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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>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Use of Large Language Models to Improve Transportation Services</title>
      <link>https://rip.trb.org/View/2447009</link>
      <description><![CDATA[This project aims to leverage Large Language Models (LLMs) to enhance the analysis of public complaints and suggestions related to transportation systems. By processing feedback from multiple agencies, this study seeks to cluster and analyze common concerns, aiding agencies in aligning their services with public demands and safety needs. An open-source LLM model will be developed to safeguard privacy while enabling data-driven improvements in transportation services.]]></description>
      <pubDate>Wed, 30 Oct 2024 14:52:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2447009</guid>
    </item>
    <item>
      <title>A Framework to Reveal Gaps in Transportation Asset Maintenance Through 311 Complaints</title>
      <link>https://rip.trb.org/View/2420220</link>
      <description><![CDATA[Several mechanisms exist for identifying sites for upgrading transportation infrastructure for non- emergency maintenance. One key element is the complaints that citizens make about broken or missing features using the 311-service call system. Researchers argue, however, that the types and rates of complaints vary by location type. Over time some places may end up having worse infrastructure simply because not enough complaints are generated there.

This project will rely on 311 data (2019-2023) available from open data catalogs across case cities in the U.S. We will focus on transportation-sector grievances and generate a new understanding of how regular complaints cluster across census block group typologies. We will assemble 311 data, where available, for case cities roughly the size of Denver, CO. We will append U.S. Census data at the block group level for each 311 complaint, and rely on multilevel statistical models. The key output from this work is to generate a gaps framework by relying on 311 calls and share this knowledge with public-sector engineers and planners.]]></description>
      <pubDate>Sat, 24 Aug 2024 11:07:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2420220</guid>
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    <item>
      <title>Mode Substitutional Patterns of Ridehailing and Micro-Mobility Services</title>
      <link>https://rip.trb.org/View/2137501</link>
      <description><![CDATA[In this study, the research team explores the heterogeneous impacts of ridehailing on the use of other travel modes using survey data (N = 1,438) collected from June to October 2019 (i.e., before the COVID-19 pandemic) across three regions in southern U.S. states: Phoenix, Arizona; Atlanta, Georgia; and Austin, Texas. The research team applies a latent-class cluster analysis to indicators of changes in the use of various travel modes as a result of ridehailing adoption, with covariates of socioeconomics, demographics, a land-use attribute, and individual attitudes. The research team identifies four distinctive latent classes of behavioral changes in response to the use of ridehailing. About half of ridehailing users in the sample (49.7%) are found to behave as Mobility augmenters, who use ridehailing rarely, in addition to other travel modes, and do not change their travel routines much as a result of the adoption of this mobility service. The second largest class includes Exogenous changers (24.5%), whose members report many changes in their use of various travel modes, but which can be largely explained by other reasons. Private car/taxi substituters (15%) frequently hail a ride, and as a result, reduce their use of private vehicles while making more trips by public transit and active modes, as the result of using ridehailing. Interestingly, Transit/active mode substituters (10.8%) often use ridehailing, likely for trips that they previously made by public transit or active modes, and consequently reduce their use of these less-polluting modes while enjoying enhanced mobility. This study reveals substantial heterogeneity in ridehailing impacts, which were masked in previous studies that focused on average impacts, and it suggests that policy responses should be customized by users’ socioeconomics and residential neighborhoods.]]></description>
      <pubDate>Tue, 14 Mar 2023 12:31:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/2137501</guid>
    </item>
    <item>
      <title>Bridge Load Posting Prediction</title>
      <link>https://rip.trb.org/View/1750147</link>
