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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>AI-assisted Condition Assessment of Roads</title>
      <link>https://rip.trb.org/View/2752288</link>
      <description><![CDATA[The objective of this project is to develop an AI-assisted road monitoring system that enables low-cost, autonomous, and frequent condition-based assessments using a network of mobile sensing units. The system will use computer vision and machine learning to detect and quantify pavement defects, replacing traditional schedule-based inspections with continuous, data-driven monitoring. The proposed system provides transportation agencies with an affordable, scalable, and intelligent tool for real-time pavement monitoring. By using low-cost sensors on existing vehicles and automated data interpretation, it delivers accurate condition insights, reduces inspection costs, and supports timely maintenance decisions.]]></description>
      <pubDate>Thu, 13 Aug 2026 15:31:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2752288</guid>
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    <item>
      <title>Using Vehicle Probe Data to Evaluate Speed Limits on Texas Highways</title>
      <link>https://rip.trb.org/View/2553168</link>
      <description><![CDATA[The research team will explore if there are refinements that could be made to the recently developed protocol for using third party data in generating suggested speed limits. The suggested speed limit probe data (SSL-Probe) protocol was developed to allow Texas Department of Transportation (TxDOT) districts to be more pro-active and responsive with their speed zone program. The protocol also provides a much safer method of collecting speed data, especially on high-speed and controlled access highways.]]></description>
      <pubDate>Wed, 14 May 2025 10:06:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553168</guid>
    </item>
    <item>
      <title>Integration of a Real-time Traffic State Estimation and a Decentralized Game-Theoretic Traffic Signal Controller</title>
      <link>https://rip.trb.org/View/2447006</link>
      <description><![CDATA[This project proposes to enhance a decentralized traffic signal controller based on a Nash Bargaining game-theoretic framework, integrating a Kalman filtering (KF) algorithm for real-time traffic state estimation. By combining KF with the traffic signal controller, the project aims to optimize signal phasing sequences at intersections based on turning movements and traffic density, thereby reducing queue lengths and delays. The approach involves traffic data collection through loop detectors and probe vehicle data, and it will address saturation flow rates for shared lanes. This integration intends to achieve efficient system performance across varying probe vehicle market penetration levels, ultimately providing a robust solution for improved traffic flow and reduced environmental impact at intersections.]]></description>
      <pubDate>Wed, 30 Oct 2024 14:41:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2447006</guid>
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    <item>
      <title>Utilizing daily traffic as a sensor network for infrastructure health monitoring </title>
      <link>https://rip.trb.org/View/2410418</link>
      <description><![CDATA[Mobile sensing is a novel paradigm that offers numerous advantages over conventional stationary sensor networks for real time bridge monitoring. Mobile sensors have low setup costs, collect spatio-temporal information efficiently, and require no dedicated sensors to any particular structure. Most importantly, they can capture comprehensive spatial information using few sensors. The advantages of mobile sensing combined with the ubiquity of smartphones with internet of things (IoT) connectivity have motivated researchers to consider smartphones carried within vehicles as large-scale sensor networks that can contribute to the health assessment of structures. A practical implementation of mobile sensors has several challenges. Most notably, the signals collected within a vehicle's cabin is contaminated by the vehicle suspension dynamics and the road profile; therefore, the efficient extraction of bridge vibration from signals collected within the vehicle is of great importance. The majority of available approaches for addressing this are typically system specific and restricted by assumptions of linearity. This limits the scope of application since vehicles mostly act nonlinearly depending on their manufacturing specifications. In addition, the variety of vehicle systems and road conditions complicates the exploration for a unified method for this task. This project proposes deep learning frameworks with domain adaptability that enable vehicle signal decontamination in a more reliable and practical manner. This framework will transform vehicles into robust and high-quality vibration sensors for infrastructure monitoring. Furthermore, this will render smartphone-based vehicle sensing data a valuable source of information that will enable crowdsourcing and facilitate infrastructure condition assessment in real time at an unprecedented scale, rate and resolution. ]]></description>
      <pubDate>Wed, 31 Jul 2024 16:57:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2410418</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>Expansion: Enhanced Traffic Signal Performance Measures</title>
      <link>https://rip.trb.org/View/2189216</link>
      <description><![CDATA[The Pooled Fund Project TPF-5(377) is led by Indiana and includes participation from FHWA, California, Connecticut, Georgia, Minnesota, NorthCarolina, Ohio, Pennsylvania, Texas, Utah, and Wisconsin. The project developed methodologies and tools for using
high resolution vehicle trajectory data to compute enhanced traffic signal performance measures. Significant outcomes from the study are listed at the end of this document in the reference section. The Indiana led pooled fund traffic signal research projects have a strong history of implementation. The first study, TPF-5(259), was recognized by EDC 4 and virtually all controllers now provide high resolution data logging. There is a strong commercial base of advanced traffic signal performance measure providers.The technical reports from TPF-5(259) listed below are widely distributed and cited. Performance Measures for Traffic Signal Systems: An Outcome-Oriented Approach. http://dx.doi.org/10.5703/1288284315333; Integrating Traffic Signal Performance Measures into Agency Business Processes. http://dx.doi.org/10.5703/1288284316063.
