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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Improving Our Understanding of How Highway Congestion and Pricing Affect Travel Demand</title>
      <link>https://trid.trb.org/View/1530094</link>
      <description><![CDATA[Driver response to congestion and road pricing is an essential element to forecasting the future use of roadway systems and estimating the effect that pricing has on demand and route choice. This project develops mathematical descriptions of the full range of highway user behavioral responses to congestion, travel time reliability, and pricing. These descriptions were achieved by mining existing data sets. The report estimates a series of nine utility equations, progressively adding variables of interest. This research explores the effect on demand and route choice of demographic characteristics, car occupancy, value of travel time, value of travel time reliability, situational variability, and an observed toll aversion bias. This research reveals a number of policy implications. Drivers place a value on travel time across a wide range from $5 to over $50 per hour and approaching $100 per hour when trip pressure is high. Therefore, toll levels have to be significant to influence congestion. Travelers’ responses to congestion and pricing are also dependent on the options available. Driver response to congestion and pricing usually escalates from changing a route or departure time, to switching to transit if available, to rescheduling trips, and finally moving or changing jobs. Providing travel options is an important complement to a road pricing strategy that is aimed at reducing congestion. Finally, improvements to travel time reliability are as important as improvements to average travel time. This implies that operational improvements and information provided to travelers may be as valuable as increases in speed. The report contains extensive documentation on the estimation of these models and the policy implications. It also contains insights on the value of travel time reliability and the use of reliability in travel demand and simulation models.]]></description>
      <pubDate>Thu, 02 Aug 2018 14:56:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/1530094</guid>
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      <title>Pilot Testing of SHRP 2 Reliability Data and Analytical Products: Minnesota</title>
      <link>https://trid.trb.org/View/1471611</link>
      <description><![CDATA[The Reliability Program of the second Strategic Highway Research Program (SHRP 2) has developed a number of products to support estimating travel time reliability, identifying reliability deficiencies and contributing factors, identifying alternative solutions, and analyzing the impacts of these solutions. As part of the SHRP 2 program, tools have been developed to assess reliability based on a variety of approaches such as sketch planning, analytical analysis, simulation analysis, and travel time monitoring. SHRP 2 initiated the L38 project to pilot test products from five of the program’s completed projects. The products support reliability estimation and use based on data analyses, analytical techniques, and decision-making framework. The L38 project has two main objectives: (1) to assist agencies in using travel time reliability as a measure in their business practices and (2) to receive feedback from the project research teams on the applicability and usefulness of the products tested, along with their suggested possible refinements. SHRP 2 selected four teams from California, Minnesota, Florida, and Washington. This document reports on the activities performed as part of the Minnesota project (Project L38B).  The SHRP 2 reliability data and analytical tools evaluated by the Minnesota team are intended to address travel time variability in one of three ways: 1) establish monitoring systems to identify sources of unreliable travel times (Project L02); 2) identify potential solutions to cost-effectively improve reliability (Project L07); 3) incorporate consideration of travel time reliability into transportation agencies’ planning and programming framework (Project L05).   The Project L02 data and analytical tools were pilot tested by collecting vast amounts of traffic and nonrecurring conditions data and compiling it in a travel time reliability monitoring system (TTRMS) for purposes of conducting reliability evaluations. The L07 benefit-cost tool was evaluated in a number of facets, including usability, performance, and sensitivity testing. Guidance from Project L05 to incorporate reliability into the planning and programming process was introduced to a wide audience of stakeholders through an extensive outreach effort of meetings, workshops, and web conferences. Following the pilot testing technical work and outreach, MnDOT is committed to advancing reliability evaluation in its business practices.]]></description>
      <pubDate>Wed, 16 Aug 2017 11:23:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/1471611</guid>
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      <title>Pilot Testing of SHRP 2 Reliability Data and Analytical Products: Washington</title>
