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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>Data-driven assessment of rigid pavement vulnerability in Texas coastal regions</title>
      <link>https://rip.trb.org/View/2663108</link>
      <description><![CDATA[This research aims to evaluate the vulnerability of rigid pavements in two major coastal districts of Texas (i.e., Beaumont and Houston) spanning about 900 miles using data-driven approaches. Particularly, the study will (1) identify the key factors contributing to rigid pavement distress under dynamic coastal weather conditions, and (2) develop data-driven strategies to enhance the durability and performance of these pavement networks. Multi-source datasets, such as weather, geotechnical, traffic, coastal proximity, and pavement conditions, will be collected and integrated to support this analysis. Weather data, including temperature and precipitation, will be obtained from national and global databases such as NOAA’s National Centers for Environmental Information (NCEI) and NASA Earthdata/GES DISC. Soil classification and geotechnical attributes will be sourced from the NRCS SSURGO (Soil Survey Geographic Database), while coastal proximity data will be derived from Google Earth. Traffic volumes and loading data will be gathered from TxDOT’s Statewide Traffic Analysis and Reporting System (STARS II). Pavement condition metrics, including distress quantity, distress score, condition score, and ride quality, will be extracted from the Texas Department of Transportation (TxDOT)’s Pavement Management Information System (PMIS) and supplemented with satellite imagery. By integrating these datasets, the project will perform statistical and spatial analyses to establish correlations between weather variables, geotechnical conditions, traffic patterns, and pavement performance indicators.]]></description>
      <pubDate>Thu, 29 Jan 2026 19:58:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663108</guid>
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      <title>Implementing and Evaluating Machine Learning Algorithms for Bikeshare System Demand Prediction
</title>
      <link>https://rip.trb.org/View/2315311</link>
      <description><![CDATA[A bikeshare (public bicycle, or bicycle-sharing) system is a service in which bicycles are made available for shared use to individuals on a short-term basis for a price or free. Bikeshare systems have increased from operating in a few European cities to expanding in the United States at an increasing pace. Many bikeshare systems allow users to borrow a bike from a station and return it at another station belonging to the same system. The goal is to encourage cycling as a mode of transportation as well as recreation. Nevertheless, the flexibility to pick up and return bicycles at any station can lead to inventory imbalances in the system. To enhance the effectiveness of the system, bikeshare operators should implement suitable methods to realign resources, guided by precise forecasts of bicycle demand. This research endeavors to develop models for Houston bikeshare system demand prediction at the station level by leveraging data on station activities. Accurate prediction of bikeshare demand has the potential to transform the way these systems are managed and integrated into urban transportation networks, leading to improved efficiency, customer satisfaction, and sustainability.]]></description>
      <pubDate>Wed, 27 Dec 2023 17:50:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2315311</guid>
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      <title>Investigating Transportation Decarbonization through Transit and Rideshare Electrification: A Scenario Analysis with Large-Scale Models</title>
      <link>https://rip.trb.org/View/2312903</link>
      <description><![CDATA[Enhancing the environmental sustainability of the transportation system hinges on the critical aspect of transportation decarbonization. While the concept of rapid electrification across all existing modes has been previously explored, understanding the profound implications of such a transformative shift is paramount. Typically, proposed policies and strategies have been evaluated using traditional four-step models, often lacking a vehicle powertrain model in the analytical loop, which has limited the ability to comprehensively assess their holistic impact. Addressing this gap, this project capitalizes on the Department of Energy's Systems and Modeling for Accelerated Research in Transportation (SMART) workflow to delve into the realm of transportation decarbonization, specifically through the electrification of transit and rideshare systems. The core objective is to evaluate the potential outcomes of this endeavor. This will be accomplished by harnessing a large-scale agent-based activity-based transportation modeling tool meticulously designed for the Houston Metropolitan Area.]]></description>
      <pubDate>Wed, 20 Dec 2023 16:06:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2312903</guid>
    </item>
    <item>
      <title>Transits First/Last Mile Solution: the EZ Zeus, a zero-emission, Level 4, automated shuttle bus that is FMVSS compliant, ADA-compliant, and Buy America-compliant</title>
      <link>https://rip.trb.org/View/2062456</link>
      <description><![CDATA[The Metropolitan Transit Authority of Harris County (Houston Metro) will receive funding for an automated electric shuttle bus that will serve Texas Southern University, the University of Houston and Houston's Third Ward community. The shuttle will connect to Metro buses and light rail and be studied for potential use in urban, suburban, and rural environments.]]></description>
      <pubDate>Tue, 15 Nov 2022 16:17:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2062456</guid>
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    <item>
      <title>Investigating the Impact of COVID-19 Pandemic Outbreak on Bike Share Usage and Ridership: A Case Study in Houston</title>
      <link>https://rip.trb.org/View/1881802</link>
      <description><![CDATA[Public bicycle share systems have increased from operating in a few European cities to expanding in the United States at an increasing pace. During the COVID-19 pandemic outbreak, people may opt to bike instead of riding transit to avoid exposure to the coronavirus.
