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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>Synthesis of Information Related to Highway Practices. Topic 57-09. Using Crowdsourced Data for Inventory and Transportation Asset Condition</title>
      <link>https://rip.trb.org/View/2630489</link>
      <description><![CDATA[State departments of transportation (DOTs) are increasingly incorporating crowdsourced data into their data collection, management, and analysis activities. Once considered experimental, crowdsourced information has become a mainstream resource, drawing from navigation application user reports, social media, in-house applications, third-party probe services, and active transportation platforms. Federal initiatives have encouraged this shift by providing frameworks for institutionalizing the practice and demonstrating cost savings.

Beyond traffic operations, state DOTs are applying crowdsourced data to maintenance and asset management activities, including identification of potholes, roadway debris, and damaged infrastructure. Crowdsourced data offers the potential to complement traditional sources, expand coverage, improve safety and reliability, and reduce costs.

OBJECTIVE: The objective of this synthesis is to document state DOT practices for using crowdsourced data to support asset inventory and condition assessments and to support maintenance and asset management system operations and planning.]]></description>
      <pubDate>Wed, 26 Nov 2025 15:58:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2630489</guid>
    </item>
    <item>
      <title>Leveraging Crowd-Sourced Data and Artificial Intelligence for Timely Detection of Roadway Anomalies</title>
      <link>https://rip.trb.org/View/2604566</link>
      <description><![CDATA[Roadway debris like carcasses and tire fragments, can pose a hazard to traffic. It can be struck by vehicles and result in a crash or secondary crash. Advance roadway debris detection is critical for traffic operations and safety. Transportation Management Centers (TMCs) can detect debris in urban areas, but a recent study found that Waze, reported three times as many road hazards to similar TMC events on freeways. Waze detected 72% of road hazards first and nearly sixteen (16) minutes earlier. Harnessing crowd-sourced data from navigational apps or connected vehicle dashcams has become a reality with Artificial Intelligence (AI)-enabled processing. This action opens opportunities for enhanced situational awareness and expedite the appropriate responses from the Texas Department of Transportation (TxDOT). The research team will leverage crowd-sourced third-party data and AI-enabled processing to capitalize on this opportunity.]]></description>
      <pubDate>Mon, 29 Sep 2025 16:31:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2604566</guid>
    </item>
    <item>
      <title>Innovations Deserving Exploratory Analysis--The Transit IDEA Program. IDEA 110. Boosting Transit Service Customer Satisfaction with an AI-Enhanced Crowdsourcing Platform</title>
      <link>https://rip.trb.org/View/2572337</link>
      <description><![CDATA[Enhancing customer satisfaction is a critical focus for public transit providers, as it directly
influences ridership. Consequently, public transport operators prioritize the collection and analysis of customer satisfaction data to refine their services and support sustainable urban mobility initiatives. Traditional data collection and analysis approaches, such as surveys and focus group interviews, offer direct perspectives into passenger experiences but are often expensive, time-consuming, labor-intensive, and may not meet the immediate need of capturing customer complaints. These issues underscore the need for innovative, non-traditional data collection strategies to complement the traditional ones. Crowdsourced data emerges as a viable solution, offering riders’ real-time insights into aspects like bus cleanliness, safety, and service reliability. However, despite its benefits, this method faces challenges like providing prompt feedback, managing vast amounts of public input, and efficiently organizing diverse comments. To address these challenges, this IDEA project proposes the development of an artificial intelligence (AI)-enhanced crowdsourcing platform specifically designed to gather public feedback and complaints regarding transit services. This platform will be powered by advanced AI technologies such as Large Language Models (LLM) and Natural Language Processing (NLP). A key feature of this platform will be its ability to automatically categorize customer feedback into specific topics to help with swift identification and resolution of issues. The platform will also address riders’ complaints with pre-defined instructions from operators, suggest actions to follow for operators, and flag items for manual review when necessary. Furthermore, the platform will be able to integrate public contributions with specific transit lines or stations, overlaying this information on transit service maps to enable spatial analysis and visualization for operators.