      <link>https://trid.trb.org/View/1471894</link>
      <description><![CDATA[In its Reliability focus area, the second Strategic Highway Research Program (SHRP 2) research emphasizes improving the reliability of highway travel time by reducing the frequencies and effects of events that cause travel time to fluctuate in an unpredictable manner. Washington State Department of Transportation (WSDOT), in association with the Smart Transportation Applications and Research Laboratory (STAR Lab) at the University of Washington (UW), is one of the four research teams conducting the pilot testing for project L38. This research project mainly tested and evaluated SHRP 2 Reliability Data and Analytical Products. These analytical tools are designed to use for travel time reliability measurement, monitoring, enhancement, and impact assessment.  The specific projects were:  Establishing Monitoring Programs for Travel Time Reliability (L02); Evaluation of Costs and Effectiveness of Highway Design Features to Improve Travel Time Reliability (L07); Incorporation of Nonrecurrent Congestion Factors into Highway Capacity Manual Methods (L08); Development of Improved Economic Analysis Tools (C11); Incorporating Reliability Performance Measures into the Transportation Planning and Programming Process (L05).  This research project has two major objectives: (1) to provide feedback to SHRP 2 on the applicability and usefulness of the reliability products tested; and (2) to assist agencies in moving reliability into their business practices through testing of the products developed by the five SHRP 2 Reliability projects. To test the SHRP 2 Reliability Data and Analytical Products, the research team employed a research procedure that consists of three major steps: (a) data compilation, integration, and quality control; (b) experiment design for testing different SHRP 2 products; and (c) test results evaluation and suggestions for possible improvements.  In summary, the SHRP 2 Reliability project products clearly are needed to address the practical challenges in travel time reliability monitoring and analysis that transportation agencies are facing. However, most tools require significant improvements to the application level. Details of the test data, test procedures, and test results are documented in this report.]]></description>
      <pubDate>Wed, 02 Aug 2017 08:51:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/1471894</guid>
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    <item>
      <title>Naturalistic Driving Study: Linking the Study Data to the Roadway Information Database</title>
      <link>https://trid.trb.org/View/1352917</link>
      <description><![CDATA[This report details the methodology used to link the second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS) data to the SHRP 2 Roadway Information Database (RID), the final critical step in completing the SHRP 2 Safety database. The NDS data set contains extensively detailed data collected continually from more than 5.5 million trips taken by the instrumented vehicles of 3,147 volunteer drivers in six sites. The RID contains extensively detailed data on 25,000 centerline miles of roadways in these six sites, less detailed data on 200,000 centerline miles of roadways in the six states in which the sites were located, and supplemental data on topics such as crash histories, travel volumes, construction, and weather in the six states. The true power of the NDS and the RID comes when they are linked—when each trip is matched to the roadway segments that were traveled and each roadway segment is matched to the trips that traveled on it. The matching methodology documented in this report uses as input the GPS position data collected once per second by the NDS instrumentation and the NAVTEQ network of road segments of all public roads in the continental United States over which the NDS vehicles could travel. The Matching Algorithm associates each GPS point of an NDS trip with the road segment on which a vehicle traveled. The principal challenges overcome by the algorithm were to accommodate GPS readings that may drift far from the correct roadway and to be operationally efficient in comparing the 3.7 billion GPS readings with the 2.6 million NAVTEQ road segments that were traversed. The algorithm’s results are stored in a very large table that associates trip timestamps with road segments.]]></description>
      <pubDate>Wed, 06 May 2015 09:36:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/1352917</guid>
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      <title>Value of Travel Time Reliability in Transportation Decision Making: Proof of Concept—Portland, Oregon, Metro</title>
      <link>https://trid.trb.org/View/1352905</link>
