<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <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>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
    </image>
    <item>
      <title>SPR 781 Optimizing SCDOT’s Permitting, Mitigation, and Compliance Systems to Improve Project Delivery</title>
      <link>https://rip.trb.org/View/2719301</link>
      <description><![CDATA[The overarching goal of Phase III is to optimize South Carolina Department of Transportation's (SCDOT’s) permitting, mitigation, and compliance
systems by leveraging emerging technologies. The objectives are to (1) maintain the existing
applications and enhance them through feedback from SCDOT users and consultants, (2) migrate
the geographic information system (GIS) applications to Experience Builder for improved functionality, (3) update the e-permitting
app to align with upcoming regulatory changes, (4) develop standardized templates for permit
drawings, and (5) explore emerging technologies to check the permit drawings and provide
feedback on errors and required changes, predict wetland impacts for future projects, and autogenerate National Environmental Policy Act (NEPA) applications classified as Categorial Exclusions.]]></description>
      <pubDate>Thu, 25 Jun 2026 08:53:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2719301</guid>
    </item>
    <item>
      <title>Developing an Automated Framework for Aggregating Right-of-Way Data through GIS and Computer Vision Methods</title>
      <link>https://rip.trb.org/View/2714448</link>
      <description><![CDATA[Ohio Revised Code 125.16 requires current and accurate records of tangible personal property and real property be maintained. The Ohio Department of Transportation (ODOT) can obtain records of state-owned right-of-way (ROW) by specific locations and/or project, however there is no simple repository or mechanism to obtain information in a wholistic, statewide manner. ODOT's Office of Real Estate has an online Arcis application, OhROW, that lets people click on a map and get direct access to ROW plans. However, OhROW does not have any data aggregation capabilities, data is not queryable, and not all areas are available in the application. ODOT's Office of Data Governance is working on an initiative to obtain ROW line data from new 3D plan sets. This process is tremendously slow and dependent on the availability of 3D plan sets that include some kind of ROW lines. It is estimated that it could take up to 50 years to cover all of ODOT's road network using this process. Other state DOTS, such as Texas and Nevada, have attempted to address this issue by manually reviewing every plan set of their road network and manually inputting the legal descriptions to make polygons of their ROW. This process is very labor intensive, time consuming, and is expected to take several years to complete. An innovative approach to obtain ROW data, in granular details (such as acreage, type, access parcels, nature of control, etc.), and create maps is needed. OBJECTIVE: Develop an innovative approach to identify ODOT owned property, propose methods for storing datasets, and utilize the data to pilot the approach by generating property maps of a specific county, city, township or State Route. The approach should be repeatable, reliable, and streamlined and findings should include recommendations for statewide implementation of the innovative approach.]]></description>
      <pubDate>Tue, 16 Jun 2026 15:19:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2714448</guid>
    </item>
    <item>
      <title>Infrastructure Data System (IDS)</title>
      <link>https://rip.trb.org/View/2696940</link>
      <description><![CDATA[Transportation data have been growing in volume, velocity, and variety, especially since the introduction of connected vehicle data, crowd-sourced data, and other recent technological advancements. While this is an enormous opportunity for transportation advancement, the challenge lies in translating these data into actionable information for analysts and decision-makers. Therefore, it is crucial to make these datasets easily accessible to industry professionals and decision-makers.
This project would establish the Infrastructure Data System (IDS), a robust data hub and information portal centered around various aspects of infrastructure and NCIT. IDS shall provide a one-stop shop solution for data and ad hoc- analysis needs by leveraging skills in building data systems, web-based tools, visualizations, and geographic information systems. This system could ultimately serve as a hub to host research results under NCIT and as a repository of data and information for researchers, analysts, and policymakers.
