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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>
    <image>
      <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>Enhancing Railway Track Data Measurements and Collection
System with Blockchain Technology- Use of Inter Blockchain Communications
</title>
      <link>https://rip.trb.org/View/2431591</link>
      <description><![CDATA[The public demand for railway safety continues to be a major concern for the railway operators, the public, and the government. The Federal Railway Administration has invested in the development of various equipment for rail data collection. The automated data collection equipment has been very successful, but in this era of cyber breaches, the receiving, sharing, and access to data are of paramount importance. Blockchain technology, although in its infancy will create an immutable record for the data, storage, and reporting if incorporated into the current automated data equipment. The main objective of this proposal is to develop a framework whereby blockchains will be used as lines of cyber defense and in storing and sharing railway track data and information.
The number of blockchain platforms and decentralized applications are increasing rapidly in the last few years. Most of the existing blockchain networks are operating in a standalone environment isolated from each other. This creates scalability issues and in most cases connectivity nightmares, therefore limiting the application of the technology. This will be achieved through the following sub-objectives: This approach will apply to both passengers, freight, and metro systems. This Blockchain model can further be connected to various railway information systems hence providing additional defense methods against hackers. The blockchain will provide some “sort of supply chain” so only authorized personnel can approve and share the information in real-time. The successful implementation of this project can be extended to other autonomous vehicle data collection systems. The project at this level will be based on a private blockchain.]]></description>
      <pubDate>Tue, 17 Sep 2024 17:36:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2431591</guid>
    </item>
    <item>
      <title>SPR-4918:  Enhancing Pavement Instrumentation and Monitoring: A Novel Edge-First, Network Level Solution for Pavement and Roadside Sensors and Live Data Visualization</title>
      <link>https://rip.trb.org/View/2422896</link>
      <description><![CDATA[This project will develop an Avena-powered hardware system to sample in-pavement sensors, host a camera package and analysis software, and integrate remote data storage and visualizations. The initial prototypes will be installed at the Accelerated Pavement Testing (APT) facility and I-65 near Lebanon, improving the utility of embedded pavement sensors for comprehensive road health monitoring.]]></description>
      <pubDate>Thu, 29 Aug 2024 08:48:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2422896</guid>
    </item>
    <item>
      <title>Synthesis of Information Related to Highway Practices. Topic 55-02. Practices for Collecting, Managing, and Using Light Detection and Ranging (LiDAR) Data</title>
      <link>https://rip.trb.org/View/2190449</link>
      <description><![CDATA[The objective of this synthesis was to document state DOTs’ practices related to technical, administrative, policy, and other aspects of collecting, managing, and using LiDAR data to support state DOTs’ current and future practices. ]]></description>
      <pubDate>Mon, 05 Jun 2023 16:55:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2190449</guid>
    </item>
    <item>
      <title>Michigan Cone Penetrometer Test Calibration</title>
      <link>https://rip.trb.org/View/2143711</link>
      <description><![CDATA[The Michigan Department of Transportation (MDOT) purchased Cone Penetration Test (CPT) equipment in 2019 to better define the geotechnical conditions at project sites.
To date, MDOT has used the CPT on 12 sites including the M-66 Monroe Creek Crossing. One year of CPT data has been
collected on projects that were also drilled using traditional techniques. A total of 50-55 CPT soundings exists alongside
traditional soil borings with visual descriptions and blow counts. Some grain size distribution and other lab tests have been
conducted from the soil boring samples. MDOT has graphed some of the data on published soil type behavior charts to note
preliminary correlations. However, more statistical comparison is needed to calibrate the Michigan CPT test and identify
procedures that should be followed to produce and interpret Michigan soil data reliably. In addition, MDOT could benefit from a
standardized procedure that stores data in the Data Interchange for Geotechnical and Geoenvironmental Specialists (DIGGS)
data storage format and provides automated output that assists with risk-based design. Further identifying site variability may
help with appropriate site characterization and design savings.]]></description>
      <pubDate>Mon, 27 Mar 2023 12:01:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2143711</guid>
    </item>
    <item>
      <title>Transportation Enterprise Data Warehouse Implementation Guide



</title>
      <link>https://rip.trb.org/View/1957057</link>
      <description><![CDATA[As part of a robust data governance strategy, transportation agencies must decide how to best manage the storage, access, and dissemination of data products and services for internal use/reuse and external distribution within its data architecture. An important piece of modern data architecture is an enterprise data warehouse, which, conceptually, will provide a way to reduce data redundancy, improve data consistency, and enable data usage for better decision-making. Effective data warehouse implementations are complex, especially when an entity has highly diverse data sets and technology infrastructure.