      <description><![CDATA[This report documents and presents a pilot approach at the regional planning level to develop, justify, apply, and assess the impact of travel time reliability as a dimension in the economic analyses that support project investment evaluations. The pilot approach had a multimodal focus that addressed both automobile and public transit trip reliability impacts. Background research was conducted to select the travel time reliability metrics for use in the pilot, as well as to establish an understanding of the value of reliability. The research results served as the basis to engage the regional stakeholder agencies in a collaborative exercise to establish a locally agreeable value of reliability as an input to analytical modeling application. The modeling framework involved replacing the common static traffic and transit assignment with a dynamic transportation network assignment for both traffic and transit. This network assignment could measure travel times and reliability for both modes to serve the development of generalized cost estimates and establish monetary values for the value of travel time and reliability. The model framework was applied to a single corridor, under baseline and future traffic conditions. The cost-benefit impacts of various competing improvement scenarios were assessed considering both travel time and travel time reliability. Business processes that compare operational alternatives with more traditional capital improvements were evaluated and ranked. The approaches, methods, and findings of this pilot application could be informative to other agencies and jurisdictions that have an interest in adopting and incorporating travel time reliability into their project development and programming decision-making process.]]></description>
      <pubDate>Wed, 06 May 2015 09:36:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/1352905</guid>
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    <item>
      <title>Naturalistic Driving Study: Collecting Data on Cell Phone Use</title>
      <link>https://trid.trb.org/View/1352916</link>
      <description><![CDATA[This report details the methodology used to acquire cell phone use records from a subset of participants in the SHRP 2 Naturalistic Driving Study (NDS) during the time when they were enrolled in the study. The cell phone records were then matched with participants’ NDS driving data to identify the times while they were driving when they may have been using their cell phones. These data will help researchers quickly and accurately identify events of interest in the NDS database related to cell phone use, from which researchers can learn crucial information about driver behavior and changes in crash risk when drivers choose to use or not to use cell phones. This report provides researchers with essential information about the Cell Phone Records Study (CPRS) data and how the data were collected. It describes the methods used to recruit and enroll CPRS participants, the collaboration with the cell phone carriers, and the processes of obtaining data from the carriers and directly from the participants. It also provides summary information on the data collected and discusses some limitations of the data.]]></description>
      <pubDate>Wed, 06 May 2015 09:36:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/1352916</guid>
    </item>
    <item>
      <title>Naturalistic Driving Study: Alcohol Sensor Performance</title>
      <link>https://trid.trb.org/View/1352909</link>
      <description><![CDATA[This report analyzes the performance of a passive alcohol sensor included in the head unit of the data acquisition system used in the Strategic Highway Research Program 2 (SHRP 2) Naturalistic Driving Study (NDS). Driver impairment is a critical issue in traffic safety, and the ability to identify alcohol-impaired drivers would be valuable for users of the NDS data. The sensor responds to the presence of alcohol in the cabin air. A positive sensor reading can come from many sources: alcohol from the breath of a driver or other occupant, an open container of an alcoholic beverage, aftershave lotion or perfume, windshield wiper fluid, and even some fast food. On the other hand, open windows may dissipate alcohol from an impaired driver’s breath before it reaches the sensor. Thus, the sensor can produce a positive reading when the driver is sober and can produce a negative reading for an alcohol-positive driver. The objective of this report is to evaluate the sensor performance under several scenarios with known driver alcohol levels and to investigate the feasibility of developing an algorithm to identify potentially alcohol-impaired drivers based on the sensor output.]]></description>
      <pubDate>Wed, 06 May 2015 09:36:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/1352909</guid>
    </item>
    <item>
      <title>Developing the "Guide for the Process of Managing Risk on Rapid Renewal Projects"</title>
      <link>https://trid.trb.org/View/1328417</link>