 Similarly, TPF-5(377) is now stimulating a second generation of commercial implementation of trajectory based traffic signal performance measures.The current TPF-5(377) project end date is June 20, 2023. OBJECTIVES: During the April 2022 TPF-5(377) Pooled Fund Study Panel Meeting in Columbus, OH, participating states expressed interest in developing a new study led by a neutral state/academic partner in the following areas: 1) Broadening performance measures to additional modes that are impacted by traffic signal systems, particularly transit and pedestrians; 2) Identifying use cases for enhanced probe data beyond  the current trajectory and hard braking/hard acceleration data; 3) Integrated Analysis of High-resolution Controller Data and Trajectory Probe Data. These initiatives would complement and expand the past work the multi-state team has done in the area of enhanced traffic signal performance measures using connected vehicle data.
]]></description>
      <pubDate>Wed, 31 May 2023 19:38:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2189216</guid>
    </item>
    <item>
      <title>Leveraging Probe-Based Data to Enhance Long-Term Planning Models</title>
      <link>https://rip.trb.org/View/2056329</link>
      <description><![CDATA[Long-range transportation planning (LRTP) and travel demand models (TDMs) play an important role in the planning process, which assists transportation agencies with prioritizing future transportation investments. Improved LRTP and TDMs can bring direct benefits to transportation planning in the state. Effective transportation planning and investment decision making depends on timely, comprehensive, and accurate data. However, traditional data collection methods only provide a “snapshot” of the travel information, which limits the performance of conventional LRTP and TDMs. In this regard, while these sources are still used, transportation planners at the state, metropolitan, and local levels are beginning to incorporate third-party traffic data into their planning processes. Planners also start to look at the opportunities afforded through third-party data and provide guidance on how to take advantage of that data to expand and improve planning practices. This project aims to utilize probe-based data to improve the LRTP process and TDMs used by Texas Department of Transportation (TxDOT), metropolitan planning organizations (MPOs) and other planning agencies in the state. The research teams will study how probe-based and location-based data may be leveraged to facilitate the validation and calibration of existing planning models, enhance existing modeling tools, and incorporate advanced modeling techniques.
]]></description>
      <pubDate>Fri, 04 Nov 2022 10:11:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2056329</guid>
    </item>
    <item>
      <title>Develop Improved Queue Warning System Combining Multiple Data Sources</title>
      <link>https://rip.trb.org/View/2055963</link>
      <description><![CDATA[Existing queue warning (QW) systems predominantly use infrastructure-based sensors to detect the formation of queues and use dynamic message signs (DMS) to warn drivers. These QW systems, may be inadequate where the required number or density of sensors are not available. Research has shown that crowd sourced probe and connected vehicle (CV) trajectory data used in combination with sensor data can significantly improve the accuracy and latency of queue detection. Furthermore, because of gaps between fixed location of DMSs a subset of drivers can encounter a queue without seeing any warning. Sharing queue information through third-party providers and providing vehicle-specific queue warning in CVs can ensure provision of queue warning to a broader audience in a timely manner. The research team will: (1) Develop detailed design of an enhanced queue detection and warning system that combine point-, probe-, and vehicle trajectory data. (2) Test and fine-tune the queue detection algorithm design using computer simulation. (3) Conduct a proof of concept, prototype deployment and field evaluation of the new QW system design on a freeway segment where vehicle queues frequently form. (4) Document the systems engineering and algorithm for the QW system as a potential enhancement to the Lonestar™ advanced traffic management system software application.]]></description>
      <pubDate>Thu, 03 Nov 2022 14:33:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2055963</guid>
    </item>
