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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>Transport Research International Documentation (TRID)</title>
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    <item>
      <title>Understanding and Using New Data Sources to Address Urban and Metropolitan Freight Challenges (Website)</title>
      <link>https://trid.trb.org/View/1599914</link>
      <description><![CDATA[The rapid explosion of new freight data sources is creating significant opportunities for more effective and well-targeted planning and operation of roadways, particularly in urban and metropolitan areas. This research explored how new sources of freight data, including “big data” from smart cities initiatives, crowd-sourcing (e.g., via smartphones, vehicle fleet tracking), sensors (e.g., vehicle-to-infrastructure, vehicle-to-vehicle), and cameras are being or could be used to address urban and metropolitan freight challenges. The specific objectives of this research were four-fold: 1) Problem: Understand persistent urban and metropolitan freight challenges facing transportation agencies; 2) Opportunity: Identify and assess new and emerging freight data sources; 3) Applications: Assess the analytical approaches and techniques that could employ these new data sources to address freight challenges; and 4) Value Proposition: Recommend how agencies can leverage new data sources and analytics while overcoming practical and institutional challenges.  This website includes executive summaries of various aspects of freight data and case studies highlighting some uses of freight data.]]></description>
      <pubDate>Thu, 18 Apr 2019 09:47:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/1599914</guid>
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    <item>
      <title>Fusing mobile phone data with other data sources to generate input OD matrices for transport models</title>
      <link>https://trid.trb.org/View/1581163</link>
      <description><![CDATA[Origin to Destination (OD) matrices that describe mobility patterns provide major input to most transport analysis models. Since OD matrices are not yet directly observable, they are usually estimated indirectly. Data from new sources such as mobile phone records and Global Positioning System (GPS) traces from mobile apps are emerging alternatives that allow for cheaper and timely estimates. However, they are still hindered by weaknesses that must be studied for use as input to transportation models. This paper presents a case study using mobility data from mobile phone records, with the goal of establishing a methodology for validating the obtained OD matrices and generating the appropriate input for traffic assignment models. Spatial and temporal consistency of OD data elaborated from mobile records has been proved to be useful for transportation modelling needs.]]></description>
      <pubDate>Wed, 27 Mar 2019 12:47:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/1581163</guid>
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      <title>Highway Economic Requirements System: Safety Model Assessment</title>
      <link>https://trid.trb.org/View/1592086</link>
      <description><![CDATA[This report is presented in five sections. Section 1, Basic Issues in Predicting Crash Costs. discusses causes of highway accidents and crash modeling strategies. Section 2, HERS Crash Estimation Models, examines how the Highway Economic Requirements System (HERS) predicts crash cost benefits and reviews the HERS crash frequency models. Section 3, Highway and Crash Data Sources, looks at highway attributes and accident data sources. Section 4, Recent Research on Geometric Effects, explores previous, current, and future research on geometric factors in crashes. Section 5, Urban Two-Lane Streets, addresses the following: the current HERS crash model; preparing and cleansing Highway Safety Information System (HSIS) data; exploratory data analysis; modeling - variable definitions; and estimating Ohio crash counts.]]></description>
      <pubDate>Sun, 24 Mar 2019 20:11:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/1592086</guid>
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    <item>
      <title>Analysis of Address-Based Employment and Demographic Data Sources For Travel Modeling and Transportation Planning</title>
      <link>https://trid.trb.org/View/1589179</link>
      <description><![CDATA[Marketing support firms compile consumer and business data to provide business-to-business and business-to-consumer products. These firms sell the most up-to-date data to companies for marketing campaigns, through mailing, emailing, and other solicitations to potential customers. Two of the largest and most established providers of this type of data for the private sector, InfoGroup and Dun & Bradstreet, offer address-based employment and consumer (demographic) data to transportation planning agencies, which can supplement traditional sources of employment and demographic data like the U.S Census and the Quarterly Census of Employment from a state Department of Labor. These new data sources offer significant opportunities for travel modeling and transport analysis for the Vermont Agency of Transportation (VTrans) for the following types of analyses: Economic growth/impacts modeling and calculation; Disaggregate travel modeling calibration and validation; Accessibility calculations; and Vulnerable populations identification. However, the fact that there are only two known providers for this type of high-resolution data and that they do not have a long history of supporting transportation agencies presents some risk for the Agency. The goal of this project was to reduce that risk by assessing samples of the data and gathering information on its uses from the experience of others. The purpose of this project was to obtain samples of the data being offered from both vendors, to conduct an evaluation of its quality and accuracy for travel modeling and transportation analysis, and to solicit other users in the travel/transport modeling community for experiences with this type of data.]]></description>
