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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>Research in Progress (RIP)</title>
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      <title>Crime Prevention for Truckers</title>
      <link>https://rip.trb.org/View/1497909</link>
      <description><![CDATA[In this project, the research team will collect data on the prevalence of crimes involving threats and assaults against minority and female truckers. In addition to conducting a literature review, the research team will survey a sample of the truck driver population, gathering data on how many female/minority drivers have been threatened or assaulted; the nature of the offenses (e.g., verbal threat, physical assault, etc.); times and locations of the incidents (e.g., day versus night; truck terminal, shipping dock, fueling station, etc.); and characteristics of both the perpetrators and the victims. The survey will also ask whether victims have reported the incidents to law enforcement officials, and if they have not reported the incidents, the survey will ascertain the reasons why. After the survey has been implemented and all data have been collected, the research team will analyze the data and identify any trends. Findings will be summarized in a final report.]]></description>
      <pubDate>Wed, 17 Jan 2018 09:57:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/1497909</guid>
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    <item>
      <title>Data-Driven Highway Infrastructure Resilience Assessment - Phase II</title>
      <link>https://rip.trb.org/View/1371651</link>
      <description><![CDATA[The transportation systems sector, one of the most critical infrastructure sectors in the US, has a subsector of highway and motor carrier industries that supports daily activities and emergency actions by providing services to other critical infrastructure segments such as healthcare and public health, emergency services, manufacturing, food and agriculture, etc. However, transportation networks face risks from natural and human-made events such as hurricanes, tsunamis, earthquakes, bridge collapse, and terrorist attacks. Thus, to improve the reliability of the components in interconnecting networks, it is necessary to consider these unpredictable failures in the network design. Resilient network design ensures that the network functionality is at an acceptable level of service in the presence of all probabilistic failures.

In this study, the authors addressed uncertainty in a transportation network by proposing a trilevel optimization model, which improves the resiliency of the network against uncertain disruptions. The link capacities are uncertain parameters and the origin-destination demands are deterministic. The goal was to minimize the total travel time under uncertain disruptions by designing a resilient transportation network. The trilevel optimization model has three levels. The lower level determines the network flow, the middle level assesses the resiliency of the network by identifying the worst-case scenario disruptions that could lead to a maximal travel time, and the upper level uses the system perspective to expand the existing transportation network to enhance the network’s resiliency. In addition, the authors propose a new formulation for the network flow problem that will significantly reduce the number the number of variables and constraints.

The results of solving the trilevel optimization model can improve the resiliency of the network. However, this study was subject to some limitations, which suggested future research directions. In reality, transportation demands are not consistent, but the proposed model considers origin-destination demands as deterministic parameters. Relaxing this assumption requires a more complicated model to reflect uncertain demands. Other possible future work would be designing an exact algorithm to find the optimal solution.]]></description>
      <pubDate>Tue, 13 Oct 2015 14:07:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/1371651</guid>
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    <item>
      <title>Mitigate Threats Through Space Environment Modeling/Prediction</title>
      <link>https://rip.trb.org/View/1362683</link>
      <description><![CDATA[No summary provided.]]></description>
      <pubDate>Thu, 23 Jul 2015 01:00:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/1362683</guid>
    </item>
    <item>
      <title>Mitigate Threats through Space Environment Modeling/Prediction Including Micrometeoroid and Orbital Debris (MMOD)</title>
      <link>https://rip.trb.org/View/1362682</link>
      <description><![CDATA[No summary provided.]]></description>
      <pubDate>Thu, 23 Jul 2015 01:00:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/1362682</guid>
    </item>
    <item>
      <title>Data Driven Highway Infrastructure Resilience Assessment</title>
      <link>https://rip.trb.org/View/1350449</link>
      <description><![CDATA[The transportation systems sector, one of the most critical infrastructure sectors in the US, has a subsector of highway and motor carrier industries that supports daily activities and emergency actions by providing services to other critical infrastructure segments such as healthcare and public health, emergency services, manufacturing, food and agriculture, etc. However, transportation networks face risks from natural and human-made events such as hurricanes, tsunamis, earthquakes, bridge collapse, and terrorist attacks. Thus, to improve the reliability of the components in interconnecting networks, it is necessary to consider these unpredictable failures in the network design. Resilient network design ensures that the network functionality is at an acceptable level of service in the presence of all probabilistic failures.

In this study, the authors addressed uncertainty in a transportation network by proposing a trilevel optimization model, which improves the resiliency of the network against uncertain disruptions. The link capacities are uncertain parameters and the origin-destination demands are deterministic. The goal was to minimize the total travel time under uncertain disruptions by designing a resilient transportation network. The trilevel optimization model has three levels. The lower level determines the network flow, the middle level assesses the resiliency of the network by identifying the worst-case scenario disruptions that could lead to a maximal travel time, and the upper level uses the system perspective to expand the existing transportation network to enhance the network’s resiliency. In addition, the authors propose a new formulation for the network flow problem that will significantly reduce the number the number of variables and constraints.

The results of solving the trilevel optimization model can improve the resiliency of the network. However, this study was subject to some limitations, which suggested future research directions. In reality, transportation demands are not consistent, but the proposed model considers origin-destination demands as deterministic parameters. Relaxing this assumption requires a more complicated model to reflect uncertain demands. Other possible future work would be designing an exact algorithm to find the optimal solution.]]></description>
      <pubDate>Wed, 15 Apr 2015 01:01:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/1350449</guid>
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    <item>
      <title>Consolidated Research Program, Right of Way Automated Monitoring Threat Prevention</title>
      <link>https://rip.trb.org/View/1261701</link>
      <description><![CDATA[The objective of this research project is to develop a new generation of surveillance management systems by delivering a step-change improvement in the proactive prevention of threats to pipelines.  Technologies developed will be used for the detection, identification, and communication of threats and vulnerabilities to underground pipeline infrastructure.  The program will develop an integrated, autonomous sensor/detector system for near real-time automated detection, identification, and notification of threats and leaks.]]></description>
      <pubDate>Tue, 10 Sep 2013 01:01:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/1261701</guid>
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