The IDS will ensure consistency by having one centralized location and will seamlessly integrate two key NCIT topical pillars: policy and technology. By leveraging advanced technology, the IDS will provide the tools and infrastructure needed to harness the power of data, driving innovation and enabling informed decision-making in the field of transportation. While the intent is to create an active data system, the lifespan of the application will be constrained due to limited funds. However, the results of this project will serve as a proof of concept, i.e. a stepping stone, for establishing a truly effective national-level system for NCIT.
]]></description>
      <pubDate>Sat, 30 May 2026 12:16:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696940</guid>
    </item>
    <item>
      <title>Emergency Response and Access Mapping for Rural Navajo Communities </title>
      <link>https://rip.trb.org/View/2658056</link>
      <description><![CDATA[In the Navajo Nation Area, poorly maintained, unpaved, and seasonally hazardous road conditions in rural areas hinder timely response of emergency services. For example, in Crownpoint, heavy snowfall can make it difficult for ambulance services and firefighting vehicles to reach homes, as they must travel through unpaved or unmaintained roads to reach their destinations. Although current routing tools are able to locate the best route between two points, they do not contain pavement condition data or hazard data that would allow for the accurate determination of safe passage for emergency vehicles. Satellite images are also unable to show potholes, ruts, washouts, etc.; therefore, responders are forced to guess which is the best route based on their experience or try different routes until they find one that works. In many cases, this results in substantial delays, especially during severe weather when traditional navigation systems provide little guidance on actual road accessibility. A new platform is needed that has reliable and accessible data to help direct emergency responders to the safest route to the point of origin. Such a system would not only improve response time but also provide agencies with a standardized way to assess roadway risk during rapidly changing environmental conditions. This project will create a reliable, data driven, artificial intelligence (AI)-assisted Road Accessibility Index (RAI), and a geographic information services (GIS)-based routing dashboard utilizing Vialytics' smartphone-based road assessment capabilities, along with data on transportation, crashes, maintenance, and climate to provide real time accessibility ratings for each ten meter section of road within the Crownpoint area (150 miles total) and direct Emergency Medical Services, Fire and Law Enforcement departments towards the safest routes to travel to emergency locations. 
 ]]></description>
      <pubDate>Wed, 04 Feb 2026 19:20:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2658056</guid>
    </item>
    <item>
      <title>Rockfall Hazard Assessment and Monitoring Program for New Mexico's Transportation Infrastructure</title>
      <link>https://rip.trb.org/View/2607967</link>
      <description><![CDATA[The New Mexico Department of Transportation (NMDOT) Geotechnical Team proposes to develop and implement a geotechnical asset management program that integrates remotely sensed drone data (including photogrammetry, LiDAR, and thermal imagery) with Geographic Information Systems (GIS) to enhance the state’s rockfall monitoring program. Historically, rockfall events have been documented using paper records and Excel spreadsheets. This initiative will modernize data collection and management practices, improving the accessibility, consistency, and utility of rockfall monitoring across New Mexico. Recent advancements in remote sensing and geospatial analytics will equip NMDOT to conduct proactive maintenance, inform planning, and implement long-term risk mitigation strategies for transportation corridors susceptible to rockfall and other slope failure hazards - particularly those exacerbated by changing climate conditions. ]]></description>
      <pubDate>Thu, 09 Oct 2025 13:11:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2607967</guid>
    </item>
    <item>
      <title>Research Project Name: Development of a CAV Testbed-enhanced Smart Campus at Morgan State University - Phase III</title>
      <link>https://rip.trb.org/View/2606401</link>