The goal of this project is to investigating the impact of COVID-19 pandemic outbreak on bicycling mode share. The research is developed based on the CAMMSE theme of addressing the FAST Act research priority area of “Improving Mobility of People and Goods.” The research is relevant to three CAMMSE research thrusts, “Innovations to improve multi-modal connections, system integration and security” and “Develop data modeling and analytical tools to optimize passenger and freight movements.” Specific project objectives include:
(a)	Identify potential attributes related to bike sharing demand,
(b)	Examine the trip distribution of bike share users throughout the four seasons of the year, and the different hour blocks of a day,
(c)	Model bike share station activity,
(d)	Examine and locate the dock stations in relation to potential demand, and
(e)	Investigate system users and impacts on the bike share.
]]></description>
      <pubDate>Mon, 04 Oct 2021 12:12:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/1881802</guid>
    </item>
    <item>
      <title>Residual life and reliability assessment of underground RC pipelines under uncertainty</title>
      <link>https://rip.trb.org/View/1751155</link>
      <description><![CDATA[Because of scare financial resources and the abundance of urgently needed pipeline maintenance and repair projects, the prioritization of funding to these projects is a major issue that municipalities encounter everywhere, especially in Region 6. One way to optimize the limited resources allocated to operation and management of sanitary sewers is to consider probabilistic performance assessment, which provides a complete characterization of performance of structural elements and systems. The most widely employed probabilistic performance indicator is reliability, a measure of probability of failure relative to a particular limit state (e.g., ultimate strength or serviceability). Reliability methods can be used to identify which pipeline sections within a particular system require the most urgent inspection or repair. In order to apply the proposed approach to the RCPs in the city of Houston, the research team will work intensively with the Center for Structural Engineering Research/Simulation and Pipeline Inspection at UTA to obtain filtered, LIDAR data. From this filtered LIDAR data, a probability distribution representative of wall thickness loss at the time of inspection will be calculated. Next, this derived probability distribution will be integrated within a serviceability limit state that defines failure as the complete loss of concrete cover. Considering this limit state and a prescribed probability of exceedance threshold, a reliability-based prediction of the remaining service life will be determined. Advanced statistical techniques will also be used to convey the confidence of these predictions. Finally, an asset management report that outlines the location of the most vulnerable pipeline sections, will be created. The asset management report will provide decision makers crucial information regarding the current state of their city’s pipeline network. Although the approach developed can be applied to any municipality’s pipeline network, the capabilities of developed methodology will be elucidated through its application to Houston-area sanitary sewers.]]></description>
      <pubDate>Tue, 10 Nov 2020 20:20:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/1751155</guid>
    </item>
    <item>
      <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>I-10 Western Connected Freight Corridor Concept of Operations</title>
      <link>https://rip.trb.org/View/1509353</link>
      <description><![CDATA[The portion of I-10 under consideration extends east from the vicinity of the ports of Los Angles and Long Beach in the Los Angeles region, through the cities of Phoenix and Tucson, across New Mexico, passes through El Paso and San Antonio, and ends in Houston, a total of about 1500 miles in length. The project objectives include the following: (1) All Interstate credentialing and permitting information will be handled "end-to-end" with a single permit per load; (2) Truck parking and reservation systems will be in place at strategic locations, expandable as needed and as practical; (3) Interstate transponder technology (example: PrePass and/or PrePass 360) will be in use to facilitate a single inspection for each truck and load, once the truck is on the Corridor; (4) Interstate Weigh-In-Motion (WIM) devices will be in place and communicating between and among participating states from LA/Long Beach to Houston; and (5) A fleet of five to ten trucks, with drivers and employing selected connected vehicle technologies (to be determined), will successfully navigate from LA/Long Beach to Houston in a driver-assisted Truck Platoon.]]></description>