      <description><![CDATA[This report documents the development of Guide for the Process of Managing Risk on Rapid Renewal Projects, which is geared to manage risk during rapid renewal projects. The report and the associated guide and supporting products provide information and tools that transportation agencies can use to apply risk management principles systematically to their projects. They are specifically useful for projects that are below $500 million. The primary objectives of Strategic Highway Research Program 2 (SHRP 2) Renewal Project R09 were to address the general lack of understanding of risk and risk management options associated with the unique aspects of rapid renewal projects and to develop practical guidance and materials for the application of risk management methods to the rapid renewal project development process in a manner consistent with state transportation agency business practices. The products developed as part of this project include (1) a comprehensive guide, with checklists and an example application, and (2) associated implementation materials for conducting risk management on nonmajor rapid renewal projects, including a presentation introducing the risk management process and a Microsoft Excel template (with user’s guide) for both documenting the process and conducting the necessary analyses. The report, guide, and training materials provide the state of the practice for risk management on rapid renewal projects, as well as a detailed process of risk identification and mitigation strategies. The materials will be useful to state departments of transportation, municipal agencies, and consultants working on projects that involve accelerated project delivery and will make the risk management process more accessible for use as a standard project solution.]]></description>
      <pubDate>Mon, 23 Mar 2015 11:35:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/1328417</guid>
    </item>
    <item>
      <title>Naturalistic Driving Study: Descriptive Comparison of the Study Sample with National Data</title>
      <link>https://trid.trb.org/View/1344942</link>
      <description><![CDATA[This report provides a descriptive comparison of data from the Strategic Highway Research Program 2 (SHRP 2) Naturalistic Driving Study (NDS) sample and national data. The primary objective of the SHRP 2 NDS is to support analyses relating crash risk to driver, vehicle, roadway, and environmental characteristics. Since age is one of the most important driver characteristics, this objective is best supported by adequate sample sizes across all age groups. The national population of drivers has the greatest number of drivers in the middle age groups and progressively fewer in the younger and older ages. In contrast, the NDS oversampled younger and older drivers. In addition, the NDS oversampled newer-model-year vehicles because these vehicles provided useful data through their vehicle networks. It is important for users of the NDS data to have information on the relationship of the NDS sample to the national population. In general, many statistics taken directly from the NDS sample will not be nationally representative unless they are adjusted to account for relevant characteristics of the NDS sample.]]></description>
      <pubDate>Tue, 03 Mar 2015 12:55:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/1344942</guid>
    </item>
    <item>
      <title>Technologies to Support Storage, Retrieval, and Use of 3-D Utility Location Data</title>
      <link>https://trid.trb.org/View/1341400</link>
      <description><![CDATA[Highway renewal projects depend on the availability of accurate underground utility records and information to support effective planning, design, and delivery. Providing the necessary underground utility information for road renewal projects requires a suite of innovative nondestructive technologies and methods and a decision support framework that can address the complexity of varying utilities, site soil, geology, and environmental conditions. Strategic Highway Research Program 2 (SHRP 2) Project R01, Encouraging Innovation in Locating and Characterizing Underground Utilities, provided the background and basis for a series of research and development projects that aimed to provide products to serve this highway renewal business need. This report presents information on a project undertaken to use 3-D utility location information for planning, design, and construction of highway renewal work and to develop long-term storage of the 3-D utility location information for use on future projects. Information on current practices and protocols for addressing aboveground and belowground utility location data was assembled from a literature search and a review of a number of selected projects. These findings, together with input from a workshop, the expertise of the project team, and the knowledge of current commercially available technologies and software, were used to develop a framework of policies, procedures, and systems to store, retrieve, and use 3-D utility location data in highway renewal projects. Brief commentary is provided on conceptual workflows and software, system, and administrative needs. A 3-D utility location data repository was developed to support the project. The repository, developed on a standard relational database product, was based on the federal Spatial Data Standards for Facilities, Infrastructure, and Environment database (SDSFIE 3.0). For this project, the SDSFIE data model was modified and extended to add additional features, to provide a richer set of definitions, and to permit 3-D spatial features storage. The repository can be used to support planning, design, and construction activity, as well as to store 3-D utility location information for future highway renewal projects. A limited pilot demonstration using the 3-D utility location data repository and commonly available software tools is documented. The demonstration using right-of-way utility information from a state highway renewal project discusses the uploading, using, updating, and storing of 3-D utility location data.]]></description>