      <description><![CDATA[This research advances Connected and Automated Vehicle (CAV) infrastructure through Phase III expansion of an established testbed, integrating LiDAR-powered safety applications with signal control systems and conducting comprehensive CAV market penetration analysis in partnership with Maryland Department of Transportation. Building on previous phases, the study coordinates signal phasing and timing across three campus intersections equipped with LiDAR and roadside unit infrastructure, implementing dynamic all-red extensions based on vehicle speed and red-light violation risk detection. The methodology develops pedestrian signal extensions activated by real-time crosswalk occupancy detection and creates Safety Data Sharing Messages compliant with SAE J2735 standards for broadcasting object-level data to vehicles. Portable LiDAR deployments collect trajectory data at additional intersections and work zones for solution validation. The market penetration analysis component catalogues CAV data sources, develops quality assurance frameworks, and compares traditional probe data with connected vehicle information. Collaboration with Maryland Motor Vehicle Administration provides vehicle registration cross-referencing with automation levels, while commercial vendor partnerships supply dynamic usage patterns. The research creates geographic information system (GIS)-based visualizations representing regional CAV penetration and develops interactive dashboards for transportation planning support.]]></description>
      <pubDate>Thu, 02 Oct 2025 14:53:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2606401</guid>
    </item>
    <item>
      <title>Milkweed Presence Detection from High-Resolution Mobile Roadway Photography and
LiDAR</title>
      <link>https://rip.trb.org/View/2601429</link>
      <description><![CDATA[This project builds on previous Idaho Transportation Department (ITD) studies to identify and map milkweed along highway rights-of-way using existing roadway photography and LiDAR data. The effort will produce a geographic information system (GIS)-based inventory that documents route, milepost, area, and density information for each identified milkweed cluster, supporting ITD’s initiatives to prioritize and preserve high-quality Monarch and pollinator habitat. Key objectives include detecting milkweed using LiDAR and photo imagery, mapping occurrences by route and milepost, estimating cluster size and plant density, and analyzing correlations between milkweed area and density.]]></description>
      <pubDate>Wed, 17 Sep 2025 16:30:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2601429</guid>
    </item>
    <item>
      <title>Vulnerability Assessments of Critical Slope Areas Using Advanced Monitoring Techniques</title>
      <link>https://rip.trb.org/View/2577111</link>
      <description><![CDATA[The Minnesota Department of Transportation (MnDOT) recently conducted a multi-phase study on developing a geographic information system (GIS)-based model to determine the risk of slope failure along state highways. The risk assessment identified 1.4% of the studied ;and as Critical Slope Areas (CSAs). The proposed study aims to utilize state-of-the-art sensor technology, having both LIDAR and camera sensors, mounted on uncrewed aerial vehicles (UAVs) to provide high-resolution data to reevaluate the slope vulnerability assessments of the areas identified as CSAs by the current model.]]></description>
      <pubDate>Fri, 18 Jul 2025 10:58:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2577111</guid>
    </item>
    <item>
      <title>Pedestrian volume Estimates for Maine Towns</title>
      <link>https://rip.trb.org/View/2554000</link>
      <description><![CDATA[The state of Maine has produced a model of average annual daily traffic (AADT) for vehicular volumes on roadways, which has been used for countless applications, such as safety network screening, project design decision-making, preliminary intersection control evaluations, application of engineering instructions and design guidance, etc.
An analogous model for pedestrian volumes remains lacking, making it difficult to make informed location decisions about pedestrian infrastructure and safety investments. Models for pedestrian volume estimates have recently been developed for urban districts (Sevtsuk et al. 2021; Sevtsuk et al. 2024) and even large cities (Alhassan & Sevtsuk, 2024). Caltrans and the UC Berkeley Safe Transportation Research and Education Center have co-produced partial pedestrian volume estimates for selected state highway intersections . Such estimates can enable important insights about the distribution non-motorized road users, suggesting, for instance, where pedestrian infrastructure improvements and investments could impact most constituents. Pedestrian volume estimates can also form a critical denominator for various pedestrian hazard data (e.g. crashes, noise, air pollution). For example, state traffic crash location records can illustrate total crashes involving pedestrians at intersections, which may be used to justify Vision Zero intersection improvements. However, normalizing such crash records with pedestrian volume estimates can illustrate what percent of non-motorized road users experience crashes—suggesting potentially different policy-relevant interventions and helping identify not only locations with most crashes, but also locations with highest crash probabilities.  Pedestrian volume estimates could assist project decision-making, help identify locations for RRFB’s and Pedestrian Hybrid Beacons, identify High Priority Active Transportation routes, determine locations for continual pedestrian volume collection, etc.