State departments of transportation (DOTs) need guidance on how to best architect and implement an enterprise data warehouse strategy. DOTs would also benefit from guidance on the complete set of functional requirements including, but not limited to, federation (aggregating from multiple sources), data extract-transform-load (ETL) and extract-load-transform (ELT), storage, naming convention, structure (model), roles and responsibilities, web services, data publishing processes, access by third-party applications, and records management. Any conventions, models, and structure covered by the guidance will also need to support and align with standard frameworks used in transportation, such as Industry Foundation Classes (IFC), to the greatest extent practical.

OBJECTIVE: The objective of this research is to develop a guide for enterprise data warehouse development, implementation, and best practices to support DOT business needs.

DOT business needs include, but are not limited to, efficient upload, use, sharing, security of data, data analytics, operations management, performance management, asset management, safety management, data-driven decision-making, data integration, and federal and state data reporting.]]></description>
      <pubDate>Fri, 27 May 2022 12:57:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/1957057</guid>
    </item>
    <item>
      <title>Cost-effective Approach Towards Building a Traffic Sign Data Inventory Using Open Street Images</title>
      <link>https://rip.trb.org/View/1942838</link>
      <description><![CDATA[Traffic signs are critical assets for roadway and infrastructure management. They are also in a great variety and different conditions. According to the asset management plan proposed by US DOT, the research team proposes a cost - effective approach to build a traffic sign data inventory using open street images. The system consists of three sub-systems. Firstly, the data collection system can capture the real-time open street image data along with the associated information. Secondly, the team will train a traffic sign detection and recognition model to extract the information from original images. Finally, the data storage system will store the recognition results as well as associate information into the database.]]></description>
      <pubDate>Fri, 22 Apr 2022 09:43:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/1942838</guid>
    </item>
    <item>
      <title>SPR-4540: Incorporating Time Dependent Data to Pro-Active Safety Management –A Pilot Study</title>
      <link>https://rip.trb.org/View/1722749</link>
      <description><![CDATA[The current reactive safety-related decision making is based on crash data collected for at least three years. The existing road data supplemented with emerging data that reflect temporal changes in safety factors such as volume, speed, weather, and pavement conditions are expected to be useful in estimating hourly risks of crashes and their severity. The project will demonstrate the development and proactive use of new safety management system components for freeway rural segments and urban intersections. The results will include the guidelines of acquiring, storing, and handling massive amount of data needed for this purpose.]]></description>
      <pubDate>Mon, 20 Jul 2020 12:54:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/1722749</guid>
    </item>
    <item>
      <title>Synthesis of Information Related to Highway Practices. Topic 52-05. Implementation of Subsurface Utility Engineering for Highway Design and Construction</title>
      <link>https://rip.trb.org/View/1707235</link>
      <description><![CDATA[While it is recognized to be in the public interest to permit the installation of utility infrastructure in roadway rights-of-way, the practice has contributed to utility-related issues being one of the leading causes of delays for transportation projects. Subsurface utility engineering (SUE) is an approach state departments of transportation (DOTs) have implemented to locate utilities and assist their project-development teams with avoiding these issues.

The TRB National Cooperative Highway Research Program's NCHRP Synthesis 583: Implementation of Subsurface Utility Engineering for Highway Design and Construction documents state DOT use and practices related to SUE and specifically examines how and when SUE is implemented during the project-design and delivery processes.]]></description>
      <pubDate>Tue, 19 May 2020 09:40:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/1707235</guid>
    </item>
    <item>
      <title>Utilization of Lidar Technology — When to Use It and Why</title>
      <link>https://rip.trb.org/View/1638641</link>
      <description><![CDATA[The use of Lidar can add value to almost any transportation project. However, the expense of data collection and post-processing may outweigh the overall benefits. This project will explore integrating Lidar technology into KYTC operations is most feasible and affordable. After developing a robust understanding of the benefits and cost of collecting and using Lidar data, the project will address best practices for data storage, software solutions for post-processing, and how data can be used within multiple operations across Kentucky.]]></description>
      <pubDate>Wed, 17 Jul 2019 13:37:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/1638641</guid>
    </item>
    <item>
      <title>Modernization of Iowa Transportation Program Management System TR-726
</title>
      <link>https://rip.trb.org/View/1580364</link>
      <description><![CDATA[The research aspect of this project will consist of cataloging, evaluating and optimization of operations. It will explore the processes via which Iowa Local Public Agency transportation projects are chosen, programmed, prepared and delivered, with the goal of optimizing the utilization of human resources. The project will also seek to replace exchanges of documents for review/approval with actually storing the document data online and facilitating performance of processing/review steps within the application itself.