      <pubDate>Tue, 27 Jan 2015 11:23:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/1341400</guid>
    </item>
    <item>
      <title>Naturalistic Driving Study: Development of the Roadway Information Database</title>
      <link>https://trid.trb.org/View/1330446</link>
      <description><![CDATA[This report describes the development of the Roadway Information Database (RID), which  was carried out by Iowa State University’s Center for Transportation Research and Education  (CTRE). The objective of the RID is to provide high-quality roadway data that are linkable  to the Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS) database in order to incorporate roadway  characteristics into the analysis of the NDS data. The RID includes data from the SHRP 2  mobile data collection project (Safety Project S04B), other existing roadway data, and supplemental traffic operations data. Potential database users were identified to determine data and  usability requirements, and roadway data elements were identified and prioritized. A mobile  data collection vendor was selected using the 2008 SHRP 2 Safety Project S03 rodeo results  and additional evaluation. A quality assurance plan defined data accuracy requirements and  tolerances and outlined the handling of nonconforming data. Mobile data collection covered  about 12,500 centerline miles in the six NDS sites, or approximately 25,000 miles in both  travel directions. The final RID design is a simplified version of the revised UNETRANS  network data model. Data are referenced to a national base map for a consistent centerline  across the NDS sites, and users can employ dynamic segmentation to produce road segments  with any variable of interest.]]></description>
      <pubDate>Mon, 24 Nov 2014 15:28:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/1330446</guid>
    </item>
    <item>
      <title>Transferability of Activity-Based Model Parameters</title>
      <link>https://trid.trb.org/View/1330766</link>
      <description><![CDATA[The Strategic Highway Research Program 2 (SHRP 2) program developed proof-of-concept Dynamic Integrated Models in partnership with planning organizations in Sacramento, California, and Jacksonville, Florida. “Dynamic Integrated Model” refers to an activity-based travel demand model linked with a feedback loop to a Dynamic Traffic Assignment (simulation) model. The goal of that research was to improve urban-scale modeling and network procedures to address operations or spot improvements that affect travel-time choice, route choice, mode choice, reliability, or emissions. Building a new activity-based model set for transportation planning is an expensive and time-consuming commitment. The objective of this research was to determine if activity-based model parameters can be successfully transferred from one community to another. If transfer of parameters could be shown to produce reasonable results, it could save development time and money.  DaySim, an activity-based travel demand model originally developed in Sacramento, California, was applied to Jacksonville, Florida, with Sacramento parameters and then calibrated to the Jacksonville environment. DaySim was also applied to Tampa, Florida, with Sacramento parameters and then calibrated with local data. A statistical analysis was performed to identify significant differences between transferred parameters and parameters developed from local data. Variations in model performance on validation tests were also evaluated. The analyses identified specific model components that would be better transferred than re-estimated and others for which it would be better to re-estimate. A model with borrowed parameters must still be calibrated against local conditions. A significant finding of the research was that there must be a good match between the complexity of the source model to be transferred and the depth and coverage of data available for calibrating at the destination site. A second finding was that urban areas must be similar in key demographics such as household size, age, income, auto ownership, and trip purposes. This report will be of particular interest to planning organizations considering development of an activity-based travel demand model and, in general, to professionals who use travel demand models as part of the transportation planning process.]]></description>
      <pubDate>Fri, 14 Nov 2014 13:21:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/1330766</guid>
    </item>
    <item>
      <title>Innovations to Locate Stacked or Deep Utilities</title>
      <link>https://trid.trb.org/View/1330445</link>
