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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>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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      <title>Adaptive Environmental Interfaces: Biomimetic Morphologies and Tactical Urbanism</title>
      <link>https://trid.trb.org/View/1396005</link>
      <description><![CDATA[Human social needs, once facilitated by cities are now satisfied through the internet of everything (IoE) and the internet of me (IoMe). Historically, centralized power structures have shaped urban environments to support human interaction. These interactions are now also satisfied through the distributed power structures of the internet; undermining the built environment. Forces of sustainability combined with the grafting of technology onto bodies and buildings are re-shaping urban development – catalyzing the emergence of new urban morphologies. Here stochastic and biomimetic research methods and modeling generate new morphological urban forms. Opportunities are revealed through IoE sensory information and infrastructural systems’ analysis.]]></description>
      <pubDate>Tue, 23 Feb 2016 15:27:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/1396005</guid>
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      <title>Examining Differences and Commonalities of Life Cycle Stages in Daily Contacts and Activity-Travel Time Allocation</title>
      <link>https://trid.trb.org/View/1336939</link>
      <description><![CDATA[In this paper, the authors employ structural regression models to identify the differences and commonalities among life cycle stages in daily interpersonal contacts and their activity-travel time allocation. The authors use data from a two-day geocoded time use survey that enables the creation of variables depicting daily contacts with family, friends, schoolmates, co-workers, clubmates, and others. The daily contacts, duration of activities by type, and travel time are the endogenous variables of the system. Life-cycle stages, day of the week, and a home-based accessibility indicator are used as exogenous variables. The model identifies a variety of life-cycle dependent roles of interpersonal interactions in activity-travel behavior. Intra-household contacts and friends play important roles in explaining service, shopping, home-leisure, and out of home social activity duration. Moreover, many significant indirect influences are found with this model, demonstrating the necessity of incorporating human interactions as well as the paths of influence among daily activity durations. The authors also compare the direct and indirect influence between two models, one that includes number of daily contacts and a second model without daily contacts. Three groups are found as the most sensitive in allocation of time when controlling for daily contacts and they are children younger than 17 years, persons in home duties, and college students. In addition, allocation of time to discretionary activities is the most sensitive to specifications that control for number of daily contacts.]]></description>
      <pubDate>Thu, 05 Mar 2015 17:48:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/1336939</guid>
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      <title>Modeling Multiple Human-Automation Distributed Systems using Network-form Games</title>
      <link>https://trid.trb.org/View/1218693</link>
      <description><![CDATA[The focus of the modeling framework is on interactions between human and automation agents in large, distributed agent networks/systems. This model combines Bayes nets with Game Theoretic methods to model human behavior and predict the behavior of a composite system involving  humans and automation. In general, some of the nodes of the Bayes net will be set by the humans in the system, some will be set with known conditional distributions (e.g., noise models of sensors), and some might be “black boxes” provided by the proposer that simulate behavior of automated devices. Novel algorithms are required for sampling and prediction with this model.]]></description>
      <pubDate>Thu, 15 Nov 2012 14:01:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/1218693</guid>
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    <item>
      <title>Homicide Rate as a Predictor of Traffic Fatality Rate</title>
      <link>https://trid.trb.org/View/907098</link>
      <description><![CDATA[In the United States, traffic fatality rates per distance driven vary greatly from state to state, with the maximum rate being 2.9 times the minimum rate. This study was designed to examine factors associated with this variability. A multiple regression was performed on the 2006 state data. The dependent variable was the fatality rate per distance driven. There were 10 independent variables. The analysis identified seven statistically significant factors: homicide rate per capita (used in the analysis as a proxy for aggression), physicians per capita, safety-belt usage rate, proportion of male drivers, proportion of drivers over 64 years of age, income per capita, and deaths caused by alcohol-related liver failures per capita (a proxy for the extent of intoxicated driving). These seven factors accounted for 71 percent of the variance in the traffic fatality rates. The strongest predictor of the traffic fatality rate was the homicide rate. This finding suggests that social aspects of human interaction may play an important role in traffic safety.]]></description>
      <pubDate>Mon, 25 Jan 2010 08:08:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/907098</guid>
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      <title>Child-Parent Interaction in Relation to Road Safety Education: Part 2 – Main Report</title>
      <link>https://trid.trb.org/View/879912</link>
      <description><![CDATA[Children and young people are particularly vulnerable road users. Child pedestrian injury rates are poor in England compared with the rest of Europe. The main aim of this study was to explore the way parents influence children and young people aged 0–16 years to be safer road users. This study included children and young people aged 5–16 and parents of children aged 0–16 years old. In order to explore child–parent interaction in relation to road safety education, multiple research methods were used. It was found that parents influenced their children’s road use behaviour by the way they exercised control, the role model they presented and other types of behaviour to increase skill development, understanding, motivation and a positive attitude to road safety.]]></description>
      <pubDate>Tue, 17 Feb 2009 12:30:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/879912</guid>
    </item>
    <item>
      <title>Advances in Travel Behavior Analysis 2007</title>
      <link>https://trid.trb.org/View/848954</link>
      <description><![CDATA[This collection of 14 papers addresses advances in travel behavior analysis.  Specific topics discussed include intrahousehold interaction analysis; agent-oriented coupling of activity-based demand generation with multiagent traffic simulation; modeling adults' weekend day-time use; human interaction spaces under uncertainty; analysis of children's daily time-use and activity patterns;  stated adaptation survey of activity rescheduling; recurrence of daily travel patterns; interactions between residential relocations, life course events, and daily commute distances; capturing human activity spaces; identifying skeletal information of activity patterns; successfully changing individual travel behavior; mobility management in Japan; who chooses to carpool and why; and commuter parking versus transit-oriented development.]]></description>