    <item>
      <title>Exploring Cost-effective Computer Vision Solutions for Smart Transportation Systems</title>
      <link>https://rip.trb.org/View/1953288</link>
      <description><![CDATA[The project is focused on developing a deep learning based data acquisition and analytics tool using vision - based sensors (i.e., cameras) to understand cities with machine eyes . The team will assess the maturity of various smart city applications using computer vision and object detection (e.g., pedestrian detection, work zone identification , curb lane usage, connected and automated vehicles [CAVs] ) as well as the needs of the local agencies. The goal is to demonstrate the c ost - effectiveness of the computer vision technology to generate new stream of mobility data and provide support for planning and operational strategies , utilizing both existing transportation infrastructure and emerging probe and CAVs . More specifically, t his project aims to establish an inventory of available traffic camera systems in the U.S. and deploy two computer vision smart city applications based on stakeholder feedback that are customized for New York City (NYC) . The team will also establish a formalized pipeline for running the computer vision algorithm enhanced for NYC conditions and prototype the applications for real - world implementation.]]></description>
      <pubDate>Wed, 18 May 2022 13:26:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/1953288</guid>
    </item>
    <item>
      <title>Best Practices for Data Fusion of Probe and Point Detector Data</title>
      <link>https://rip.trb.org/View/1854176</link>
      <description><![CDATA[The National Cooperative Highway Research Program (NCHRP) Research Report 1127 presents a guide for facilitating data fusion and improving data reporting to support traffic management at state departments of transportation (DOTs). This guide provides an overview of pertinent transportation data for fusion, considerations before initiating data fusion, and a proposed framework for fusion of point and probe data. Real-world use-cases demonstrating application of the framework are also included. The guide should be of interest to a broad cross-section of staff at state DOTs seeking to understand the relevance and importance of data fusion, implement it within their agency, and collaborate with other staff in the process.  Data fusion is the process of integrating multiple data sources to produce more consistent, accurate, and comprehensive information than that provided by any individual data source. In a transportation context, state DOTs are seeking to (1) define the types and characteristics of data for entry into data fusion engines; and (2) identify the challenges, issues, and proven or potential practices for performing data fusion to measure or forecast travel time, speed, reliability, and other aspects of operational performance on roadway networks. Traffic datasets of interest include point sensors; Bluetooth; data from GPS devices embedded in smartphones, personal navigation devices, taxis, and fleets; third-party travel time data; and emerging connected vehicle (CV) data sets. Better knowledge of the network state could help improve traffic management and planning decisions to address impacts of recurrent and non-recurrent congestion. Improved network state estimates could also enhance safety outcomes by identifying locations with high crash rates and anomalous traffic flow conditions. Under NCHRP Project 08-158, “Best Practices for Data Fusion of Probe and Point Detector Data,” MLP LLC was asked to develop (1) a process to identify specific objectives for data fusion and specific data sources; and (2) a guide for state DOTs to facilitate data fusion, improve data reporting, and ultimately improve traffic management. The guide is divided into sections to serve multiple audiences. Chapters 1 through 4 are for all transportation professionals regardless of background or position. Chapter 5, the data fusion framework, has been divided into sections for specific audiences: (1) for executives, a high-level overview of the framework step, explaining its relevance and importance to their agency; (2) for systems implementers, deeper details that someone charged with implementing the data fusion algorithms and technologies would likely need to know; and (3) for traffic systems management and operations (TSMO) professionals, sufficient knowledge to facilitate collaboration with systems implementers. 