      <pubDate>Sun, 17 Mar 2019 17:49:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/1589179</guid>
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    <item>
      <title>Workshop Synthesis: Surveys on long-distance travel and other rare events</title>
      <link>https://trid.trb.org/View/1567785</link>
      <description><![CDATA[This paper summarizes the findings from the workshop “Surveys on long-distance travel and other rare events”. The main objectives for this workshop were to discuss suitable definitions for long-distance passenger travel as well as to exchange knowledge and ideas about available and potential data sources, methods for data collection, and emerging research questions. Various interesting topics were identified for future research on long-distance travel. These include the combination of complementary and innovative data sources, new survey techniques, and addressing hindrances to survey participation. Strengthened interdisciplinary research is expected to reveal new valuable insight.]]></description>
      <pubDate>Sat, 24 Nov 2018 17:29:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/1567785</guid>
    </item>
    <item>
      <title>Workshop Synthesis: Use of social media, social networks and qualitative approaches as innovative ways to collect and enrich travel data</title>
      <link>https://trid.trb.org/View/1567749</link>
      <description><![CDATA[Transportation Planning and Analysis is living a new era in terms of new data sources. Traditional survey data are being enriched with information generated by the use of smartphones, smartcards, online social media and social networks, etc. However, these new data sources require the use of specific techniques to extract valuable information, and the use of inferring methods to be useful in the transportation context. This workshop reviewed advantages and drawbacks of innovative travel data sources related to the use of online Social Media and Social Networks.]]></description>
      <pubDate>Sat, 24 Nov 2018 17:29:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/1567749</guid>
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    <item>
      <title>Comparing road safety performance across countries: Do data source and type of mortality indicator matter?</title>
      <link>https://trid.trb.org/View/1549469</link>
      <description><![CDATA[This study examined the impact of data source estimates (World Health Organization (WHO) versus Global Burden of Disease (GBD)) and the type of mortality indicator (population-based versus exposure-based mortality) on road safety performance evaluation. Data were derived from WHO publications and the GBD results tool, and the authors calculated mortality rate ratio (MRR) and differences in country ranking between the two data sources, plus differences in country rankings and in mortality changes between 2010 and 2013 for population-based and vehicle-based mortality. Of 172 countries in both datasets, 32 countries (19%) had low consistency across the two data sources (MRR ≤ 0.49 or ≥1.51). Using population-based mortality data to rank the 172 countries, 77 (45%) had ≥ 20 position difference between the two data sources. Population-based vs. vehicle-based mortality data yielded ≥ 20 position difference in 33 countries for WHO estimates and 42 for GBD estimates. Among the 80 countries having comparable population-based and vehicle-based GBD mortality rates over time, 9 countries displayed opposite changing directions – that is, the change increased in one mortality indicator but decreased in the other indicator between 2010 and 2013. Data source and type of mortality indicators yield a substantial impact on ranking road safety performance across countries, as they are widely used for decision-making by global and national policy-makers and injury researchers. The differences between WHO and GBD estimates may arise from inconsistencies in data input and estimation models. Exposure-based indicators should be preferred in road safety evaluation when data are available. Advanced research is needed to interpret large country variations in road traffic mortality and mortality progress and to develop strategies to narrow the gaps across countries.]]></description>
      <pubDate>Wed, 24 Oct 2018 11:17:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/1549469</guid>
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    <item>
      <title>Integrating Emerging Data Sources into Operational Practice: State of the Practice Review</title>
      <link>https://trid.trb.org/View/1540960</link>