A pedestrian volume model for cities and towns in Maine would enable better decision-making around non-motorized street user needs and help further the state’s goals to support all street users equally, and to decarbonize the transportation sector in socially just ways.

The research team proposes a collaboration between Maine Department of Transportation (MaineDOT) and the Massachusetts Institute of Technology (MIT) City Form Lab to develop a first pedestrian volume model for the 140 largest cities and towns in Maine, with populations over 2,500 residents. These towns constitute approximately 77 per cent of the state’s population. The model will estimate AADFT—average annual daily foot-traffic for all for all road and street centerlines in the selected towns. The model will be developed using detailed land use, demographic, transportation and point-of-interest data, which function as address-level origins or destinations for pedestrian movement. The research team will use the open-source Madina Python package (recently developed at the MIT City Form Lab) to estimate the spatial distribution of pedestrian trips during  typical weekday and weekend periods between expected land use pairs and calibrate the model estimates on observed pedestrian counts from hundreds of available intersections throughout Maine, where pedestrian flows have been counted using computer vision detection from camera feeds (such as MioVision).  Most existing MaineDOT pedestrian counts are from intersection turning movement counts which are represented in a geographic information system (GIS) interface on the Drakewell platform.  Additional continuous pedestrian volume data will be available at new traffic signals.  .

The longitudinal nature of camera counts will enable us to use average weekday and weekend counts (instead of specific day counts) during different seasons, allowing the model to also estimate segment-level pedestrian flows for different seasons. The model will be similar in structure to the ones developed in New York City (Alhassan & Sevtsuk, 2024) and Melbourne Australia (Sevtsuk et al. 2021), but implemented at a state-wide scale for significantly larger lower-density areas for the first time. 
For one case-study town (TBD), the research team will also develop a detailed pedestrian sidewalk network-- comprising sidewalks, crosswalks, and footpaths using Tile2Net—another open-source Python package for mapping pedestrian infrastructure from aerial imagery tiles, developed at the MIT City Form Lab. Using these data, the research team will estimate a similar pedestrian volume model for these more detailed network segments (instead of road centerlines), providing a higher resolution overview of pedestrian activity, which can substantially differ between opposing sides of the same street segment, or different crossing segments at the same intersection.

If this research is successfully implemented on most of the road segments in Maine where pedestrians are expected, there could be major safety benefits.  This could help guide decision-making for construction of Complete Streets infrastructure, traffic calming elements, pedestrian hybrid beacons, highway design methods, etc.  This could also reduce costs by reducing needs for data collection and reducing “missed opportunities” to construct infrastructure which requires additional programmed work.  Infrastructure constructed where people are walking and where generators are present can improve access for residents and improve economic opportunity for the local businesses.
It is MaineDOT Research and Innovation’s understanding that this project would provide the state of Maine with the most comprehensive “statewide” pedestrian volume model in the country.

The research should result in at least an accessible and sharable GIS dashboard with the pedestrian volume estimates.  It would be the primary research objective to get the results implemented as a layer in the MaineDOT Public Map Viewer.  MaineDOT GIS would be engaged in this project from the beginning to increase the likelihood of successful data and GIS migration.  The research team anticipates being able to accomplish this and they are going through a similar technology transfer process with the city of New York and other entities.