      <pubDate>Wed, 30 Jan 2008 15:47:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/848954</guid>
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    <item>
      <title>A Review of Human-Automation Interaction and Lessons Learned</title>
      <link>https://trid.trb.org/View/815494</link>
      <description><![CDATA[This report reviews 37 accidents in aviation, other vehicles, process control and other complex systems where human-automation interaction is involved.  Implications about causality with respect to design, procedures, management and training are drawn.  A number of caveats and recommendations from the salient literature are discussed with regard to human-automation interaction.]]></description>
      <pubDate>Fri, 21 Sep 2007 13:55:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/815494</guid>
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    <item>
      <title>Final Report and Recommendations for Research on Human-Automation Interaction in the Next Generation Air Transportation System</title>
      <link>https://trid.trb.org/View/815489</link>
      <description><![CDATA[This is the final report of an 18-month project to: (1) review Next Generation Air Transportation System (NGATS) Joint Planning and Development Office (JPDO) documents as they pertain to human-automation interaction; (2) review past system failures in aviation and other contexts involving human-automation interaction; (3) conduct a workshop of JPDO, NASA and academic experts in the area; (4) perform analyses of selected problems; and (5) make recommendations for National Aeronautics and Space Administration (NASA) research that is needed to support JPDO on these aspects of NGATS.  This report first reviews reports issued separately on the failures review and the workshop findings as well as several papers and technical notes.  Recommendations for needed research in human-automation interaction are then detailed.]]></description>
      <pubDate>Fri, 21 Sep 2007 13:55:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/815489</guid>
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    <item>
      <title>Human Interaction Spaces Under Uncertainty</title>
      <link>https://trid.trb.org/View/800940</link>
      <description><![CDATA[Rigid activity schedules and pressure of time can strongly influence the determination of a meeting place and time. When several uncertain determinants need to be taken into account, joint activity planning becomes a complex issue. In this respect, a cross-pollination of Hägerstrand’s time geography and Pawlak’s rough set theory yields a fruitful foundation for analyzing multiple agents’ travel spaces under uncertainty. This paper reports on an attempt to analyze the effects of uncertain spatiotemporal settings on the determination of interaction spaces. The aim is to provide a better understanding of the uncertainty component of constraints to support agents pointing out a feasible meeting place while respecting individuals’ fixed activity programs. The concept of rough space–time prisms and their effect on current space–time accessibility measures is presented. The paper focuses on three types of uncertainty: temporal, spatial, and speed.]]></description>
      <pubDate>Wed, 02 May 2007 13:02:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/800940</guid>
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    <item>
      <title>Theoretical and Conceptual Frameworks for Studying Agent Interaction and Choice Revelation in Transportation Studies</title>
      <link>https://trid.trb.org/View/804841</link>
      <description><![CDATA[The field of literature studying community, business, and household agent interactions, particularly in economics, psychology, and marketing, is growing.  The primary focus of this literature is how the preferences and choices of one agent influence others, whether specific or general, whether individuals, organizations, or influences.  A number of paradigms associated with experimental economics-, game theory-, and case or rule-agent-based model literature center on understanding and explaining how agent preferences influence others.  The authors promote interactive agency (stated) choice experiments extending traditional single-agent stated preference experiments to find how behavioral interactions among agents in a revise or confirm cycle (choose-feedback-review-choose) develop preferences. The article discusses agent preference identification methods in an environment that introduces transportation modelers to agent interactions.]]></description>
      <pubDate>Fri, 30 Mar 2007 07:48:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/804841</guid>
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      <title>MODES IN HUMAN--MACHINE SYSTEMS: CONSTRUCTS, REPRESENTATION, AND CLASSIFICATION</title>
      <link>https://trid.trb.org/View/656717</link>
      <description><![CDATA[This paper surveys and discusses human interaction with automated control systems that employ modes. The authors focus the discussion on those features of the control system that lead to mode confusion and error. They first provide working definitions of the term mode and discuss key constructs that contribute to mode error, such as mode ambiguity and user expectations. After this, the discussion proceeds to human interaction with automated control systems in general, and cockpit automation in particular. A modeling language, known as Statecharts that is used by system engineers to specify complex control systems, is introduced to provide a formal description of human interaction with such control systems. The Statecharts language is used to describe the 3 types of modes that are commonly found in modern control systems: interface, functional, and supervisory. Examples from cockpit automation are used to illustrate each mode type. The paper concludes with a brief discussion of the links between the mode constructs and formal representation of human interaction with control systems.]]></description>
      <pubDate>Mon, 26 Jun 2000 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/656717</guid>
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    <item>
      <title>ANALYSIS OF DRIVER SAFETY PERFORMANCE USING SAFETY STATE MODEL</title>
      <link>https://trid.trb.org/View/451830</link>
      <description><![CDATA[A significant component in the pursuit of safety is estimation of risk probability.  In transportation systems virtually all safety-related events and outcomes involve an intermediate event known as an accident.  The safety state model is a probabilistic model that is used to estimate the probability of an accident as a function of the human-machine system state.  By using a discrete Markov network, the safety state model forms a framework for capturing the human-machine and human-human interactions in a transportation system.  The observed data are used to calibrate the model, which is subsequently used to estimate the risk probability performance of other human operators.  The theoretical development of this model is reviewed.  In addition, motivation and background, as well as advantages and disadvantages with respect to existing quantitative methods of risk probability estimation, are discussed.  Finally, the applicability to driver performance analysis is discussed.]]></description>
      <pubDate>Mon, 13 Nov 1995 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/451830</guid>
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