research in progress database

Research in Progress

RIP The Transportation Research Board's Research in Progress (RIP) Database contains information on more than 9,700 current or recently completed transportation research projects. RIP records primarily are projects funded by the U.S. Department of Transportation and State Departments of Transportation. University transportation research also is included in the database.

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• TRID Database Homepage
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• Literature Searches and Literature Reviews for Transportation Research Projects

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Questions or comments? E-mail [email protected]

• Frequently Asked Questions

RIP and TRID FAQs

• Database Comparison

TRID VS RIP

What is the difference between the trid and rip databases.

What is RIP?

Why use rip.

• Prevent duplication of research
• Connect researchers working on similar projects
• Identify experts for panels or committees
• Highlight research being done by your agency
• Required for UTCs and federally funded projects

Who enters records in RIP?

What projects must be added to rip.

Under the USDOT Public Access Plan requirements, any transportation-related scientific research project receiving ANY federal funding must be entered in RIP. This is effective for new funding agreements established after 12/31/2015 and existing funding agreements adding additional funding after 12/31/2015.

Projects conducted through the USDOT’s University Transportation Centers Program must be entered into RIP. This is a mandatory requirement of the award. UTCs should consult their UTC grants manager if there are questions concerning this requirement.

What is scientific research as defined in the DOT Public Access Plan?

May projects that are not federally funded by added to rip, how do i get access to enter records in rip, do i need a password to search for records in rip, what happens when more than one organization is funding a project, when will records appear in rip, is my job done once i have entered my project records in rip, what is the difference between the project records that appear in trid and those in the rip database, what is the difference between rip and the usdot’s research hub database.

The RIP database provides records on current or recently completed records from many sources, while the USDOT Research Hub is focused only on USDOT-sponsored research. TRB and USDOT have agreed to exchange project records on a regular basis to ensure that both databases contain a comprehensive account of USDOT's research portfolio. While the basic design and functionality of the USDOT Research Hub is similar to that of RIP, there are several important differences between the two databases that are summarized in the table below.

What is TRID?

Do i need an id/password to search for records in trid, how do i obtain the full text of an article, paper, or report that i have found in trid.

TRB does not own the documents described in TRID, apart from reports published by TRB or the Highway Research Board (HRB).

However, many records in TRID do include a link to free OR fee-based full text. Access to full text through these links will depend on the publisher or your institutional/personal subscriptions. Your local or institutional library also can assist you in obtaining the document through interlibrary loan. You may find this resource useful for identifying local libraries that own or have access to a specific publication: https://www.worldcat.org/ .

Papers presented at TRB Annual Meetings, conferences or events may be obtained only through interlibrary loan.

Contact us if we can help you find a publication.

Why submit publications to TRID?

• It provides wider dissemination and easier discovery of your work.
• It helps other transportation agencies avoid duplication of work and save resources.

Additionally… After completing each federally funded project, researchers, State Departments of Transportation and University Transportation Centers are required to notify TRB of the URL of the full text of final reports for indexing and abstracting in TRID.

For further reference see: National Transportation Library Public Access Plan Compliance https://ntl.bts.gov/public-access/how-comply

AASHTO Report Guidelines and Requirements https://research.transportation.org/Report-Guidelines-and-Requirements/

23 CFR Part 420 – Planning and Research Program Administration https://www.gpo.gov/fdsys/pkg/CFR-2017-title23-vol1/pdf/CFR-2017-title23-vol1-part420.pdf

U.S. DOT Grant Deliverables and Requirement for UTCs https://www.transportation.gov/utc/2013-grant-deliverables-and-requirements-utcs https://www.transportation.gov/utc/FAST-act-deliverables-and-requirements-utcs

Should I submit final reports from projects that were not federally funded?

How do i submit publications for indexing in trid, what should i submit to trid, what about older publications that have recently been digitized and made available online, what is not accepted for indexing in trid.