To build the proposed AI-enhanced crowdsourcing platform, its requirements and features will be defined, based on the challenges identified by transit agencies, e.g., intelligent user interface and automatically processing rider complaints. The building process will consist of user experience (UX)/user interface (UI) design; technology stack design; AI algorithm design; and deployment and launch. Presently available public opinion solicitation platforms will be evaluated and analyzed. Visual components will be designed, and  user flows will be developed to define how users will interact with the platform. The UX/UI design will be user friendly and cater to both transit riders and operators. Technologies for the client side (such as HTML, CSS, JavaScript, React, Angular) and for server-side operations (such as Node.js, Python, Ruby) will be evaluated and the most suitable one will be selected. Given that this is a web-based platform for use across different mobile operation systems, selection for the database system will be made from systems such as MySQL, PostgreSQL, MongoDB, etc. Based on the needed features, an AI algorithm will be designed to automatically process rider complaints. The algorithm will be able to handle tasks such as data cleaning, topic classification, suggesting potential actions for operators, and updating riders about the status of their complaints. A pipeline will be established to facilitate the flow of public input to a dashboard that operators can use to monitor and manage the platform. Transit operators will be able to perform basic spatial and temporal analysis of rider inputs and derive insights to inform possible actions. 

For the deployment of the web-based platform, a hosting environment will be selected and the servers configured accordingly. Subsequently, the platform will be deployed on those servers to test individual components for functionality and to ensure seamless integration across all parts of the application. The platform will initially be released internally to collect feedback and make necessary adjustments. It will then be tested with transit riders in collaboration with the Detroit and Houston transit agencies. Based on riders’ feedback, the platform’s functionality and any inconveniences encountered by riders when providing input will be assessed. The platform can be updated, as needed, based on the riders’ feedback. Training session will be held with relevant transit agency staff. Demonstrations will be conducted on how to navigate the platform, access features like data filtering and spatial-temporal analyses tabs, and following up with a specific comment.]]></description>
      <pubDate>Wed, 09 Jul 2025 16:18:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/2572337</guid>
    </item>
    <item>
      <title>Characterizing and Mitigating Community-level Compound Algorithmic Biases for Data-driven Road Infrastructure Decision-making</title>
      <link>https://rip.trb.org/View/2556691</link>
      <description><![CDATA[Efficient road infrastructure maintenance, ranging from fixing cracks/potholes to repairing traffic signs, is critical to increasing ride comfort and traffic safety and reducing operation costs. In recent years, government agencies increasingly rely on the open e-government systems to acquire real-time data of road infrastructure conditions from community residents, due to their low-cost, timely, and transparent natures, for optimizing maintenance strategies. These data are often multi-modal and shared by people in active or passive manner: (1) in active e-government platforms, community residents can directly call in or send request via websites to report observed road infrastructure problems (e.g., 311 or City Connect programs), or (2) people can install mobile software on individual smart devices that automatically collect and share data in the back end while moving around the neighborhood. Such multi-modal human-centric crowdsensing data are further analyzed and utilized to train statistical or machine learning models for estimating road infrastructure conditions and commonly used to help the government agencies optimize allocations of maintenance resources. 
However, a major challenge is the sensing biases in the data that can cause inefficient maintenance decision-making. Previous studies indicate that community status would significantly impact the levels of civic engagement in multi-modal e-government systems. The disparities in community status may lead to distinct qualities of crowdsensed data and thus introduce sensing biases to the data (e.g., under-reporting and over-reporting). These sensing biases further activate and amplify the inherent algorithmic bias in data-driven infrastructure modeling, leading to under-estimation of the urgency and severity of maintenance demands. The biased estimations of road infrastructure conditions will finally lead to inefficient road maintenance service.  Existing studies mainly focus on understanding the engagement in single-mode e-government system or addressing algorithmic biases regardless of sensing biases, but none of them have yet to quantitatively reason the generation and propagation of the coupled social-algorithmic biases in multi-modal e-government systems and mitigate them jointly. 
