’s (Iowa DOT) minimum separation standards between interchanges and first access points are overly restrictive and potentially detrimental to development opportunities around those interchanges. To achieve this, the project will utilize deep learning techniques to analyze high-resolution aerial photographs to identify interchanges on state-owned roadways, their first driveway access points, and the specific aspects of development status, such as the presence of commercial or residential buildings, vacant land, or agricultural use of the surrounding land. Crash data from the Iowa dataset will be examined to assess safety outcomes about these separation distances. A critical part of the analysis will involve evaluating the economic potential of these lands and estimating the impact of separation standards on land utilization and potential economic growth. 
In addition to state-owned interchanges, the study will identify non-interchange intersections with roadways with similar AADT levels, the number of lanes, if a median is present, and other relevant geometric features to access management. The closest access point will be determined for these intersections, mirroring the approach taken with the interchanges. The crash history for these locations will be retrieved to compare the safety performance of interchanges and non-interchange intersections directly.
This analysis, focusing on interchange and access point separation distances, will help isolate the effect of these separation standards on safety and development, controlling for traffic volume and other features. By examining interchange and non-interchange sites under similar conditions, the research will determine if the minimum separation distances at interchanges are justified or could be adjusted to better balance safety with economic development, potentially informing future policy decisions. The research will also determine the amount of developable land that could be available should the standards be relaxed.
]]></description>
      <pubDate>Wed, 19 Nov 2025 14:36:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2627344</guid>
    </item>
    <item>
      <title>A data-driven framework for traffic incident duration prediction</title>
      <link>https://rip.trb.org/View/2343620</link>
      <description><![CDATA[Traffic incidents pose significant challenges to the efficient flow of transportation systems, causing congestion, delays, and potential safety hazards. This research aims to utilize several data sources, including probe vehicle data, to predict traffic recovery time and analyze the impact of each traffic duration component on traffic recovery time in the State of Maryland. The results derived from the traffic recovery time prediction models can be a valuable tool for decision-makers in planning alternative routes, adjusting signal timings, or providing real-time traffic information to drivers.]]></description>
      <pubDate>Thu, 22 Feb 2024 15:46:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2343620</guid>
    </item>
    <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>Improved Incident Response through Coordinated, Interoperable Communications</title>
      <link>https://rip.trb.org/View/2114950</link>
      <description><![CDATA[The objectives of this study are to: (1) carry out an operational needs assessment and a performance evaluation of the state’s TIM; (2) perform a functional analysis of the Mutualink system; and (3) carry out a benefit cost analysis of integrating Mutualink into the state’s TMC.
]]></description>
      <pubDate>Thu, 09 Feb 2023 11:18:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2114950</guid>
    </item>
    <item>
      <title>Advancing TIM Through Non-traditional Partners</title>
      <link>https://rip.trb.org/View/2077923</link>
      <description><![CDATA[This project will develop strategies for advancing TIM through relationships with non-traditional partnership with interests in risk reduction of responders.]]></description>
      <pubDate>Tue, 06 Dec 2022 09:48:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2077923</guid>
    </item>
    <item>
      <title>Annual Traffic Incident Management Capability Self Assessment</title>
      <link>https://rip.trb.org/View/2077922</link>
      <description><![CDATA[This project enables jurisdictions to evaluate capability and provides a framework for improvement.]]></description>
      <pubDate>Tue, 06 Dec 2022 09:48:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2077922</guid>
    </item>
    <item>
      <title>Advancing the Use and Analytics of TIM Data</title>
      <link>https://rip.trb.org/View/2077921</link>
      <description><![CDATA[The EDC4 Using Data to Improve TIM initiative successfully increased the collection of TIM data. This project goes beyond data collection to use and analytics.]]></description>
      <pubDate>Tue, 06 Dec 2022 09:48:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2077921</guid>
    </item>
    <item>
      <title>Corridor-Wide Surveillance Using Unmanned Aircraft Systems Phase III: Exploration of the Implementation of Using Unmanned Aircraft Systems for Freeway Incident Detection and Management: Part A</title>
      <link>https://rip.trb.org/View/2004402</link>
      <description><![CDATA[In the last two phases of the project, the collaborative UPRM and USF research team (the research team hereafter) designed traffic data collection experiments on freeways with unmanned aerial systems (UAV with RGB and thermal cameras). The parameters of experiments included the height and speed of the drones, camera angles, congestion and non-congested traffic conditions, etc. The research team developed a learning-based object detection algorithm and evaluated the performance of the algorithm for RGB and thermal videos with different parameter settings and identified the settings with consistent high performance. In addition, the research team developed automated incident detection algorithms by identifying abnormal traffic characteristics. In Phase III of this project, the research team will focus on the validation of the algorithms developed in the previous phases and implementation matters of Phase II. Two main tasks of the research include (1) validating the object detection algorithm and automated incident detection algorithm developed in previous phases; (2) exploring the integration of UAS with the traffic management center. For the first task, researchers from UPRM will focus on incident detection with CCTV video. Traffic data during the incident will be collected with drones and shared with researchers from USF. Researchers from USF will focus on applying