      <description><![CDATA[The purpose of this report is provide agencies responsible for Transportation Systems Management and Operations (TSM&O) with an introduction to successful Big Data tools and technologies that can be used to aggregate, store, and analyze new forms of traveler-related data that may be useful for operations. While traditional sources of transportation data for TSM&O will remain, emerging data sources, largely those from Connected Travelers, Connected Vehicles, and Connected Infrastructure, will represent a significant opportunity for Departments of Transportation (DOTs) and localities to improve TSM&O practices. In addition, this report will identify ways these collection, storage, and analytics practices can be integrated into the next generation of transportation management systems. Big data techniques outside of the transportation field were considered, to identify practices that may be useful within the transportation field. The first chapter reviews the common functions of TSM&O and provides a state of the practice summary of how data and information currently are acquired, processed, stored, and analyzed. The second chapter identifies emerging data sources from connected vehicles (CV), connected travelers, and other sources relevant to TSM&O and predicts the point(s) of access of these data to a DOT. Chapter 3 then characterizes each of the emerging sources by current and future volume and data velocity (the rate at which data is generated and the rate at which the data accumulates over time). The future data volumes are assessed at a national scale and at the scale of a “typical” agency. The data volumes for the national level are computed and presented only to assess the sheer scale. Chapter 4 provides an overview of Big Data tools and technologies. Chapter 5 then introduces the reader to the popular and common platforms for ingesting, processing, and analyzing large volumes of information. Lastly, chapter 6 introduces cost models for commercial tools and platforms.]]></description>
      <pubDate>Wed, 10 Oct 2018 21:42:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/1540960</guid>
    </item>
    <item>
      <title>Understanding the Causative, Precipitating, and Predisposing Factors in Rural Two-Lane Crashes</title>
      <link>https://trid.trb.org/View/1526260</link>
      <description><![CDATA[The overall objectives of this study were to (1) identify and explore alternative safety data sources and analysis perspectives and (2) demonstrate the potential utility of these alternative approaches in increasing understanding of precipitating events and predisposing factors for crashes occurring on horizontal curves and at unsignalized intersections along rural two-lane roads. Generalized conceptual crash model frameworks were developed, informed by a review of supporting published literature on conceptual crash models and contributing factors, alternative approaches to accident analysis, and the role of constraints in systemic approaches to accident analysis. The frameworks proved useful from several perspectives, including (1) identifying and organizing all factors that influence the likelihood of a crash and defining the event sequences that lead to a crash, (2) providing terminology that will encourage clear communication across accident analysis disciplines as research on crash causation continues, (3) visualizing the nature by which a certain factor influences the likelihood of a crash or by which an event directly causes a crash, and (4) identifying data needs (versus data availability) for studying the precipitating events, system constraints, predisposing factors, and target groups associated with a specific crash type. After marrying the conceptual crash model framework with available data, a study was conducted to determine whether crash causal types, or similar crashes grouped together based on their key precipitating events, could be developed from data, photographs, and narratives developed from detailed, on-scene crash investigations available in the National Motor Vehicle Crash Causation Survey. This was followed by a set of three additional studies primarily focused on alternative safety data sources and analysis perspectives related to predisposing factors. Enhanced data collection and subsequent analysis were demonstrated for three high-priority crash scenarios on rural two-lane roads: “straight crossing path crashes” at unsignalized intersections, combination “control loss/no vehicle action” and “road edge departure/no maneuver” single-vehicle crashes on horizontal curves, and “opposite direction/no maneuver crashes” on horizontal curves. Findings demonstrate that expanding beyond traditional databases used for crash-based evaluations can provide further insight into these crashes. One follow-on analysis in the final part of the study indicated that the alternative approaches to estimating disaggregate measures of exposure, kriging, and quasi-induced demand techniques show some promise and should be considered in future research.]]></description>
      <pubDate>Sat, 28 Jul 2018 17:02:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/1526260</guid>
    </item>
    <item>
      <title>Integrating Emerging Data Sources into Operational Practice—Opportunities for Integration of Emerging Data for Traffic Management and TMCs</title>
      <link>https://trid.trb.org/View/1514098</link>
      <description><![CDATA[With the emergence of data generated from connected vehicles, connected travelers, and connected infrastructure, the capabilities of traffic management systems or centers (TMCs) will need to be improved to allow agencies to compile and benefit from using this information. New capabilities will be needed for data acquisition, communications bandwidth from the roadside to the TMC, new computing hardware, software, data storage and management systems, decision support subsystems, and data sharing and dissemination systems. The magnitude of what capabilities will be needed by individual TMCs, agencies and service providers will vary across regions. Regardless of the size of the traffic management system and TMC capabilities, the big data tools and technologies and systems are likely quite similar. The purpose of this report is to: Identify how big data tools and technologies can be used in traffic management systems or TMCs; Develop potential use cases for integrating big data technology and tools into traffic management systems or TMCs; Assess how connected vehicle and traveler related data could be used to enhance the operation of traffic management systems or TMCs; Analyze how the sharing of data with other TMCs, systems, connected vehicles and travelers; and agency business processes or systems could impact the performance of a traffic management system or TMC; and Identify the challenges and options to consider to compile, use and share this data.]]></description>
