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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>Analyzing Pre- and Post-Coastal Hazard Pavement Conditions to Optimize Response Strategies for Coastal Infrastructure Resilience</title>
      <link>https://rip.trb.org/View/2427611</link>
      <description><![CDATA[Texas' coastal region stretches over 367 miles along the Gulf of Mexico which is a significant ecological and economic zone encompassing beaches, marshes, estuaries, and barrier islands. This area supports a vibrant tourism industry, international trade, commercial fishing, and energy production, with major ports such as Houston, Corpus Christi, and Galveston playing vital roles. However, Texas' coastline faces increasing risks from natural hazards, necessitating efficient and effective infrastructure response strategies to mitigate impacts and ensure rapid recovery. This research aims to investigate the effects of coastal hazards on pavement conditions and to use network analysis for optimizing pavement infrastructure response, maintenance decisions, and treatment allocation to support coastal communities. The study focuses on Houston which is a key urban center exposed to frequent coastal hazards.  Hurricane Harvey was selected as a case study for in-depth analysis.

Initially, the research team will conduct a comprehensive literature review of existing studies focusing on methods used for evaluating pavement conditions before and after coastal hazards. This review aims to identify best practices and effective methodologies for enhancing pavement durability and performance. Following this, the team will analyze historical pavement condition data from Houston before Hurricane Harvey, focusing on different pavement types (ACP, CRCP, JCP) and utilizing statistical models to understand data variability and characteristics. Subsequently, the team will analyze pavement conditions in Houston following Hurricane Harvey. This analysis will involve comparing pre- and post-Harvey data to assess the impact on pavement performance. Statistical methods will be applied to evaluate distress distribution and severity of pavements. Additionally, the research will evaluate the effectiveness of pavement condition analysis models for better maintenance prioritization post-coastal hazards. This step aims to understand how maintenance strategies evolve post-disaster and to enhance decision-making for maintenance planning. The final phase of the research focuses on developing tailored strategies for improving infrastructure durability. This involves reviewing existing strategies including customizing them for Texas' coastal context and assessing their effectiveness through scenario analysis. The expected outcome of this research is to provide valuable insights into pre- and post-coastal hazard pavement conditions in Houston. By leveraging network analysis models, the study aims to inform maintenance decisions that prioritize efficient response measures. The findings will contribute to developing strategies that enhance durability for coastal pavement infrastructure.
]]></description>
      <pubDate>Thu, 12 Sep 2024 15:33:49 GMT</pubDate>
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      <title>Detection and Estimation of Inundation and Associated Risks using Traffic Monitoring Cameras and High-Resolution Flood Maps</title>
      <link>https://rip.trb.org/View/1644426</link>
      <description><![CDATA[During extreme flooding such as Hurricane Harvey, photo images from traffic monitoring cameras provide critical information, sometimes as the only reliable source, to identify whether or not a road is flooded. The advent of new image processing and filtering technologies has enabled us to extract extent of inundation from low-resolution photos with reasonable accuracy. Despite the high potential, however, the images from traffic monitoring systems have yet to be investigated to extract more accurate flood information using objective and automatic ways. The main objective of this project is to develop an inundation detection and evaluation framework using images from traffic monitoring cameras and high-resolution flood maps under extreme precipitation conditions. A new Bayesian filtering method will be devised to detect occurrence of flooding and extract inundation extent from low-resolution images taken by the existing traffic monitoring cameras during the extreme events. High-resolution urban flood modeling will produce street-resolving flood maps based on multiple extreme precipitation frequencies. Capability of the filtering algorithm and the flood model will be demonstrated for the past extreme event (e.g. Hurricane Harvey) at a city scale (e.g. the Downtown Houston areas).]]></description>
      <pubDate>Thu, 08 Aug 2019 07:57:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/1644426</guid>
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      <title>The Impact of Increased Adverse Weather Events on Freight Movement</title>
      <link>https://rip.trb.org/View/1644424</link>
      <description><![CDATA[With significant increases in freight volumes, the impacts from severe weather events to port truck traffic may cause an economic loss in Texas and the surrounding region. Although adverse weather events significantly impact transportation infrastructure and networks, a lack of understanding on the scope and magnitude of a weather event’s impact on freight movement persists. This project aims to characterize the port truck movements by identifying operational patterns by associated industry and service types and evaluate system response during adverse weather events. The research will focus on identifying (1) truck activity from the port of Houston, (2) capturing truck flow disruptions due to Hurricane Harvey, and (3) identifying flow changes and recovery process during and immediately after the adverse events. Large-sized global positioning system (GPS) data will be used to represent individual trip characteristics such as travel time, origin-destination (OD), major route choice, and industry type. The developed framework will be applied in Houston as the major destination (or origin) of freight or the intermodal point of the shipment. Identified truck flows will be categorized by their service (trip) type (i.e., intercity, first or last mile trip, or localized service). Major trip origins and destinations will be matched with the associated industry using geographic information system (GIS) programming. Flow disruptions and activity changes will be investigated before and after the Hurricane Harvey to understand the interactions of truck behavior to the flow disruptions due to flooding. Operational strategies before the event and behavior changes during the event such as re-routing, shifting schedules or mode changes will be classified by the truck service (trip).]]></description>
      <pubDate>Thu, 08 Aug 2019 07:52:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/1644424</guid>
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