This project aims to reason and mitigate community-level compound social-algorithmic biases generation and propagation in human-centric e-government systems for promoting efficient road infrastructure maintenance decision-making.
]]></description>
      <pubDate>Wed, 21 May 2025 12:51:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2556691</guid>
    </item>
    <item>
      <title>Network Level Proactive Traffic Operations Indicator (NPTOI) Using Artificial Intelligence (AI) and Connected Vehicle Data Integration</title>
      <link>https://rip.trb.org/View/2529964</link>
      <description><![CDATA[This project will focus on developing an extensive and implementable artificial intelligence (AI)-driven Network Level Proactive Traffic Operations Indicator (NPTOI) system aimed at mitigating urban traffic congestion and delays through effective and proactive prediction and prevention of traffic disruptions. The system will be built on real-time sensor-based and connected vehicle data, including various crowdsourced data from platforms such as the newly released Streetlight connected vehicles data, Lytx, LYNX, etc. and infrastructure-based data from Automated Traffic Signal Performance Measures (ATSPM), and as needed Microwave Vehicle Detection Systems (MVDS) and data from other available sensors, e.g., Close Circuit TVs (CCTVs), that could be used for validation or augment crowdsourced data. The NPTOI system can be integrated into existing traffic management infrastructure through testing and validation. NPTOI can also be used in a dashboard system that evaluates change over time and alerts operators to changes in the field that may affect traffic operations. Additionally, the University of Central Florida (UCF) team will analyze and report on the most effective Connected Vehicles data types for enhancing Florida Department of Transportation (FDOT) operations. Machine Learning (ML) methods such as Graph Neural Networks (GNN) and other techniques will be deployed to make such predictions since several ML algorithms are able to capture spatial and temporal features. Various data sources would be explored, and a combination of the sources will be experimented to obtain the best output predictions. The expected outcome of the research would enable FDOT to transition to AI-driven analysis reports that can screen network level traffic to give mobility indicators. The expectation is that such metric can enable operators to make decisions that can alleviate congestion.]]></description>
      <pubDate>Fri, 28 Mar 2025 08:00:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2529964</guid>
    </item>
    <item>
      <title>Leveraging Vehicle Camera Data for Road Condition Monitoring: A Crowdsourcing and Machine Learning Approach</title>
      <link>https://rip.trb.org/View/2408289</link>
      <description><![CDATA[One key part of pavement management is to assess the road condition and identify pavement distresses such as cracks and potholes. These road distresses, if not identified and repaired timely, could compromise road safety, cause expensive damage claims, and also lead to more expensive later repairs. To assess pavement condition, pavement condition data need to be collected first. However, traditional pavement data collection still relies on manual or specialized vehicles equipped with expensive sensors and requires personnel driving along each road in the road networks. Therefore, traditional road inspection methods are often costly, labor-intensive, and sporadic with limited coverage, leading to delayed maintenance and compromised safety. Recent advancements in machine learning (ML) and the proliferation of  vehicles equipped with various cameras (built-in or dashacams) and sensors offer a promising avenue for revolutionizing road condition assessment practices. This project will establish a framework for collecting and processing crowdsourcing vehicle camera data, and develop machine learning algorithms that uses such data to automatically assess road conditions and identify road damages such as cracks and potholes. The project has the potential to offer a more efficient, cost-effective, and real-time approach to road condition monitoring over large road networks and provide critical information for timely maintenance.]]></description>
      <pubDate>Fri, 26 Jul 2024 21:32:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2408289</guid>
    </item>
    <item>
      <title>Enhancing Safety for Vulnerable Road Users: Exploring the Impact of Funding for Unconventional Data Access on Safety Improvements, Phase II – Summit to Better Understand the Funding Mechanism for Ped/Bike Projects in State DOTs</title>