      <pubDate>Mon, 04 Jun 2018 16:57:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/1514098</guid>
    </item>
    <item>
      <title>Feasibility of Using Police Crash Records as a Potential Data Source for Acquisition and Continued Updates of Posted Speed Limits</title>
      <link>https://trid.trb.org/View/1495628</link>
      <description><![CDATA[The Federal Highway Administration (FHWA) encourages states to collect and maintain the 200 roadway and traffic elements recommended by Model Inventory of Roadway Elements (MIRE). States are therefore looking for ways to systematically collect and update data for these variables. One potential source of data that is readily available and rich in roadway information is the police crash records. Law enforcement officers at the crash site often record a wealth of roadway characteristics information such as posted speed limit, number of lanes, etc. This paper describes a study that explores the feasibility of using police crash records both to acquire posted speed limits and to detect their changes for continued data updates. A two-step process was developed. The first step extracts posted speed limits for road segments that have at least one crash and the second step applies spatial analysis to estimate posted speed limits for road segments that do not have a crash. Application of both steps resulted in 39.4% of the total road segments being populated with posted speed limits. Verification using a well-maintained state roadway database showed that the posted speed limits match 62.7% of the time. It was found that this level of accuracy was sufficient for detecting potential changes in posted speed limits for the purpose of continual data updates, but not for acquiring posted speed limits for which a higher accuracy would be needed. This method could be applied to acquire and detect changes for other variables such as number of lanes, and could reduce the need for resource-intensive field data collection efforts.]]></description>
      <pubDate>Mon, 26 Mar 2018 14:31:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/1495628</guid>
    </item>
    <item>
      <title>Reconstruction of Western Europe’s Premotorway Road Network Back to 1500: Data Sources, Historical Information Processing, and First Application</title>
      <link>https://trid.trb.org/View/1496631</link>
      <description><![CDATA[This paper presents the construction of a new historical road network data set, capturing conditions between 1500 and 1900, focusing on the 19th and 18th 3 century. It continuous the work presented at the Transportation Research Board (TRB) in 2015 on Reconstruction of 1950s Global Road Network Using American Army Maps. It covers Western Europe. The main part of this paper reports in detail the heuristics used, which were established based on an assessment of 900 or so collected historical transport related documents, mostly transport maps. Switzerland in 1850 is employed as an example of a geographical information system used to generate historical travel times. As far as the authors can ascertain, such comprehensive and detailed historical transport data has not been available until this point, but it enables examination of various historical and current transport-related research questions using spatial and time-variant data. The purpose of this paper is: first, to report on the reconstruction of these historical road network data and to position it in the existing literature and research tradition; second, to explain how the network is generated; third, to present the authors' approach to model historical generalized travel costs; fourth, show how to use such information by applying it in a Swiss case study.]]></description>
      <pubDate>Tue, 20 Feb 2018 09:30:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/1496631</guid>
    </item>
    <item>
      <title>Synthetic Household Travel Data Using Consumer and Mobile Phone Data</title>
      <link>https://trid.trb.org/View/1480014</link>
      <description><![CDATA[This research develops a method that fuses consumer marketing data with anonymous, passive location data to create synthetic populations with individual-level synthetic travel diaries. The travel diaries detail each person’s travel and activities at locations in a timeline format.  This low cost synthetic data could give entities up-to-date, detailed data that match the population’s short-term and long-term movements. The research team previously built an initial implementation of the data fusion process in Atlanta, Georgia.  This IDEA project proposed building a synthetic household travel dataset for a different city that had a larger study area in land and population size. The four-county planning region of the Puget Sound Regional Council in metropolitan Seattle, Washington was selected.  The project aimed to test the transferability of the concept, the scalability of the method to larger study areas, and the suitability of the specific implementation method chosen in the initial study.  The research focused on developing a process that will be consistent nationally, rapidly deployable for any size city, and systematically updateable over regular time periods.  