      <description><![CDATA[1600 (~12%) of the 13,000 bridges in Louisiana that facilitate movement of people, goods, and services are load posted, i.e. they are deemed to lack the strength to safely carry all legal loads. With time, bridges will age and deteriorate; at the same time, legal loads might also become heavier. In this context, it is essential to estimate the expected number of load posted bridges in future to allocate necessary resources during long term planning and maintenance scheduling. Therefore, the research goal of this project is to quantify the number of load posted bridges in Louisiana for the next 50 years by combining machine learning techniques, physics based deterioration models, and probabilistic methods. To this end, the National Bridge Inventory (NBI) database along with element level inspection data from Louisiana Department of Transportation and Development (LADOTD) (if available) will be used to gather data on bridges. Next, clustering techniques will be used to identify the key bridge parameters that have the most influence on load posting decisions. The future values of the key parameters will be determined using Markov chain models whose transition probability matrices will be developed using the available datasets and physics-based deterioration models. To estimate the probability of load posting on a bridge given its key parameters, logistic regression models will be developed. This model will be used along with future values of key parameters to estimate the load posting probability of each bridge for the next 50 years. These load posting probability estimates will be used in a Monte Carlo simulation-based methodology to estimate the expected number of load posted bridges, along with confidence interval estimates. During the implementation phase, an interactive tool will be developed, training manuals will be prepared, and training workshops will be conducted to facilitate the adoption of the research products. Additionally, the research outcomes will be disseminated via various platforms and outreach activities will be organized to involve middle and high school students to increase their interests in engineering careers.]]></description>
      <pubDate>Mon, 09 Nov 2020 14:04:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/1750147</guid>
    </item>
    <item>
      <title>Latent Vehicle Type Propensity Segments: Considering the Influence of Household Vehicle Fleet Structure</title>
      <link>https://rip.trb.org/View/1746067</link>
      <description><![CDATA[Understanding vehicle type propensities and choices is of interest to academics and practitioners in a wide array of fields. For example, market researchers may study vehicle type choices to predict consumer purchase behaviors and future market shares (Train and Winston, 2007), while energy researchers study individuals’ vehicle type preferences and corresponding driving habits to calculate energy consumption and emissions (Gao et al., 2019). Transportation scholars traditionally study vehicle type to understand and forecast individual and household travel behaviors (Bhat and Sen, 2006), while in recent times, there has been a proliferation of vehicle type studies intended to model the adoption of emerging transport technologies such as electric and automated vehicles (Higgins et al., 2017; Mocanu, 2018). In this study, the research team proposes to investigate vehicle type from a travel behavior perspective, identifying segments with the aim of understanding how personal and household mobility needs, along with a novel range of individual- and household-level characteristics, attitudes, and behaviors, influence vehicle type propensities. Based on the developed model, the team will further examine the relationships between vehicle type propensities, gender roles, attitudes, and current and future travel behavior choices/interests, focus areas that can have policy implications in transportation. The team will fuse multiple datasets, including those pertaining to two surveys (the National Household Travel Survey) completed by the same Georgia respondents, together with targeted marketing and land use data associated with those respondents.  Apply latent class cluster analysis to the data, to identify naturally occurring vehicle type segments based on the influence of both individual vehicle type choices and household vehicle fleet structures.  ]]></description>
      <pubDate>Tue, 20 Oct 2020 18:06:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/1746067</guid>
    </item>
    <item>
      <title>Exploration of Alternative Spatio-Temporal Methods of Traffic Safety Network Screening</title>
      <link>https://rip.trb.org/View/1716342</link>
      <description><![CDATA[The roadway system represents a major investment and valuable resource that enables mobility and accessibility to users. With rising costs, tight budgets, and limited resources, agencies seek techniques to identify critical mobility and safety concerns, manifested through federal encouragement to increase safety data collection, analysis, and implementation. Network screening methods exist, both historical and recent, with all the methods relying on connection of crashes to the roadway network. Historically, this has been accomplished through assignment of crashes to the network. More recently, methods have been developed that first analyze the spatiotemporal nature of crashes and then connect the resultant clusters to the network. Because the methods either rely on arbitrary network typologies or ignore the network, cohesive screening results may not be provided.