      <pubDate>Sun, 22 Apr 2018 14:15:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/1509353</guid>
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    <item>
      <title>Life-Cycle Environmental Impact of High-Speed Rail System in the I-45 Corridor</title>
      <link>https://rip.trb.org/View/1505436</link>
      <description><![CDATA[The Houston-Dallas I-45 corridor was ranked as the top priority among 18 traffic corridors in Texas for the development of an Intercity Passenger Transit System, by the Texas A&M Transportation Institute. The city councils of Dallas and Houston have recently taken positive legislative steps towards the construction of a 240-mile high-speed rail (HSR) system connecting the cities via Shinkansen N700 series trains with top speeds of 200 mph. At this juncture, there is an imperative to examine the potential life-cycle environmental impacts of the HSR system and compare/contrast with the environmental impacts associated with existing transportation modes of highway and air travel. HSR systems powered by electricity have significantly lower releases of criteria air pollutants (CAP) and greenhouse gases (GHG) during operation stage, in comparison to conventional transportation by road/air. However, this study will consider the total life cycle of an HSR system including all stages from ‘cradle-to-grave’ such as raw material extraction, infrastructure development, vehicle manufacturing, electricity generation, operation & maintenance, and end-of-life for two components: Vehicle and Infrastructure. This proposal would conduct a holistic life cycle assessment (LCA) study exploring the energy and environmental impact of the HSR system and the role of this transportation mode in alleviating persistent air quality problems in the nonattainment areas of Houston and Dallas. The proposed research would develop estimates for CAP, GHG emissions, and energy consumption per vehicle/passenger-kilometer traveled under scenarios of varying passenger ridership/migration level to the HSR system. The outcomes from this LCA study would provide vital information to regulators, planners and researchers studying environmental impacts of fossil fuel usage in the transportation sector of the US.]]></description>
      <pubDate>Fri, 23 Mar 2018 08:44:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/1505436</guid>
    </item>
    <item>
      <title>I-10 Western Connected Freight Corridor Project</title>
      <link>https://rip.trb.org/View/1408377</link>
      <description><![CDATA[The portion of I-10 under consideration extends east from the vicinity of the ports of Los Angles and Long Beach in the Los Angeles region, through the cities of Phoenix and Tucson, across New Mexico, passes through El Paso and San Antonio, and ends in Houston, a total of about 1500 miles in length. The project objectives include the following:  (1) All Interstate credentialing and permitting information will be handled "end-to-end" with a single permit per load. (2) Truck parking and reservation systems will be in place at strategic locations, expandable as needed and as practical. (3) Interstate transponder technology (example: PrePass and/or PrePass 360) will be in use to facilitate a single inspection for each truck and load, once the truck is on the Corridor. (4) Interstate Weigh-In-Motion (WIM) devices will be in place and communicating between and among participating states from LA/Long Beach to Houston. (5) A fleet of five to ten trucks, with drivers and employing selected connected vehicle technologies (to be determined), will successfully navigate from LA/Long Beach to Houston in a driver-assisted Truck Platoon.]]></description>
      <pubDate>Sun, 22 May 2016 11:35:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/1408377</guid>
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