The first stage of the research effort refactored the methodology from the initial study in Atlanta so that the same code would build a synthetic population and travel diaries in the larger metro region of Atlanta and in metro Seattle.  The required “big” data were obtained for metro Seattle and the synthetic travel diaries were built. The second phase of the research effort validated the resulting synthetic travel diaries for both Atlanta and Seattle against external sources.  It also checked for internal consistencies with the passive data that were fed into the synthesizing method.  Based on implementations in Seattle, Atlanta, and Asheville, North Carolina, and on the comments by the project's expert panel, it was concluded that this method would be most useful, as it is functioning right now, in small-and medium-sized regions for planning.  For large regions that have  invested in large household travel survey collection programs and sophisticated activity-based models (ABMs), more research is needed to merge a data- driven approach into existing activity- based models.  For state departments of transportation, this data-driven approach lessens (or removes) the need for a statewide household travel survey program, standardizes travel models in use without time-intensive local calibration, and standardizes analysis of projects for transportation improvement programs within the state. For regions interested in analyzing the impact of autonomous vehicles (AVs), this method can  be combined  with open-source MATSim to rapidly analyze short–term responses to AVs assuming  shared fleets, privately owned fleets, or a mix of the two.]]></description>
      <pubDate>Fri, 11 Aug 2017 14:00:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/1480014</guid>
    </item>
    <item>
      <title>Innovations in Freight Data</title>
      <link>https://trid.trb.org/View/1479139</link>
      <description><![CDATA[Emerging “big” freight data have the potential to significantly improve freight planning, freight operations and mobility, and visualization of freight data. The Transportation Research Board’s Standing Committee on Freight Transportation Data and Task Force on Understanding Big Data in Freight Transportation initiated a workshop to bring together freight data users and decision makers to learn about and share the latest applications that leverage emerging big freight data sources. This event brought together traditional freight-planning stakeholders with data and technology innovators from related areas to explore opportunities to advance the state of the practice.  The event brought together agencies, consultants, industry experts, and academic researchers who have developed innovative data applications. It consisted of a variety of sessions, including a keynote opening speech, focused panels, speed presentations, an interactive demonstration session, and a field presentation of an instrumented truck. Two award winners were recognized for “best application of a new data source” and “best data fusion application.” In breakout sessions at the end of the workshop, attendees reviewed the events of the workshop to identify what real advances have been made, and what critical gaps remain to be addressed through research, strategic partnerships, or other means.  This e-circular serves as a record of workshop events and findings, and as an information source for freight data stakeholders.]]></description>
      <pubDate>Thu, 03 Aug 2017 11:54:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/1479139</guid>
    </item>
    <item>
      <title>Understanding Travel Behavior: How Good Are Our Data Sources for Addressing Travel Behavior Information Needs?</title>
      <link>https://trid.trb.org/View/1439545</link>
      <description><![CDATA[Recent travel behavior trends in the United States reveal significant and unprecedented shifts. While factors causing these shifts are probably abundant and diverse in nature, it is alarming that these changes were not a priori forecasted. Even more troubling, these significant shifts are continue to occur with no considerable improvement in our ability to forecast these changes. However, it has become clear that we have a dire need to identify and develop new sources of travel information to improve our ability to understand and forecast recent travel behavior trends. Naturally, different transportation agencies have different information needs and every agency is interested in identifying the best methods suitable for addressing their specific set of information needs. Typically, a transportation agency relies on one of two tools in their toolbox: transportation data or travel models. In general, this paper intends to provide transportation agencies with additional tools (relating to both models and data) that could enable them identify more efficient means for addressing their specific needs. This work develops and presents a methodology to demonstrate how different and diverse data sources could be evaluated and ranked to answer travel behavior information needs. The paper presents and implements a Multi Attribute Decision Making (MADM) model to evaluate and rank 7 data sources (NHTS, ACS, Local Surveys, ATUS, AHS, Airsage, and Omnibus Surveys) to address 8 specific data needs. In addition, to examine the robustness of the developed methodology, 5 different versions of the MADM model are implemented, and a sensitivity analysis is performed. The results demonstrate the potential value of the developed methodology.]]></description>
      <pubDate>Tue, 07 Mar 2017 10:25:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/1439545</guid>
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