      <link>https://rip.trb.org/View/2401755</link>
      <description><![CDATA[Despite significant progress in leveraging unconventional data for pedestrian and bicyclist safety, previous phase of the project, “Exploring the Impact of Funding for Unconventional Data Access on Safety Improvements”, has revealed limitations in assessing the impact of funding for unconventional data access, notably due to sparse literature and minimal stakeholder involvement through basic surveys. To address these gaps and build upon Phase I findings, a modest budget extension is anticipated to facilitate a summit/peer-exchange. This summit aims to gather key industry stakeholders, public sector representatives, and relevant entities for in-depth discussions, interactions, and collaborations. By fostering a deeper understanding of the current landscape and encouraging knowledge-sharing, the summit will cultivate a thriving ecosystem for emerging data, particularly crowdsourced data. Ultimately, this initiative aims to drive meaningful enhancements in pedestrian and bicyclist safety within transportation systems by harnessing the potential of innovative data sources.]]></description>
      <pubDate>Mon, 08 Jul 2024 14:54:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2401755</guid>
    </item>
    <item>
      <title>Elevating Traffic Safety in Native American Communities: A Comprehensive Approach with Online Mapping and Crowdsourcing Solutions</title>
      <link>https://rip.trb.org/View/2401748</link>
      <description><![CDATA[This proposed project will focus on investigating the effectiveness of modern web-based tools and technologies in improving traffic safety education and decision-making within Native American communities. Technologies that will be implemented and investigated include, but are not limited to, spatial data visualization, spatial data management, spatial analysis, spatial education, and internet mapping. Leveraging free and open-source software programs, modules, and libraries, the research team aims to implement a tailored online mapping and analysis portal, along with a crowdsourcing tool. This approach ensures a cost-effective solution for the Native American communities we are committed to assisting and serving. The proposed project will begin by organizing comprehensive training workshops in collaboration with the New Mexico Local Technical Assistance Program (LTAP). These workshops will focus on the practical applications and benefits of an existing online traffic crash mapping and analysis portal developed by the University of New Mexico’s Center for Pedestrian and Bicyclist Safety (CPBS). The proposed project will also implement a crowdsourcing web application based on Volunteered Geographic Information (VGI), and at the same time incorporate gamification elements to encourage crowdsourcing of traffic crash data and addressing issues related to insufficient traffic data.]]></description>
      <pubDate>Mon, 08 Jul 2024 14:54:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2401748</guid>
    </item>
    <item>
      <title>Community-Centered Traffic Safety</title>
      <link>https://rip.trb.org/View/2325713</link>
      <description><![CDATA["Community-Centered Traffic Safety" is a research project focused on addressing the complex issue of traffic safety through a multifaceted, locally informed approach. Recognizing the limitations of uniform strategies, the project explores traffic safety challenges by considering varied cultural, financial, and infrastructural factors that influence different communities. The project includes four research thrusts: evaluating the effectiveness of current citation fine structures across different population groups, developing tailored safety messaging, creating a crowdsourced app to identify local safety concerns, and analyzing pedestrian crashes near transit stops to identify contributing factors.

This comprehensive effort involves collaboration with the Massachusetts Department of Transportation (MassDOT) and interdisciplinary student participation from Engineering, Planning, and Public Policy departments. The project will engage with local communities through surveys, focus groups, and observational studies, and will use the UMassSafe Crash Data Warehouse for analysis. Expected outcomes include a proposed revision to the fine structure, targeted traffic safety campaigns, and a prototype of a community-informed app. The project also emphasizes student involvement in data analysis, stakeholder collaboration, and app testing, contributing to the broader goal of improving traffic safety and strengthening community engagement.