Phases I and II will seek to acquire a multi-layered understanding of underlying business processes, user decision and perception patterns, function delivery, and technical needs. In Phase III, as an operations research effort, the project will seek to fulfill the Iowa Code 310.35 objective of “the more efficient use of funds and materials that are available for the construction and maintenance of secondary roads” by providing road agencies with on-line process and communications tools that minimize engineering staff time that must be spent on the programming, budgeting, and development tasks involved. 
The deficiencies of the legacy code and design will be addressed, security will be enhanced to meet modern threat levels, the code will become capable of functioning on a larger slate of devices, and support integrated submittal/review/update processing. By improving process efficiency, the modernized system will, consistent with the intent of the FHWA’s “Make Every Day Count” initiative, help reduce the cost and time required to deliver bid ready projects for public benefit. 

TPMS modernization will be performed in four phases. During Phase I, the Principal Investigator will seek out representatives of the various user groups, (county engineers, cities, planning agencies, DOT – [Local Systems, Program Management, Systems Planning, District Local Systems staff, District Planners], RPA & MPO planning agencies, FHWA personnel, and design consultants), to learn how they are using TPMS tools, what’s going well, what needs improvement, and what new functionality would be desired. There will also be a forensic review of existing TPMS code, to capture business rules implicitly established via code patches made over the last 14 years. Data back-up methods and sites will also be re-evaluated.

]]></description>
      <pubDate>Mon, 28 Jan 2019 12:42:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/1580364</guid>
    </item>
    <item>
      <title>Improving Access and Management of Transit ITS Data</title>
      <link>https://rip.trb.org/View/1577716</link>
      <description><![CDATA[Data from bus and rail intelligent transportation systems (ITS) are a valuable resource for transit service planning and management.  In particular, vehicle location and passenger activity data from automatic vehicle location (AVL), automatic passenger counter (APC), and automatic fare collection (AFC) systems can be used to provide essential insight into transit operations and to inform decision making to increase the efficiency, productivity, and safety of transit service. There are, however, significant challenges for transit agencies in accessing and using this data. Many agencies cannot get to the data at all or do not understand the data they have. Data validation and quality control, integration and matching across various data sets, and aggregating data are all difficult, as is developing the types of reports, tools, and analytics that contribute to informed decision making. Even when transit agencies, researchers, and consultants do address these challenges, they often have difficulty sharing their work with their peers in the industry because the same types of data may be managed differently among transit agencies. The result is that transit ITS data is rarely used to its full benefit.

Creating a common approach to accessing and managing transit ITS data would facilitate the development and exchange of data management practices, of advanced reports and tools, and of new analytical techniques among transit agencies. The use of the General Transit Feed Specification (GTFS) format as the basis for a wide variety of service planning and customer information tools is a useful comparison. By providing a common way of representing schedule data, GTFS has facilitated significantly faster development of tools than would otherwise occur if every transit agency had their data in different formats. As a result, there is a recognized need for research to generate similar advancements in the use of transit ITS data, replicating improvements comparable to what GTFS has achieved for schedule data.                           

