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Publication Number:  FHWA-HRT-19-004    Date:  Summer 2019
Publication Number: FHWA-HRT-19-004
Issue No: Vol. 83 No. 2
Date: Summer 2019

 

Communication Product Updates

Compiled by Lisa A. Shuler of FHWA's Office of Corporate Research, Technology, and Innovation Management

Below are brief descriptions of communications products recently developed by the Federal Highway Administration's Office of Research, Development, and Technology. All of the reports are or will soon be available from the National Technical Information Service (NTIS). In some cases, limited copies of the communications products are available from FHWA's Research and Technology (R&T) Product Distribution Center (PDC).

When ordering from NTIS, include the NTIS publication number (PB number) and the publication title. You also may visit the NTIS website at www.ntis.gov to order publications online. Call NTIS for current prices. For customers outside the United States, Canada, and Mexico, the cost is usually double the listed price. Address requests to:

National Technical Information Service
5301 Shawnee Road
Alexandria, VA 22312
Telephone: 703–605–6050
Toll-free number: 1–888–584–8332
Website: www.ntis.gov
Email: customerservice@ntis.gov

Requests for items available from the R&T Product Distribution Center should be addressed to:

R&T Product Distribution Center
Szanca Solutions/FHWA PDC
700 North 3rd Avenue
Altoona, PA 16601
Telephone: 814–239–1160
Fax: 814–239–2156
Email: report.center@dot.gov

For more information on R&T communications products available from FHWA, visit FHWA’s website at www.fhwa.dot.gov, the FHWA Research Library at https://highways.dot.gov/resources/research-library/federal-highway-administration-research-library (or email fhwalibrary@dot.gov), or the National Transportation Library at ntl.bts.gov (or email library@dot.gov).

Foundation Reuse for Highway Bridges (Report)

Photo. Cover of Foundation Reuse for Highway Bridges.

Publication Number: FHWA-HIF-18-055

When an existing bridge is being considered for reconstruction or major rehabilitation because of a deteriorated or obsolete superstructure, the foundation may still have significant functional value. Reusing these foundations during bridge replacement or widening can provide significant cost and time savings over constructing new elements. The potential time savings can reduce mobility impacts, and the cost savings can increase the economic viability and sustainability of the project.

FHWA's report Foundation Reuse for Highway Bridges focuses on reusing bridge foundations for bridge reconstruction projects. The report addresses critical issues encountered during the decisionmaking process, including the assessment of existing bridge foundations for structural and geotechnical integrity, durability, and load-carrying capacity. It also discusses planning for reuse during the construction of new bridges, including strengthening and designing bridge foundations for future reuse—an important sustainability initiative.

The report includes examples from the United States and Canada to highlight the benefits of foundation reuse from social, environmental, and economic perspectives. The report is geared toward transportation professionals and agencies interested in reusing bridge foundations.

The document is available for download at www.fhwa.dot.gov/publications/research/infrastructure/structures/18055/hif18055.pdf.

Using Data Analytics for Cost-Effective Prediction of Road Conditions: Case of the Pavement Condition Index

Photo. First page of the summary report Using Data Analytics for Cost-Effective Prediction of Road Conditions: Case of the Pavement Condition Index.

Publication Number: FHWA-HRT-18-065

Municipalities and transportation departments devote considerable effort to collecting data, particularly related to road conditions. Many small municipalities do not have sufficient resources to collect data regularly, and in larger municipalities, collecting field-based data may be hampered by crew safety and traffic interruptions. Data analytics could help reduce these negative impacts. In this study, researchers used data analytics to test if affordable and easy-to-collect data can be used to predict future values of the Pavement Condition Index (PCI).

This paper demonstrates how machine-learning models can help municipalities predict the PCI values of roads using easy-to-collect and cost-effective attributes. The scope of this paper is not limited to predicting the conditions of roads using data analytics. The authors also investigated the relative significance of a road's attributes in its deterioration. This type of analysis can guide municipalities and transportation departments in crafting a more efficient policy of data collection and data management. Researchers chose to use the PCI because municipalities and transportation departments in North America commonly use it.

The document is available for download at www.fhwa.dot.gov/publications/research/infrastructure/pavements/ltpp/18065/index.cfm.

The Role of Artificial Intelligence and Machine Learning in Federally Supported Surface Transportation Initiatives

Photo. Cover of The Role of Artificial Intelligence and Machine Learning in Federally Supported Surface Transportation Initiatives.

Publication Number: FHWA-HRT-18-066

The term artificial intelligence might call to mind talking robots that efficiently perform some of the functions that humans do, or—within the context of the transportation sector—cars driving by themselves. However, artificial intelligence is currently helping highway transportation researchers answer many questions that will result in safer and more reliable roadways.

The Role of Artificial Intelligence and Machine Learning in Federally Supported Surface Transportation Initiatives, published by FHWA's Exploratory Advanced Research (EAR) Program, explores the development of artificial intelligence and machine learning technology within the surface transportation sector. FHWA seeks to make surface transportation safer and more efficient by collaborating with universities, industry, and other Government agencies that conduct cutting-edge research in artificial intelligence.

The publication examines how artificial intelligence in the transportation sector can benefit the public, and how the EAR Program supports research in artificial intelligence and machine learning. It also addresses challenges and opportunities associated with this new and promising technology.

The document is available for download at www.fhwa.dot.gov/publications/research/ear/18066/18066.pdf.

Applications of Knowledge Discovery in Massive Transportation Data: The Development of a Transportation Research Informatics Platform (TRIP)

Photo. Cover of Applications of Knowledge Discovery in Massive Transportation Data: The Development of a Transportation Research Informatics Platform (TRIP).

Publication Number: FHWA-HRT-19-008

Transportation researchers and practitioners have access to enormous amounts of data but often lack the tools to easily store, manipulate, and analyze these data. The Transportation Research Informatics Platform (TRIP) is an informatics-based system designed to manage large amounts of transportation data and provide researchers an efficient way to conduct analytics on big data.

The objectives of TRIP include creating the ability to handle large quantities of transportation data; using open-source technologies and tools to ingest, store, align, and process data; accepting structured, semistructured, and unstructured datasets from any source; providing an efficient way to query data without indepth knowledge of metadata; integrating open-source and consumer off-the-shelf analytics products; and providing visualization tools to offer greater insights into data. TRIP architecture is flexible and built on state-of-the-art, open-source technology developed with big data in mind. Although predominantly developed for transportation safety research, TRIP can address issues pertaining to operations and maintenance given the incorporation of the appropriate datasets.

This report supplies the resources and instructions on how to set up an instance of the platform and how to operate it, and will be useful for transportation researchers, operators, and data managers interested in working with large transportation datasets.

The document is available for download at www.fhwa.dot.gov/publications/research/safety/19008/19008.pdf.

Efficient Messaging for Changeable-Message Signs During Nonrecurring Events

Photo. First page of the report Efficient Messaging for Changeable-Message Signs During Nonrecurring Events.

Publication Number: FHWA-HRT-19-012

Changeable-message signs (CMSs), also referred to as variable- or dynamic-message signs, are programmable signs used on roadways throughout the United States. CMSs provide relevant, current information safely, and are among the most popular methods of disseminating information about nonrecurring events to travelers.

This publication examines the various aspects of creating useful and informative CMSs. Topics discussed in the document include units of information, limitations on message length, message brevity and consistency, and phrases to avoid.

The document is available for download at www.fhwa.dot.gov/publications/research/safety/19012/19012.pdf.

 

 

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