• Market research
• Vehicle standards and specifications
• Patent information
• Military transport

In addition, we do not include:

• Book reviews
• Discussions/closures
• Annual reports of organizations
• Articles of a page or less
• Straight summaries of reports (We’d rather have the report itself)
• Introductions to “special issues” that merely summarize the papers within that journal.
• “Newsy” articles
• Articles that summarize conferences
• “Advertorials” or other articles that are actually paid advertisements

How do I know if my report has been indexed in TRID?

We have recently changed the links to our reports. how can i ensure the new links are updated in records indexing our reports in trid.

• Keyword Program Grant ID Title Display ID State ORCID Performer Sponsor Manager Search

Frequently Asked Questions(FAQs)

• What is your definition of a research project?

The database contains research, development, and technology projects as defined below*:

• Isn't this database duplicating the Transportation Research Board's Research-in-Progress (RiP) database?

No. The RiP database is a central repository for active transportation research underway in the United States and internationally, while the USDOT Research Hub is focused only on USDOT-sponsored research. While RiP does contain some USDOT-sponsored projects, up until now many USDOT research programs have not been represented. The USDOT Research Hub initiative has therefore focused on obtaining information on the USDOT-sponsored research programs that have not been represented in RiP. TRB and USDOT have agreed to exchange project records on a regular basis to ensure that both databases contain a comprehensive account of USDOT's research portfolio. While the basic design and functionality of the USDOT Research Hub is similar to that of RiP, there are several important differences between the two databases that are summarized in the table below.

• Why isn't my research program/project included in the database?

The USDOT’s research investment is very large – typically in excess of $1 billion per year. The database represents Office of the Assistant Secretary for Research and Technology (OST-R)’s best effort at capturing a comprehensive account of the Department’s research portfolio at the project level. We intend to continually improve the coverage of the database over time. If you know of a program or project that is USDOT-sponsored and should therefore be included in the database, please let us know .

• is the difference between a Sponsor and a Manager?

The project “sponsor” is the USDOT agency that is the original source of funding for the project, while the project “manager” is the organization that manages the program through which the project is funded (other federal and non-federal organizations may also co-sponsor a project). In most cases, the project is sponsored and managed by the same USDOT agency. However, in some cases the organization managing the research is different from the sponsoring agency. Examples include the various Cooperative Research programs, which are sponsored by USDOT agencies but managed by the Transportation Research Board.

• Why is information often missing from the project records?

Field coverage is highly variable across the database. The database collects information from a wide-range of sources. Some sources match closely to the kinds of information required to populate the USDOT Research Hub fields, while other sources do not. We have populated as many fields as possible using the existing data sources at our disposal.

• Why aren’t Performer contact details provided in the database?

A significant amount of the data provided in the USDOT Research Hub comes from internal USDOT databases. It was not considered appropriate to provide Personally Identifiable Information (PII), such as emails and phone numbers, of people working within other, non-Federal organizations, if this information is not already in the public domain. In this case, only the Performer organization name, business address, and point of contact name have been provided. Performer emails and phone numbers are included in the database if this information already appears on a public facing website.

• Where can I find out more about USDOT and each of its Operating Administrations?

Weblinks to further information are provided below:

USDOT: http://www.dot.gov/

USDOT Agencies:

* Not represented in the database

• Are there any other sources of information on USDOT-sponsored research and other transportation research in the U.S?

Transportation Research Board:

• Home page - http://www.trb.org/Main/Home.aspx
• Research in Progress (RiP) database- http://rip.trb.org/
• Transportation Research Information (TRID) Database - http://trid.trb.org/
• Is the search engine capable of full Boolean search functionality?

Yes. The search engine is capable of a variety of search syntax options including Boolean searching, phrase searching, truncation, wildcards, and word stemming. For more information click here .

TRB Research in Progress (RIP) Database

Most of the RiP records are projects funded by Federal and State Departments of Transportation. University transportation research is also included. The RiP Database now serves as a clearinghouse of University Transportation Centers ongoing research.

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• Policy & Planning ,
• United States & Canada

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The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

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