The research team will explore geographic information system (GIS) and spatiotemporal analysis techniques which rely on the crash locations and densities with a coincident network connection. The team will examine spatial proximity constrained by the network to form crash distributions and analyze these for distributional clustering. Network characteristics will be included during distribution and cluster development. The goal is to develop a method that produces an efficient and effective means of crash cluster identification. Crash typology distributions and clusters will also be analyzed.]]></description>
      <pubDate>Tue, 23 Jun 2020 16:30:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/1716342</guid>
    </item>
    <item>
      <title>Defining Safety-Critical Scenarios for Simulation-Based Automated Vehicle Evaluation</title>
      <link>https://rip.trb.org/View/1705305</link>
      <description><![CDATA[Simulation-based evaluation of automated vehicles (AVs) is an essential part of making sure that AVs meet certain safety standards before being allowed to operate widely on public roads. Defining scenarios for simulation-based evaluation is key and challenging. The objective of this study is to make use of publicly available historical human-driven vehicle (HDV) crash databases and identify safety-critical scenarios AV evaluation. This study will identify both common and the rare safety-critical scenarios that can be used for AV safety evaluation.

Clustering will be applied as the primary method to identify groups of crash cases. The common safety-critical scenarios are those larger clusters with more cases and high severity levels. The rare safety-critical scenarios are those smaller clusters (or outliers) with fewer cases and high severity levels. The functions and capabilities of AVs will be considered in the clustering process to ensure the applicability of identified scenarios to AV safety evaluation. The perception-reaction and decision-making mechanism of AVs are different from human drivers. It is expected that AVs have a much wider range of perception, a much faster reaction, and can make their decisions more rationally. Thus, these aspects of the AVs are what need to be tested with the safety-critical scenarios. In developing the scenarios, input variables related to the challenges for these aspects of the AVs will be included in the cluster analysis.]]></description>
      <pubDate>Thu, 07 May 2020 12:49:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/1705305</guid>
    </item>
    <item>
      <title>Micro-Analysis of Collisions in Crash Clusters: Creating Crash Patterns and Conducting a Driver Simulation Study</title>
      <link>https://rip.trb.org/View/1414808</link>
      <description><![CDATA[One of the studies that were conducted in the first phase of funding by the Center was titled “Analyzing Crash Clusters Near Senior Destination Sites Using a GIS Approach”. Using the geographic information system (GIS) shapefiles for the elderly crashes, the study identified high crash clusters for 10 counties in Florida, most of which were listed as priority counties by the Safe Mobility for Life Coalition. This proposed study is the second phase which is intended to use the results of phase 1 in conducting an in-depth crash study in a crash by crash basis (microanalysis) in order to develop elderly crash patterns, create possible countermeasures, and examine the effectiveness of those countermeasures by using a driving simulator approach. The findings of this study will assist state and local safety officials in their strategic planning efforts for developing appropriate intervention and prevention programs for various roadway conditions in order to improve safety and enhance mobility for aging road users.]]></description>
      <pubDate>Fri, 01 Jul 2016 12:51:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/1414808</guid>
    </item>
    <item>
      <title>Telephone Survey to Determine Travel Behavior of Residents of Transit Oriented Development in the Inland Empire</title>
      <link>https://rip.trb.org/View/1236522</link>
      <description><![CDATA[As articulated in Rick Willson's proposal (Phase 1 of the project), policy makers are seeking to coordinate land use and transportation planning to support environmental goals. This is required in the Sustainable Communities Strategy plans mandated by SB 375, and many other regional and local planning efforts. Part of that coordination involves clustering development near transit services (transit-oriented development). This effort requires high quality, local information about the travel behavior of those who live in transit-oriented development (TOD).  In order to gather this information, telephone surveys will be conducted with four target populations. A survey of the first target population (200 residents living within ½ mile of commuter rail stations) will be undertaken as part of Phase 1 of this project. This second phase of the study addresses the three other target populations: 200 residents living within ½ mile of high frequency bus stops, 200 residents living farther than ½ mile but within a 5 mile radius of commuter rail stations, and 200 residents living farther than ½ mile but within a 5 mile radius of high frequency bus stops. Data derived from the survey will be used to understand how travel behavior and auto ownership vary with transit proximity and to understand the factors that affect those relationships. The results will be useful for modelers and policy makers in the Inland Empire and across the state.]]></description>
      <pubDate>Thu, 03 Jan 2013 15:48:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/1236522</guid>
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