In addition to NCHRP Research Report 1127, two deliverables are not included in the published report but are available on the TRB website at trb.org by searching for NCHRP Research Report 11xx. The deliverables are as follows: (1) a plan that identifies mechanisms and channels for communicating and implementing this research; and (2) a PowerPoint presentation introducing NCHRP Research Report 1127.]]></description>
      <pubDate>Tue, 25 May 2021 16:33:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/1854176</guid>
    </item>
    <item>
      <title>SPR-4536: Implementation of Enhanced Probe Data (CANBUS) for Tactical Workzone and Winter Operations Management</title>
      <link>https://rip.trb.org/View/1710248</link>
      <description><![CDATA[Integrating hard braking events into weekly work zone reports will provide Indiana Department of Transportation (INDOT) with assessment of their queue warning systems as well as improved hazard detection. Traction control and anti-lock braking events will be integrated into the winter operations traffic ticker and after action reports during winter months. It is anticipated that first connected vehicle truck data will begin to emerge in 2021 and the first collaboration in identifying wind speeds that warrant warnings on message signs will be conducted.]]></description>
      <pubDate>Thu, 04 Jun 2020 14:57:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/1710248</guid>
    </item>
    <item>
      <title>SPR-4451: Integration of Probe Data Tools into TMC Operations</title>
      <link>https://rip.trb.org/View/1693712</link>
      <description><![CDATA[Probe data tools developed at Purdue are used variety of Purdue/INDOT/ISP users for analyzing work zones, severe crashes, winter operations, moving maintenance operations, and overall system mobility. This project will consolidate the tools into a common suite of tools that uses consistent user and database interfaces as well as develop training material for a variety of users.]]></description>
      <pubDate>Tue, 17 Mar 2020 15:28:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/1693712</guid>
    </item>
    <item>
      <title>SPR-4306: Back of Queue Warning and Critical Information Delivery to Motorists</title>
      <link>https://rip.trb.org/View/1530598</link>
      <description><![CDATA[Based on the existing INDOT real-time queue-monitoring systems, this proposal focuses on developing a back-of-queue warning and critical information delivery to motorists approaching congestion queues on Indiana highways. The proposed research aims to investigate feasible solutions for deploying end-of-queue alarms through different smartphone Apps, tune parameters and optimize HMI for better user experience; develop a prototype back-of-queue alerting system based on probe vehicle data, and evaluate the benefits via driving simulator study and limited on-road driving test.
]]></description>
      <pubDate>Mon, 06 Aug 2018 16:34:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/1530598</guid>
    </item>
    <item>
      <title>Develop Data Storage and Access Platform for MTA Bus Time Data</title>
      <link>https://rip.trb.org/View/1517520</link>
      <description><![CDATA[New York City Department of Transportation (NYCDOT) along with many other DOTs in the region and around the country have been using probe vehicle data for monitoring time-dependent traffic conditions and conducting before and after studies of various transportation projects. Specifically, NYCDOT has been using probe vehicle data from yellow taxis and other vehicles equipped with global positioning system (GPS) and TRNSMIT system. In this project NYCDOT wants to automate and enhance their use of MTA bus data that they are already acquiring under a protocol developed between the two agencies.

The Center for Urban Science and Progress (CUSP) has developed a relationship with MTA under a recent arrangement between the City and NYU-Poly. The MTA is one of CUSP partnering institutions, and over the last year2 the two institutions have worked towards developing a close partnership. During these interactions, it has become clear that NYCT has already developed a “system to collect and store historic Bus Time data, while processing and validating the data against scheduled service”. Instead of redeveloping a similar tool for NYCDOT use, CUSP/NYU-Poly research team proposes to harness the effort already spent by NYCT to deliver the same product to NYCDOT. This effort will include the development of access and security protocols with NYCT and NYCDOT to provide NYCDOT with seamless access to MTA Bus Time data.]]></description>
      <pubDate>Sat, 30 Jun 2018 14:16:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/1517520</guid>
    </item>
    <item>
      <title>Develop Data Storage and Access</title>
      <link>https://rip.trb.org/View/1378052</link>
      <description><![CDATA[New York City Department of Transportation (NYCDOT) along with many other Departments of Transportation (DOTs) in the region and around the country have been using probe vehicle data for monitoring time-dependent traffic conditions and conducting before and after studies of various transportation projects. Specifically, NYCDOT has been using probe vehicle data from yellow taxis and other vehicles equipped with global positioning system (GPS) and TRNSMIT system. In this project NYCDOT wants to automate and enhance their use of Metropolitan Transit Authority (MTA) bus data that they are already acquiring under a protocol developed between the two agencies.

The Center for Urban Science and Progress (CUSP) has developed a relationship with MTA under a recent arrangement between the City and NYU-Poly. The MTA is one of CUSP partnering institutions, and over the last year2 the two institutions have worked towards developing a close partnership. During these interactions, it has become clear that NYCT has already developed a “system to collect and store historic Bus Time data, while processing and validating the data against scheduled service”. Instead of redeveloping a similar tool for NYCDOT use, CUSP/NYU-Poly research team proposes to harness the effort already spent by New York City Transit (NYCT) to deliver the same product to NYCDOT. This effort will include the development of access and security protocols with NYCT and NYCDOT to provide NYCDOT with seamless access to MTA Bus Time data.]]></description>
      <pubDate>Wed, 23 Dec 2015 11:44:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/1378052</guid>
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