      <description><![CDATA[This report documents research and development efforts to improve technologies for detecting, identifying, and mapping deeply buried utilities and utilities that are difficult to locate because of stacking in utility-congested areas. Five promising geophysical and mapping technologies were identified for review. For two of them—long-range RFID tags and active acoustic locating devices—prototypes were developed and field-tested. The prototype RFID tags showed some promise for development into a utility product. The active acoustic locating technology, however, did not perform well. This report will be of interest to engineers and businesses investigating the performance of technologies to improve the locatable zone for deeply buried or stacked utilities.]]></description>
      <pubDate>Thu, 13 Nov 2014 14:57:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/1330445</guid>
    </item>
    <item>
      <title>Guide to Using the Existing Pavement in Place and Achieving Long Life</title>
      <link>https://trid.trb.org/View/1330281</link>
      <description><![CDATA[On roadways that have acceptable geometric features, renewal can be greatly accelerated and costs reduced if the existing pavement can be incorporated into the new pavement structure. Transportation agencies need reliable procedures that allow them to identify when an existing pavement can successfully be used in place and how to incorporate it into the new pavement structure to achieve long life. This guide and the accompanying report and web tool provide guidance for selecting, designing, and constructing long-life pavements using existing pavement structure. The goal of this project was to develop reliable procedures and guidelines for identifying when existing pavements can be used in place and the methods necessary to incorporate the original material into the new pavement structure while achieving long life. “Long life” was defined as 50 years or longer from the time the pavement was renewed or rehabilitated until the next major rehabilitation. The report and guide encourage longer-lasting renewed pavement designs; provide realistic, easy-to-use pavement thickness scoping assessments; and guide users through the data-gathering process needed for input into designing and constructing a long-life pavement using the existing pavement structure. The guide includes the following: project assessment manual; best practices for rehabilitation of flexible pavements and rigid pavements; guide specifications; life-cycle cost analysis; and emerging pavement technology. All the guidance has been incorporated into the web-based pavement design scoping tool, which is meant to complement, not replace, a transportation agency’s normal processes for design and pavement-type selection.  As a result of outreach to transportation agencies, a set of enhancements is currently under way and will be included as a future addendum to the report and guide.]]></description>
      <pubDate>Thu, 13 Nov 2014 08:39:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/1330281</guid>
    </item>
    <item>
      <title>Dynamic, Integrated Model System: Sacramento-Area Application. Volume 1: Summary Report</title>
      <link>https://trid.trb.org/View/1329666</link>
      <description><![CDATA[This report will be of interest to professionals who use travel demand and network assignment models as part of the transportation planning process. The goal of this research was to improve urban-scale modeling and network procedures to address operations or spot improvement that affects travel-time choice, route choice, mode choice, reliability, or emissions. Such improvements may include traveler information, expanded transit service, pricing, reversible lanes, or improved bottlenecks. Operational improvements like these are difficult to model on an urban-area scale using existing tools. A secondary goal was to facilitate further development and deployment of these or similar procedures. The goals were addressed by building a proof-of-concept dynamic integrated model in two urban areas: Jacksonville, Florida, and Sacramento, California. The report describes the Sacramento, California, integration of the activity-based demand model DaySim; a dynamic traffic assignment (DTA) model, DynusT; and a transit network simulation model, FAST-TrIPs. All are open-source products. Integration means that a feedback loop was built between the demand and network assignment model systems. All of the demographic, highway network, and transit service data required to run the model set were assembled, and the feedback between the demand model and the DTA was tested in a subarea of Sacramento and on the full urban network. A Volume 2 report describes the application of DynusT and FAST-TrIPs in detail. A companion report and model set are available for the application in Jacksonville, Florida. This work has the same objective and uses DaySim as the demand model but uses TRANSIMS for the highway network assignment. Both model sets and software start-up guides are available from the Federal Highway Administration.]]></description>
      <pubDate>Mon, 03 Nov 2014 14:51:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/1329666</guid>
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