]]></description>
      <pubDate>Mon, 22 Jan 2024 12:27:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2325713</guid>
    </item>
    <item>
      <title>The Future of HD Mapping: Crowdsourcing to Improve PNT Resilience and Safety</title>
      <link>https://rip.trb.org/View/2301345</link>
      <description><![CDATA[Autonomous driving systems are vulnerable to cybersecurity attacks due to their heavy reliance
on sensors such as GNSS, cameras, LiDAR, and radar. Cybersecurity attacks on Highly Automated
Transportation Systems (HATS), such as autonomous vehicles can lead to traffic accidents, resulting in catastrophic
loss of life and property damage. While various methods exist to detect and mitigate such attacks,
the cybersecurity robustness of live HD Map use and generation remains underexplored.
This proposal is built on Year 2 effort and expands on recent developments to incorporate STOA learning
methods for exploiting HD Maps to (1) counter cyberthreats by detecting and mitigating PNT interference
and (2) crowdsource data for their creation and updates.
Problem Statement: HD Maps are increasingly used by car manufacturers who typically create their own
HD Maps to support HATS vehicle navigation. HD Maps are generally static preloaded data that enable waypoint
navigation and drive control to keep vehicles on the road. The main gap in HD Map use is their lack of
support for vehicle absolute localization, which could independently validate GNSS data, detect PNT anomalies
and provide mitigation alternatives. The lack of a common HD Map format and their redundant reproduction
represent a significant inefficiency at national level.
Objectives: We have three specific objectives:
1. Develop a lightweight, real-time HD map generation model from vehicle cameras that runs without
high-end GPUs.
2. Realize HD Map-based localization by map-matching generated local map with offline global maps.
3. Support the development of a crowdsourced framework for creating, updating and dissemination of
live HD Maps, delivered as a government/industry-backed service.]]></description>
      <pubDate>Mon, 04 Dec 2023 18:50:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2301345</guid>
    </item>
    <item>
      <title>Exploring the Impact of Funding for Unconventional Data Collection on Vulnerable Road User (VRU) Safety Improvements</title>
      <link>https://rip.trb.org/View/2229367</link>
      <description><![CDATA[This study aims to bridge the gap between funding decisions, data access, and safety improvements for vulnerable road users (VRUs). By combining qualitative and quantitative analyses, this research will provide insights and guidance to transportation organizations, specifically state departments of transportation (DOTs) and metropolitan planning organizations (MPOs), to effectively incorporate crowdsourced data into their funding strategies and prioritize safety improvements for VRUs. Through the literature review, survey/interviews (with state DOTs, MPOs, and other transportation agencies), and case studies/comparative analysis, the research team tends to answer the following questions: (1) Will unconventional data become a primary method for non-motorist data collection in the future? (2) How does the level of funding for unconventional data affect safety improvements for VRUs? (3) What is the perceived impact of such investment on agencies’ decision-making processes? (4) What are the major impediments to encouraging data sharing such as privacy concerns? (5) What are the agencies’ data management or data governance practice of unconventional data sources?]]></description>
      <pubDate>Thu, 17 Aug 2023 08:21:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2229367</guid>
    </item>
    <item>
      <title>Use of Crowdsource Passive Data to Evaluate Effectiveness of Access Management Projects Proof of Concept</title>
      <link>https://rip.trb.org/View/2004404</link>
      <description><![CDATA[Implementation of access management projects has historically caused conflict between practitioners who support the safety and mobility benefits of access management and business/property owners who are concerned about the potential negative impacts of reduced access to adjacent properties. Elected officials who are responsible for supporting or approving project funding find themselves in a delicate balancing act in the debate between safety, economy, and mobility. Past evaluations of access management projects have been limited to manual traffic data collection, simulations, and evaluation of crash data many years after the project is implemented. In addition, evaluation of sales tax receipts before and after implementation of the project has been limited. The purpose of this project is to test the use of innovative data sources (Wejo connected vehicle data and SafeGraph point of interest data) to evaluate the safety, mobility, and economic effects of access management projects by comparing trips into adjacent development before and after implementation of the project. Wejo connected vehicle data is a rich data source that provides information about vehicle movements and vehicle events (e.g., hard braking, acceleration, etc.), so there are several aspects of this data that are beneficial to this evaluation. In addition, an economic analysis will be performed to evaluate the financial impacts on adjacent businesses.]]></description>