]]></description>
      <pubDate>Wed, 02 Jul 2025 09:52:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/2554000</guid>
    </item>
    <item>
      <title>Customization of a Mobile Field Data Collection Application and Linear Referencing System to Support Geospatial Data Collaboration </title>
      <link>https://rip.trb.org/View/2566964</link>
      <description><![CDATA[Under a previous research project, Project # SPR-2253, “Development of the Digital Design Environment (DDE) ProjectWise™ – Phase 2,” the Connecticut Department of Transportation (CTDOT) initiated and accomplished significant work towards customization of a web-based editing application to facilitate field data collection of geospatial data assets, specifically road network characteristics.  This application was known as the Mobile Asset Verification and Roadway Inventory Collection tool, otherwise known as “MAVRIC”.  Field data collection of roadway geometry and asset information, and more specifically, simultaneous multi-asset editing/collection (otherwise known as “parallel data collection”), is a critical component of timely management of the geospatially accurate road network and associated attribution (e.g., lanes, shoulders, curbs, intersections, intersection approaches, etc.).  This foundational data of the road network is the backbone upon which the CTDOT’s GIS is built, and a requirement under 23 CFR 924.17 for the Model Inventory Roadway Elements (MIRE) and All Roads Network of Linear Data (ARNOLD).  The initial project targeted field data collection utilization and was successful in meeting the project goals.  This next phase of the project looks to build upon the lessons learned during the first phase, incorporate additional customization to meet the needs to expanding CTDOT stakeholders, take advantage of additional technological advances CTDOT has made since the completion of the first project, and utilize the customized solution to help CTDOT personnel to enhance the overall quality and completeness of its critical roadway datasets, while providing metrics on time and resource savings that can be expected through implementation of a similar system.  For the different types of data collection, a fully configurable customized application is needed. ]]></description>
      <pubDate>Wed, 18 Jun 2025 16:28:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2566964</guid>
    </item>
    <item>
      <title>Improved Road Flood Predictability and Disruption Response Through the Synergistic Integration of Geospatial Databases, Process-Based Modeling, and Machine Learning</title>
      <link>https://rip.trb.org/View/2536176</link>
      <description><![CDATA[Major flood events can have devastating impacts on communities, ecosystems, and infrastructure. Heavy rainfall in urban areas often overwhelms existing infrastructure, resulting in localized street or section flooding. Flooded roads hinder access to essential services and pose significant challenges for emergency management. Predicting these floods in near-real-time and with high resolution is difficult due to limited data and the computational cost of detailed models. The research team has already developed and tested a framework (Bhattarai et al., 2024). This project will test the modeling framework around the Jackson, Mississippi, downtown and surroundings. For instance, events like floodwater beneath the railroad bridge on Monument Street near Mill Street in Jackson (reported on Wednesday, January 24, 2024, and similar events). The project will compile information on flooded road and railway networks from local and regional news portals and X (formerly Twitter). Using location keywords (Jackson’, ’Jackson downtown’, ’Jackson MS’) and flood-related terms (’flood’, ’flooding’, ’road flood’, ’urban flood’, ’flash flood’, ’road closure’, ’rainfall’), the research team will identify flooding dates and affected road locations for the recent time and geolocate flooded locations using QGIS, that will serve as training-testing data for the machine learning model. Then the project will develop and test machine learning models (base learner models, such as random forest, support vector machines, and ensemble of these base learners). The research team will use datasets of covariates from other available hydrodynamic models, satellite rainfall estimates, traffic cameras (if available), flood-control infrastructure databases, and basin characteristics to predict flood inundation at street-level resolution. The research team believes these machine learning-based models offer significant improvements in computational efficiency while maintaining accuracy and consistency. In a nutshell, the research team will identify the most susceptible road and rail networks to critical urban facilities.]]></description>
      <pubDate>Thu, 10 Apr 2025 14:38:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2536176</guid>
    </item>
    <item>
      <title>Addressing Active Transportation Gaps in Communities with Limited Transportation Access</title>
      <link>https://rip.trb.org/View/2509024</link>
      <description><![CDATA[Many populations with limited transportation access reside in rural residential clusters (RRCs) just outside major population centers. In the context of this research, an RRC is defined as a small, unincorporated cluster of adjacent homes located along a state highway or major county road outside the limits of any city/town limits, urban growth area, or census-designated place. These communities are under county governance and generally lack municipal services, as well as on-site essential services. They typically depend on nearby population centers or other trip generators, such as grocery stores, schools, or clinics. These individuals often lack access to personal vehicles and must rely on walking, biking, or public transit to reach essential services and destinations. However, the active transportation infrastructure connecting these rural residential clusters to key service centers is frequently inadequate, posing significant challenges and safety concerns for pedestrians and cyclists. This project addresses these gaps in active transportation access for residents in RRCs. The key objectives are to 1) develop a method for identifying RRCs that lack adequate active transportation infrastructure; 2) assess the specific active transportation needs and challenges faced by residents in these communities; and 3) create resources for implementing targeted interventions to improve connectivity and safety for active transportation means.