the incident detection algorithm for the data collected during the incident. Results from both analyses will be compared and insights from the comparison will be drawn. The same procedure will be applied to analyzing traffic data from the Tampa area. For the second task, researchers from UPRM and USF will work closely with Puerto Rico DTPW and Metric Engineering, and Florida DOT District 7. By understanding the barriers and challenges of implementing emerging technologies in automatic incident detection, the research team will work on implementation recommendations. (See also Phase I NICR Project 4-3: Corridor-Wide Surveillance Using Unmanned Aircraft Systems, Phase II Part A NICR Project 4-4.1 Corridor-Wide Surveillance Using Unmanned Aircraft Systems Phase II: Freeway Incident Detection using Unmanned Aircraft Systems Part A, and Phase II Part B NICR Project 4-4.1 Corridor-Wide Surveillance Using Unmanned Aircraft Systems Phase II: Freeway Incident Detection using Unmanned Aircraft Systems Part B).]]></description>
      <pubDate>Sun, 07 Aug 2022 09:50:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2004402</guid>
    </item>
    <item>
      <title>Provision of Select Computer-Aided Dispatch Data to Traffic Management Centers for Enhanced Incident Detection and Tracking</title>
      <link>https://rip.trb.org/View/1939691</link>
      <description><![CDATA[TxDOT’s Traffic Management Centers (TMCs) are responsible for monitoring freeways within their respective metropolitan areas for crashes, stalls, and other incident impacting traffic flow, contacting the appropriate responding agencies (police/fire/emergency medical services/tow), and tracking incident progress. TMC staff predominantly use Closed Circuit television (CCTV) camera feeds to find incidents, as well as volume/speed detectors on the TxDOT Intelligent Transportation Systems (ITS) map, and the traffic layer on Google Maps. While these methods help to some degree, they are not always effective or efficient. CCTV tours (which show 5-10 second feeds of a freeway segment) can miss an incident if the camera is pointing in a different direction. TMC staff focusing on one freeway may miss an event on another freeway. Google traffic indicators only show the level of traffic but not incidents that caused the traffic. Often, those involved in incidents immediately contact 9-1-1 for assistance. The research team will develop a system that collects essential incident management information from 9-1-1 systems and transmits said information to regional TMCs to speed up the identification and response to an incident, and collect needed incident management data to better assess incident management programs in the region.]]></description>
      <pubDate>Thu, 07 Apr 2022 11:24:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/1939691</guid>
    </item>
    <item>
      <title>Assessment of driver route decision-making during a range of incident-induced traffic flow disruptions</title>
      <link>https://rip.trb.org/View/1889097</link>
      <description><![CDATA[Drivers typically plan and carry out travel to most effectively utilize their time. A key component of travel planning is to select routes, times, and modes that minimize both travel duration and delay. However, such plans are based on prior experience under routine travel conditions. When infrequent, yet inevitable, incidents occur that cause congestion and delay, many drivers make decisions to increase the efficiency of their trip. Although one of the most common driver strategies is to divert travel to alternative routes, relatively little is known about the motivation of this decision-making nor the characteristics that most acutely effect driver choice. The goal of this research is to address the need for a better understanding of route-diversion behavior by assessing driver decision making under a range of incident, traffic, and guidance conditions. The
result of this research is expected to advance both research and practice.]]></description>
      <pubDate>Fri, 29 Oct 2021 15:36:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/1889097</guid>
    </item>
    <item>
      <title>Effect of freeway incidents and diversionary behavior on
transportation network resiliency</title>
      <link>https://rip.trb.org/View/1696172</link>
      <description><![CDATA[An important role of transportation networks at scale of megaregion during an emergency evacuation is providing fast accessibility and safe mobility from the evacuation zone to safety. Disruptions on road networks throughout the evacuation potentially can put the safety of the people at risk. The resiliency of the network chiefly is functional recovery from performance reductions and costly delays which is a key element in at-risk situations. Although the initial level of functionality, type, and severity of an incident make an impact on the recovery time, in absence of external aid road users follow a diversionary behavior during the incident to alleviate the performance loss. There are different features that affect this behavior such as the location of the incident, the capacity, number of alternative routes, etc. In an effort to illustrate this diversionary behavior, this research will investigate the characteristics associated with road network resiliency under variation of incident features on megaregions. As a result, by considering the impact of diversionary behavior on the network the research team is able to evaluate the contrast in functional recovery in order to clarify the resiliency assessment specifically in the event of disasters.]]></description>
      <pubDate>Thu, 02 Apr 2020 16:57:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/1696172</guid>
    </item>
    <item>
      <title>Application of Big Data Approaches for Traffic Incident Management (TIM)</title>
      <link>https://rip.trb.org/View/1628590</link>
      <description><![CDATA[Big data is evolving and maturing rapidly, and much attention has been focused on the opportunities that big data may provide state departments of transportation (DOTs) in managing their transportation networks. Using big data could help state and local transportation officials achieve system reliability and safety goals, among others. However, challenges for DOTs include how to use the data and in what situations, such as how and when to access data, acquire staff resources to prepare and maintain data, or integrate data into existing or new tools for analysis. Research was needed to document issues and demonstrate the feasibility and value of big data approaches for state DOTs and other agencies to enhance operations and TIM programs.