      <pubDate>Sun, 07 Aug 2022 15:39:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2004404</guid>
    </item>
    <item>
      <title>Leveraging Crowd-sourced Data in Planning, Design, Analysis, and Evaluation of Pedestrian and Bicycle Traffic</title>
      <link>https://rip.trb.org/View/1993822</link>
      <description><![CDATA[Collecting data on the number of people walking or bicycling along or across Michigan’s vast transportation network is difficult to achieve, it can be time consuming and expensive. However, knowing the numbers of people walking or bicycling would be immensely
useful in project planning, design, analysis, and evaluation of the transportation network for safety and accessibility among other measures. This research will help improve the assessment of the pedestrian and bicycle traffic exposure and help make informed
decisions when planning, designing, and evaluating projects.]]></description>
      <pubDate>Thu, 14 Jul 2022 13:02:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/1993822</guid>
    </item>
    <item>
      <title>Field deployment and verification of an AI-based crowdsensing bridge condition as­sessment platform</title>
      <link>https://rip.trb.org/View/1907232</link>
      <description><![CDATA[Bridge condition assessment and remaining useful life estimation is critical for maintaining functionality and enhancing the resilience of existing highway infrastructure in the United States. Currently, there are approximately 617,000 bridges in the U.S. that require periodical condition assessment and maintenance (artba [2021]). The current practice employed for remaining useful life estimation involves measuring the number of load cycles that a structure of interest is subjected to through approaches such as rainflow counting of strain. This typically entails deploying a fixed network of strain gauges on a bridge. However, scalability is an issue for this paradigm, especially owing to high costs and efforts associated with deployment and maintenance of wiring, sensors and battery that powers the monitoring system. 
To overcome the existing challenges and extend this type of analysis to virtually all bridges with little operational cost, this project proposes a large-scale field implementation of a mobile sensing-based paradigm that harnesses artificial intelligence for condition assessment of an inventory of bridges. This will facilitate the use of crowdsourced data for real-time bridge health assessment at unprecedented rates, resolution and scales
]]></description>
      <pubDate>Wed, 02 Feb 2022 10:55:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/1907232</guid>
    </item>
    <item>
      <title>Developing a Regional Signal Performance Measurement Methodology in the 2021 Urban Mobility Report</title>
      <link>https://rip.trb.org/View/1885447</link>
      <description><![CDATA[The Urban Mobility Report (UMR) has been providing information on urban congestion levels in the
U.S. for more than three decades. The UMR uses private-sector crowdsourced speed data combined with traditional public-agency roadway inventory data to measure mobility conditions.  The current UMR statistics describe overall congestion levels, but do not discriminate between causes of congestion. With the advent of improvements in the third-party provider data stream, it is now possible to quantify aspects of the mobility contribution provided by enhanced traffic signal systems. This project will use detailed crowdsourced travel time data collected by a private-sector vendor (INRIX) to report on the performance of traffic signal systems. Researchers will review and rank signal operations for urban areas by evaluating signal operations metrics obtained from crowdsourced data, such as arrivals on green and split failures. These metrics will enhance the evaluations that can be obtained from traditional metrics such as arterial street delay. Urban areas will be categorized using factors such as congestion levels, population and population density to ensure that any comparisons include contextual elements that are key to decision-maker understanding and messaging strategies.

These traffic signal metrics will provide an approach to measure signal system contributions. For example, improved efficiencies on the streets will result in less delay and/or higher traffic throughput while the signal systems are improving performance. The 2021 Urban Mobility Report website will include an analysis of urban area signal performance. In subsequent years, the methods will be honed with feedback following the release of the information on the 2021 UMR website. NICR will be shown as a 2021 Urban Mobility Report sponsor on the UMR website.
]]></description>
      <pubDate>Thu, 14 Oct 2021 12:36:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/1885447</guid>
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