The research approach will involve a comprehensive literature review, spatial data collection and analysis, integration of community characteristics, land use, and transportation network data, as well as demographic data, and qualitative community assessments. The team will utilize geographic information system (GIS) mapping and comparative analyses to quantify the gaps in active transportation infrastructure between RRCs with limited transportation options and other neighborhoods. Surveys, interviews, and focus groups will provide deeper insights into the lived experiences and perceptions of residents. By addressing these gaps, the project seeks to improve access to essential services and support the overall transportation access and mobility for community residents. The anticipated outcomes include enhanced connectivity and safety for pedestrians and cyclists, increased access to opportunities, reduced transportation costs, and the development of more resilient and efficient transportation systems. The findings and recommendations will assist transportation agencies in facilitating active transportation and improving access in RRCs with limited transportation access.
]]></description>
      <pubDate>Wed, 12 Feb 2025 16:04:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2509024</guid>
    </item>
    <item>
      <title>WiLDash Development and Pilot</title>
      <link>https://rip.trb.org/View/2505745</link>
      <description><![CDATA[The prevalence of free public Wi-Fi services and the high penetration rate of smartphones enable the passive generation of wide-distributed and long-lasting (i.e., days, weeks, to years) Wi-Fi log data, which records when, where, and how the Wi-Fi devices connect or disconnect to Wi-Fi access points (APs), implying human travel activities and mobility patterns. However, raw Wi-Fi log data in different formats are collected from various Wi-Fi equipment manufacturers. Also, data processing and analysis lack a standard pipeline due to different data uses. Wi-Fi agencies or stakeholders may not have sufficient IT resources and data analytic capability to process Wi-Fi data. Moreover, Wi-Fi data users must pay attention to privacy and security concerns. Therefore, the problem or issue needing investigation is to develop a generalized, convenient, and secure Wi- Fi data processing and analysis procedure for public Wi-Fi log data from different locations to satisfy human mobility analysis needs while considering data privacy and security concerns. To solve this problem, WiLDash, a web-GIS dashboard tool for Wi-Fi log data processing and analysis, will be developed and implemented.

This project is a technology transfer effort for North Carolina Department of Transportation's (NCDOT’s) Technical Assistance Request project TAR 2022-06: University Campus Wi-Fi Log Data Processing Methodology Development. The primary research objectives include (1) developing and piloting a WiLDash tool, a web-GIS dashboard for Wi-Fi log data processing and analysis, and (2) disseminating WiLDash at high profile venues for user testing and analyzing feedback from users to improve the tool consistently. The WiLDash tool allows users to upload raw Wi-Fi log data files. It then automatically generates human mobility pattern results as diagrams, tables, and GIS maps on web pages through visualization and analytical procedures, such as descriptive and spatial-temporal analyses. After establishing and publishing WiLDash online, the research team will work with NCDOT staff to monitor web tool performance and manage the stored Wi-Fi data. The team will also set up a series of advertisements and dissemination activities, such as hosting webinars, sharing information on social media, publishing news on NCDOT and the UNC Charlotte website, or presenting at conferences to attract attention from Wi-Fi stakeholders.