Under NCHRP Project 03-138, “Application of Big Data Approaches for Traffic Incident Management (TIM),” AEM Corporation was asked to (1) demonstrate the feasibility and practical value of big data approaches to improve TIM and (2) provide guidelines, including techniques and tools, to address the findings and recommendations of NCHRP Research Report 904. The research team developed four use cases that exhibit applications of big data in TIM, as well as guidelines on how transportation officials might anticipate and navigate known challenges.]]></description>
      <pubDate>Fri, 07 Jun 2019 15:44:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/1628590</guid>
    </item>
    <item>
      <title>Quantifying Uncertainty and Distributed Adaptive Control for Unanticipated Traffic Patterns as a Result of Major Natural and Man-made Disruptions</title>
      <link>https://rip.trb.org/View/1485712</link>
      <description><![CDATA[The project aims to develop control tools (algorithms) for traffic management in congested urban networks in a way that (i) takes advantage of data made available to intersection controllers by the vehicles and (ii) adapts to dramatic changes in traffic conditions (namely, incidents and no-notice emergency management events). The first part of the research quantifies uncertainty in traffic conditions due to data and data processing limitations, the second part of the research utilizes this understanding of uncertainty to develop scalable and robust control techniques.]]></description>
      <pubDate>Tue, 17 Oct 2017 12:43:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/1485712</guid>
    </item>
    <item>
      <title>Phase II - Using Capacity Adjustments for Connected and Autonomous Based on Various Market Penetration Rates for Long Range Planning and Scenario Analysis</title>
      <link>https://rip.trb.org/View/1475617</link>
      <description><![CDATA[The capability of Connected and Autonomous Vehicles (CAV) is progressing at a faster rate with particular focus on technological performance, and its wide-ranging potential impacts on safety, operation, and regulatory issues. For example, CAVs could travel closer together at smaller headway which enables higher capacity through existing infrastructure. Existing CAV research is often limited in terms of scope, scale, approach, or underlying assumptions, and has not sufficiently addressed questions about the large-scale impacts of CAV on highway capacity, which are required by decision-makers to inform policies.
Moreover, the Highway Capacity Manual (HCM) is at risk of becoming outdated or limited in relevance/usefulness as the CAV technologies become more prevalent on the market. The current HCM has multiple limitations regarding CAV analysis including:
(1) Capacity-related HCM methods cannot be used to evaluate projects or facilities that would utilize CAV technology, as the impacts of CAV strategies are not accounted for.
(2) Lack of existing analysis guidance regarding the suitability of the HCM for analyses involving CAV strategies.
(3) Limited consideration of market penetration and the effects they will have on the realized outcomes associated with CAV technologies on various facilities.
These limitations drive a critical need to develop HCM capacity adjustments (CCAV) to be prepared for future CAV operations under varying levels of volume and market penetration.
Phase II Objectives: The HCM CAV pooled fund study is on track to meet all original study goals within the year 1 and 2 scope, and has successfully developed CAV adjustment factors for the HCM. This extension scope builds on the existing work with a primary focus on technology transfer, dissemination of results, and training. The specific tasks proposed for the Year 3 extension are as follows: Task A: Scenario Development, Task B: Training Materials, and Task C: Outreach and Webinars.]]></description>
      <pubDate>Sun, 23 Jul 2017 14:54:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/1475617</guid>
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