The objective of this research is to develop a common, practical approach to storing, accessing, and managing fixed-route transit ITS data.]]></description>
      <pubDate>Tue, 08 Jan 2019 07:15:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/1577716</guid>
    </item>
    <item>
      <title>Weather Data Environment - WxDE and ITS Data Repository</title>
      <link>https://rip.trb.org/View/1499598</link>
      <description><![CDATA[No abstract provided.]]></description>
      <pubDate>Mon, 29 Jan 2018 08:59:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/1499598</guid>
    </item>
    <item>
      <title>In-depth Investigation of the System Currently Used by the Las Vegas Metropolitan Police Department to Store and Process Crash Data and All Other Interconnected Systems</title>
      <link>https://rip.trb.org/View/1329139</link>
      <description><![CDATA[The existing software and hardware used by police agencies in Nevada to collect crash data are obsolete for a number of reasons, ranging from budget constraints to lack of coordination. Consequently, crash data collected in Nevada has quality and precision issues.  Accurately locating crashes is the key to geographic analyses of crash statistics and patterns as well as for the development of safety recommendations at intersections and other crash 'hotspots'. Currently, Safety Engineering at the Nevada Department of Transportation (NDOT) goes through a complicated process to locate crashes from the Nevada Statewide Crash Database (NCATS) on Nevada's public roads. Many crashes are unlocated or mislocated. The main impediments to locate crashes accurately are well known and include errors in data entry, street name errors by the recording officer, the existence of alias names, county coding errors as well as many other factors. The objective of this study is to understand and document, in detail, the existing software and hardware architecture used at Metro to collect, store, and process crash data as well as any other interdependent activities. It is known that the current implementation Metro uses takes care of multiple transactions. Further, the existing implementation is the result of a sequential process over years of developments and upgrades. At present, it is unclear how the existing system works, what are the interdependencies across various systems, and what would be the effects of changes to system components. Any development to replace or upgrade existing technologies at Metro for data collection must take into consideration how the replacement or upgrade is going to affect other systems and transactions. Metro is unlikely to agree to any change of the existing system without full consideration of the impact on their entire operation. Further, any change to the existing Metro system must guarantee that all the current capabilities will be available after the change; in fact, Metro is unlikely to agree to any deal that cannot guarantee this condition.  The scope of this study is limited to Metro, which is the largest crash data collector in Nevada. The research team has been working with Metro for several months, and Metro has been extremely pleased with the work that the research team has done already. Metro is very interested in cooperation with NDOT and University of Nevada at Las Vegas (UNLV). Other Nevada police agencies are likely to follow Metro's example.]]></description>
      <pubDate>Wed, 29 Oct 2014 01:00:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/1329139</guid>
    </item>
    <item>
      <title>t-HUB: The Public Transport Data Center of Connecticut</title>
      <link>https://rip.trb.org/View/1251763</link>
      <description><![CDATA[The total quantity of global digital data is expected to reach 7.9 zettabytes (1 trillion gigabytes) by 2015.  The McKinsey Global Institute estimates there will be a Big Data talent gap of 140,000 - 190,000 people globally, a gap between the supply and demand for people with the skills to properly analyze and interpret Big Data. Big Data and its inherent challenges and opportunities for improved public transportation operations and research in Connecticut has been a focus of the Public Transportation Systems research group at the University of Connecticut over the past year.  An outgrowth of these efforts is t-HUB, and initiative designed to serve big data needs for the public transportation community. t-HUB is a central data storage point, access point, management point and analysis point for transit operators and planners, hosted at the University of Connecticut. Title VI of the Civil Rights Act of 1964 (42 U.S.C. Section 2000d) states that "No person in the United States shall, on the ground of race, color, or national origin, be excluded from participation in, be denied the benefits of, or be subjected to discrimination under any program or activity receiving Federal financial assistance."   Chapter V of the Federal Transit Administration (FTA) Circular 4702.1A details the data collection and monitoring requirements of recipients and subrecipients of FTA funds.  In particular, requirements are given for collecting demographic data, setting system-wide service standards and policies, evaluating service and fare changes, monitoring transit service, and developing a Title VI evaluation plan. These federal requirements present several challenges to the state of Connecticut, as there are 14 Regional/Metropolitan Planning organizations in Connecticut, along with 15 transit operators in the state impacted by the Connecticut Department of Transportation's Title VI reporting and monitoring requirements.  In particular, there are challenges regarding: data collection and management; survey development, implementation and analysis; and, statewide adoption and implementation consistency.  The University of Connecticut (UConn) possesses significant expertise in data collection, data mining, survey development and distribution, and houses the resources for centralizing large-scale data initiatives.  In the public transportation realm, these expertise and resources are being consolidated in t-HUB, a statewide data resource for public transportation systems.   The benefits of t-HUB are: (1) streamline data management processes saving time and resources; (2) avoid duplicative efforts by the 30+ transit operators and planning agencies in CT; (3) best practices in data collection and management more easily spread throughout the state; (4) centralize burden of data storage and management; (5) leverage the infrastructure and flexibility of UConn's computational resources; (6) leverage UConn research expertise in data mining and analysis; (7) educate students - creating talent to manage Big Data; (8) build a single, centralized access point for data needs - such as Title VI requirements; and (9) improve connection between transit practitioners, UConn researchers and students. The vision for t-HUB is bold and large in scale.  Multiple phases will be necessary to realize the vision.  This project concentrates on developing a prototype analysis tool and outlining the needs of a fully-deployed system.]]></description>
      <pubDate>Tue, 04 Jun 2013 01:00:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/1251763</guid>
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