The proposed WiLDash tool will help Wi-Fi providers, stakeholders, or agencies (e.g., cities, towns, state to regional entities) with a convenient, low-cost, liability-free (safe) Wi-Fi log data processing and analysis approach to boost Wi-Fi data use for improving human mobility. This tool will benefit NCDOT’s Integrated Mobility Division (IMD) and local city governments by collecting Wi-Fi log data and enabling the analysis of Wi-Fi-based human mobility patterns to facilitate the adoption of advanced mobility (e.g., micromobility, microtransit, and ridesharing), thereby reducing travel time, waiting time, and carbon footprint.]]></description>
      <pubDate>Tue, 04 Feb 2025 08:28:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2505745</guid>
    </item>
    <item>
      <title>Enhancing Transportation Safety with InSAR Land Subsidence Monitoring</title>
      <link>https://rip.trb.org/View/2475283</link>
      <description><![CDATA[Land subsidence is a gradual downward movement and deformation of the Earth's surface. It is driven by geophysical processes such as sediment compaction, tectonic activity, erosion, and human factors like excessive groundwater extraction, mining, and urban development. This study addresses the urgent need to quantify and mitigate the impacts of land subsidence on transportation infrastructure through an integrated approach utilizing Geographic Information Systems (GIS), Interferometric Synthetic Aperture Radar (InSAR), and the Analytic Hierarchy Process (AHP). Focusing on East Baton Rouge Parish, Louisiana, the research examines areas prone to subsidence from 2017 to 2020, specifically targeting critical infrastructure such as Interstate 10, Interstate 12, and major bridges over the Mississippi River. Using a multi-criteria decision analysis framework through AHP, the study systematically prioritizes factors contributing to subsidence, including soil composition, land use/land cover, groundwater extraction rates, and slope stability, leading to the development of detailed susceptibility maps. Integrating machine learning algorithms further enhances the predictive accuracy of risk assessments and infrastructure planning. 
The following tasks will be performed to achieve the objectives of this study: Task 1: preprocess high-resolution Sentinel-1 SAR datasets for InSAR analysis, which generates detailed deformation fields through interferometric processing and time-series analysis. Task 2: apply AHP to assign weights to various subsidence drivers. Task 3: integrate spatial datasets within GIS to create risk maps. Task 4: validate the risk maps using ground truth data from global navigation satellite system observations and historical subsidence records. Task 5: perform temporal analysis of subsidence trends to forecast future deformation patterns, enabling the development of proactive intervention strategies. Task 6: report and share the results. 
The outcomes of this study include practical susceptibility maps and predictive models, offering valuable insights for transportation and urban planning stakeholders. These tools enhance infrastructure resilience by aiding in maintenance prioritization, optimizing land use, and informing policy decisions, ultimately supporting sustainable development by addressing subsidence risks, ensuring the long-term safety and efficiency of transportation networks, and advancing geospatial and remote sensing methodologies for land deformation studies.
]]></description>
      <pubDate>Fri, 20 Dec 2024 19:40:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2475283</guid>
    </item>
    <item>
      <title>A Multidimensional Network-Based Approach to Modeling Urban Growth in Texas Triangle Megaregion </title>
      <link>https://rip.trb.org/View/2470763</link>
      <description><![CDATA[This paper proposes a network analysis framework based on geographic information systems (GIS) to study the development of megaregions in support of urban planning and policy-making. The framework includes a new approach to model geo-shaped polygon data of census places as the Place Geo-Adjacency Network (PGAN). In particular, the integration of descriptive network analysis and degree distribution analysis supports the study of spatial connections, geospatial growth, hub effects, and expansion patterns in megaregions. To demonstrate this framework, a case study was conducted on four US megaregions to study their growth and expansion in the last 40 years since 1980. The degree distribution analysis captures the small-world property and quantifies the level of geospatial connectivity influenced by the hub effects. Policymakers can use the model as a decision support for urban planning and policy design to reduce disparities and improve connectivity in megaregion areas.]]></description>
      <pubDate>Mon, 09 Dec 2024 19:02:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2470763</guid>
    </item>
  </channel>
</rss>